[
  {
    "id": "revionics",
    "name": "Revionics (Aptos)",
    "sector": "Pricing Intermediary",
    "personalization": 92,
    "data": 88,
    "harm": 90,
    "opacity": 95,
    "flags": [
      "ftc"
    ],
    "summary": "Revionics is a pricing-intermediary subject of the FTC's July 2024 6(b) order. The firm sells AI pricing software to major retailers including Home Depot, Tractor Supply, and Hannaford.",
    "basis": {
      "personalization": "Core product is personalized pricing ML sold to retailers; business model is the score.",
      "data": "Aggregates client retailer data across multiple chains; one of the largest pricing-data clearinghouses.",
      "harm": "FTC 6(b) subject and disclosed client list (Home Depot, Tractor Supply, Hannaford) makes downstream harm traceable.",
      "opacity": "Client relationships and pricing logic are not consumer-facing; no disclosure mechanism to the shopper."
    },
    "evidence": [
      {
        "text": "Named in FTC 6(b) compulsory order to eight intermediary firms on surveillance pricing.",
        "src": "FTC press release, July 23 2024",
        "tier": "A"
      },
      {
        "text": "Identified as a pricing-software vendor working with Home Depot, Tractor Supply, and Hannaford.",
        "src": "CNBC, July 2024",
        "tier": "B"
      },
      {
        "text": "Staff perspective found intermediaries in the 6(b) study worked with 250+ client businesses.",
        "src": "FTC Staff Perspective, January 2025",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "uber",
    "name": "Uber",
    "sector": "Rideshare",
    "personalization": 90,
    "data": 92,
    "harm": 85,
    "opacity": 90,
    "flags": [
      "litig"
    ],
    "summary": "Uber's upfront pricing and driver-side algorithmic wage assignment are the subject of extensive investigative reporting and academic research. Individual-level reservation-price extraction is a documented capability.",
    "basis": {
      "personalization": "Upfront pricing combined with the operational data volume puts Uber at or near perfect price discrimination as a structural capability.",
      "data": "Location, time, device, historical behavior, and cohort signals all feed the fare.",
      "harm": "Academic audits and the CEO's own earnings-call language make the harm pathway documented rather than inferred.",
      "opacity": "Consumer sees one price; no reference price, no breakdown, no mechanism to compare to another user's fare."
    },
    "evidence": [
      {
        "text": "George Washington University study found racial bias in ride-share dynamic pricing algorithms, with fares correlated to neighborhood demographics.",
        "src": "Pandey & Caliskan, 2020",
        "tier": "A"
      },
      {
        "text": "Stanford CARS analysis: rideshare operators approach perfect price discrimination given data volume and absence of price aggregators.",
        "src": "Stanford Center for Automotive Research",
        "tier": "A"
      },
      {
        "text": "UC Hastings scholar Veena Dubal documented algorithmic wage discrimination on the driver side.",
        "src": "Dubal, California Law Review, 2023",
        "tier": "A"
      },
      {
        "text": "Uber CEO Dara Khosrowshahi on Feb 2024 earnings call: targeting of different trips to different drivers based on their preferences or behavioral patterns.",
        "src": "Uber Q4 2023 earnings call",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false,
    "severity": "Stanford CARS analysis: rideshare operators approach perfect price discrimination given data volume and absence of price aggregators. GW academic audit (Pandey & Caliskan, 2020) found racial bias in fare quotes."
  },
  {
    "id": "bloomreach",
    "name": "Bloomreach",
    "sector": "Pricing Intermediary",
    "personalization": 88,
    "data": 92,
    "harm": 84,
    "opacity": 90,
    "flags": [
      "ftc"
    ],
    "summary": "Bloomreach is a commerce personalization and pricing intermediary. FTC 6(b) respondent. Clients include FreshDirect.",
    "basis": {
      "personalization": "Commerce personalization platform; individualized price and product surfacing is the stated product.",
      "data": "Session-level behavioral data combined with retailer first-party data at scale.",
      "harm": "FTC 6(b) subject; FreshDirect client exposure documents the retail chain of effect.",
      "opacity": "Personalization layer is invisible to the end consumer; no price reference point disclosed."
    },
    "evidence": [
      {
        "text": "Subject of FTC 6(b) compulsory order in July 2024.",
        "src": "FTC press release, July 23 2024",
        "tier": "A"
      },
      {
        "text": "Public statement to CNBC: committed to fair competition and will cooperate with the inquiry.",
        "src": "CNBC, July 2024",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "realpage",
    "name": "RealPage",
    "sector": "Housing / Rental",
    "personalization": 84,
    "data": 82,
    "harm": 94,
    "opacity": 90,
    "flags": [
      "ag",
      "litig",
      "doj"
    ],
    "summary": "RealPage's YieldStar rent-pricing software is the subject of a DOJ antitrust suit, a proposed November 2025 settlement, and a widening multistate AG action. Major RealPage clients have separately settled: Greystar (largest US landlord) in August / November 2025, Cortland Management in April 2025, LivCor (Blackstone) consent decree pending. Court-approved monitor required for ten years; bans on pooling non-public competitor data and on requiring acceptance of algorithmic prices.",
    "basis": {
      "personalization": "YieldStar algorithmic rent-pricing is the most enforcement-active surveillance-pricing example in any sector.",
      "data": "Landlord rent, vacancy, and market data pooled across a substantial share of US multifamily.",
      "harm": "DOJ proposed settlement plus parallel state AG actions plus follow-on settlements with Greystar, Cortland, and LivCor establish the deepest single-vendor evidence base in SPX. Federal judge denied the motion to dismiss in 2024 and approved the proposed consent decree in 2025.",
      "opacity": "Landlords do not disclose algorithmic rent-setting to tenants; consumers see only a quoted rent."
    },
    "evidence": [
      {
        "text": "DOJ antitrust complaint filed August 2024 against RealPage alleging algorithmic pricing scheme.",
        "src": "United States v. RealPage, D.N.C., Aug 2024",
        "tier": "A"
      },
      {
        "text": "Multiple state AGs have joined or filed parallel actions.",
        "src": "State AG filings, 2024",
        "tier": "C"
      },
      {
        "text": "Original exposé: ProPublica, Rent Going Up? One Company's Algorithm Could Be Why, October 2022.",
        "src": "ProPublica, Oct 2022",
        "tier": "B"
      },
      {
        "text": "RealPage proposed settlement with DOJ filed November 24, 2025: court-approved monitor for ten years, ban on non-public competitor data, ban on requiring acceptance of algorithmic prices.",
        "src": "U.S. Department of Justice, Nov 2025",
        "tier": "A"
      },
      {
        "text": "Greystar (largest US landlord) settles separately for $7M with 9 state AGs and accepts DOJ consent decree.",
        "src": "California AG and DOJ, Aug and Nov 2025",
        "tier": "A"
      },
      {
        "text": "Cortland Management settlement with DOJ and Colorado / North Carolina AGs, April 2025.",
        "src": "Colorado Attorney General, April 11, 2025",
        "tier": "A"
      },
      {
        "text": "LivCor (Blackstone) proposed consent decree with DOJ, 2025; case against remaining co-defendants ongoing.",
        "src": "U.S. Department of Justice, 2025",
        "tier": "A"
      },
      {
        "text": "DOJ Tunney Act response to public comments on the proposed Final Judgment in United States et al. v. RealPage Inc. et al. (M.D.N.C. 1:24-cv-00710), defending the consent decree's owner-inputted-data limits, geographic-variable scope, and override-recommendations requirements. Final step before court entry.",
        "src": "Federal Register, May 8 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false,
    "severity": "Average 6 percent rent overcharge on RENTmaximizer-priced units, per the economic analysis cited in the Yardi class action complaint. RealPage software priced units across more than a million apartments at peak."
  },
  {
    "id": "yardi",
    "name": "Yardi Systems (Revenue IQ)",
    "sector": "Pricing Intermediary",
    "personalization": 90,
    "data": 84,
    "harm": 84,
    "opacity": 92,
    "flags": [
      "litig"
    ],
    "summary": "Yardi Systems sells RENTmaximizer (now Revenue IQ), an algorithmic rent-pricing platform that is the closest peer to RealPage. A federal antitrust class action filed in 2023 alleges Yardi orchestrated a nationwide scheme to fix multifamily rents by pooling competitively sensitive data across landlord clients. A federal judge denied the motion to dismiss in December 2024 and the case continues, narrowed in April 2026 to defendants with Washington-state ties.",
    "basis": {
      "personalization": "Revenue IQ is a dedicated rent-pricing software product; per-unit pricing recommendations are the explicit product output.",
      "data": "Pools non-public rent, occupancy, and concession data across competing landlord clients into the pricing model.",
      "harm": "Federal court denial of motion to dismiss is a strong evidence signal. A test-run economic analysis cited in the complaint shows an average 6 percent overcharge on RENTmaximizer-priced units in the studied ZIP codes.",
      "opacity": "Tenants do not see the algorithmic pricing layer; the rent quote arrives from the landlord, not from Yardi."
    },
    "evidence": [
      {
        "text": "Federal antitrust class action filed against Yardi Systems and 18 property management firms alleging coordinated rent-fixing through RENTmaximizer / Revenue IQ.",
        "src": "Hagens Berman class action filing, Sept 2023",
        "tier": "A"
      },
      {
        "text": "U.S. District Court (W.D. Wash.) denial of motion to dismiss, December 2024: 'as technology has evolved, so too have methods of price fixing.'",
        "src": "Order in Duffy v. Yardi Systems, Dec 2024",
        "tier": "A"
      },
      {
        "text": "April 2026 ruling narrowed the case by dismissing 10 out-of-state property managers for lack of personal jurisdiction; Yardi remains the lead defendant.",
        "src": "Multifamily Dive, April 2026",
        "tier": "B"
      },
      {
        "text": "FTC and DOJ joint statement of interest urging the court not to dismiss the algorithmic pricing claims.",
        "src": "FTC and DOJ, 2024",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-02",
    "substackUrl": "",
    "thinEvidence": false,
    "severity": "Average 6 percent overcharge documented in the federal class action complaint, scoped across multiple ZIP codes. 18 named property managers in the suit."
  },
  {
    "id": "tinder",
    "name": "Tinder (Match Group)",
    "sector": "Dating Apps",
    "personalization": 88,
    "data": 80,
    "harm": 88,
    "opacity": 90,
    "flags": [
      "litig"
    ],
    "summary": "Tinder has faced two major class-action settlements over age-based personalized pricing, including a $24M California settlement and a $60.5M nationwide class settlement. A multi-country Mozilla / Consumers International study found pricing varied by up to 5x for the same service.",
    "basis": {
      "personalization": "Documented up-to-5x price variation for the same Tinder Plus / Gold subscription; settled litigation establishes age as a pricing input.",
      "data": "Profile, location, swipe behaviour, photo and demographic inference data are all in the platform record. Match Group denies using gender or sexual orientation in pricing.",
      "harm": "Two settled class actions and a multi-country empirical study make this one of the most evidentially direct surveillance-pricing harm cases in the consumer-app sector.",
      "opacity": "Pricing logic is not disclosed in-app and varies between accounts shown the same product simultaneously."
    },
    "evidence": [
      {
        "text": "$23M California class-action settlement over age-based pricing (charging users 28+ roughly 2x the price of younger users).",
        "src": "Bloomberg / California Court of Appeal, 2019",
        "tier": "A"
      },
      {
        "text": "$60.5M nationwide class action settlement on age-discrimination pricing claims for Tinder Plus and Gold.",
        "src": "Top Class Actions, 2024",
        "tier": "A"
      },
      {
        "text": "Mozilla Foundation and Consumers International study found Tinder pricing varied up to 5x in the US, NZ, Netherlands, Korea, India and Brazil for the same product.",
        "src": "Mozilla Foundation / Consumers International, 2022",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "pros",
    "name": "PROS Holdings",
    "sector": "Pricing Intermediary",
    "personalization": 90,
    "data": 80,
    "harm": 82,
    "opacity": 92,
    "flags": [
      "ftc",
      "litig"
    ],
    "summary": "PROS sells AI-driven pricing optimization software primarily to airlines, B2B distributors, and travel. FTC 6(b) respondent.",
    "basis": {
      "personalization": "Explicitly markets willingness-to-pay modeling and dynamic pricing as core product capability.",
      "data": "Heavy B2B and airline data ingestion; more narrow than consumer retail but deeper per account.",
      "harm": "FTC 6(b) subject; airline pricing opacity is well-documented in consumer research.",
      "opacity": "B2B model means consumer never sees the logic; airline pricing is a standard example of opacity."
    },
    "evidence": [
      {
        "text": "Named in FTC 6(b) order on surveillance pricing intermediaries.",
        "src": "FTC press release, July 23 2024",
        "tier": "A"
      },
      {
        "text": "Publicly markets AI-powered dynamic pricing and willingness-to-pay modeling.",
        "src": "PROS investor materials",
        "tier": "B"
      },
      {
        "text": "Named alongside FullStory as a data recipient in the two pending JetBlue surveillance-pricing class actions (Phillips v. JetBlue, April 22 2026, and Squire v. JetBlue, May 1 2026, both E.D.N.Y.). Plaintiffs allege PROS's pricing algorithm ingested behavioral signals captured on JetBlue.com to set per-user fares.",
        "src": "Phillips and Squire v. JetBlue complaints, April-May 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "amazon",
    "name": "Amazon",
    "sector": "E-commerce",
    "personalization": 82,
    "data": 95,
    "harm": 76,
    "opacity": 88,
    "flags": [
      "litig",
      "cong"
    ],
    "summary": "Amazon operates one of the most sophisticated personalized-pricing and ranking infrastructures in retail. Project Nessie disclosure in the FTC monopoly case revealed algorithmic pricing experiments that raised prices when matched by competitors.",
    "basis": {
      "personalization": "Patent portfolio plus Project Nessie disclosure document individualization capability at a sophistication unmatched in retail.",
      "data": "First-party shopping, device (Alexa, Kindle), streaming, and purchase data across the largest US e-commerce footprint.",
      "harm": "Project Nessie disclosure put a dollar figure on the practice; FTC case provides the evidentiary base.",
      "opacity": "Search ranking and price-display logic are not disclosed; Buy Box pricing decisions are not transparent to shoppers."
    },
    "evidence": [
      {
        "text": "FTC v. Amazon (2023) unsealed filings described Project Nessie, an algorithmic pricing tool used to raise prices that the FTC alleged generated over $1 billion in additional revenue.",
        "src": "FTC v. Amazon, case filings 2023–2024",
        "tier": "A"
      },
      {
        "text": "Extensive patent portfolio covering personalized pricing, including US Patent 9,189,811 on behavioral-data price adjustment.",
        "src": "USPTO filings",
        "tier": "C"
      },
      {
        "text": "Product search ranking factors include individual-level behavioral data that can effectively steer consumers toward higher-priced items.",
        "src": "Multiple academic studies on Amazon search ranking",
        "tier": "C"
      },
      {
        "text": "Recipient of the May 11, 2026 House Energy and Commerce Ranking Member Pallone letter to 25 food retailers and pharmacies asking how each company uses personal data to set prices. Responses due May 26, 2026.",
        "src": "Energy and Commerce Democrats press release, May 11 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false,
    "severity": "Project Nessie generated over $1 billion in additional revenue, per the FTC v. Amazon complaint. The dollar figure is the largest documented harm in any direct-consumer surveillance pricing case."
  },
  {
    "id": "lyft",
    "name": "Lyft",
    "sector": "Rideshare",
    "personalization": 86,
    "data": 88,
    "harm": 78,
    "opacity": 88,
    "flags": [
      "litig"
    ],
    "summary": "Lyft's pricing practices are materially similar to Uber's. Shares exposure from academic audits of ride-share fare algorithms.",
    "basis": {
      "personalization": "Shares the rideshare structural pattern with Uber; upfront pricing against heavy behavioral data is the same logic.",
      "data": "Comparable data intake to Uber in the US market; slightly narrower international footprint.",
      "harm": "Included in the same GW academic audit on racial bias in rideshare dynamic pricing.",
      "opacity": "Same consumer-facing opacity as Uber: one price, no reference, no comparison mechanism."
    },
    "evidence": [
      {
        "text": "Included in George Washington University study finding racial bias in rideshare dynamic pricing.",
        "src": "Pandey & Caliskan, 2020",
        "tier": "A"
      },
      {
        "text": "Driver-side experiments documented the same customer ride being offered at different fares to different drivers.",
        "src": "PowerSwitch Action / Gig Workers Rising report",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "ticketmaster",
    "name": "Ticketmaster (Live Nation)",
    "sector": "Ticketing",
    "personalization": 82,
    "data": 76,
    "harm": 88,
    "opacity": 92,
    "flags": [
      "litig"
    ],
    "summary": "Ticketmaster's Platinum dynamic pricing system has generated sustained consumer and artist backlash. DOJ and 30 state AGs filed a May 2024 antitrust suit against Live Nation.",
    "basis": {
      "personalization": "Platinum Pricing is the most publicly visible example of surveillance pricing in any consumer sector.",
      "data": "Behavioral and demand-signal data collected across the entire primary-ticketing market.",
      "harm": "DOJ antitrust action plus years of documented consumer and artist backlash create a deep evidence base.",
      "opacity": "Platinum Pricing is labeled but the pricing logic beneath it is not disclosed to consumers."
    },
    "evidence": [
      {
        "text": "DOJ antitrust complaint filed May 2024 alleged Live Nation maintains monopoly power partly through pricing practices.",
        "src": "United States v. Live Nation, May 2024",
        "tier": "A"
      },
      {
        "text": "Artist and consumer backlash against Platinum Pricing during Bruce Springsteen and Taylor Swift tours (2022–2023).",
        "src": "Multiple reporting; artist public statements",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false,
    "severity": "DOJ and 30 state AG antitrust complaint (May 2024) alleges monopoly power partly through pricing practices. Bruce Springsteen and Taylor Swift tour incidents drew sustained public backlash."
  },
  {
    "id": "draftkings",
    "name": "DraftKings",
    "sector": "Sports Betting",
    "personalization": 84,
    "data": 88,
    "harm": 80,
    "opacity": 86,
    "flags": [
      "litig",
      "ag"
    ],
    "summary": "Massachusetts gaming regulators documented that online sportsbooks restrict customers who win regularly, while extending VIP rewards primarily to those who lose. Multiple class actions in 2025 and 2026 allege DraftKings and peers use behavioural data to target loss-chasing patterns with personalised bonus offers and push notifications.",
    "basis": {
      "personalization": "VIP host system and personalized bonus offer infrastructure are core product features documented in MGC analysis.",
      "data": "Gambling behaviour, late-night logins, loss-chasing patterns, betting velocity all feed proprietary models per public 10-K disclosures.",
      "harm": "Massachusetts Gaming Commission investigation found systematic pattern of restricting winners and rewarding losers; multiple federal-court class actions filed in 2025-2026.",
      "opacity": "Bet-limiting decisions and VIP qualification logic are not disclosed to consumers; bonus targeting is presented as generic loyalty rewards."
    },
    "evidence": [
      {
        "text": "Massachusetts Gaming Commission found online sportsbooks restrict winning customers while extending VIP status and rewards primarily to losing customers.",
        "src": "WBUR / Massachusetts Gaming Commission, October 2025",
        "tier": "A"
      },
      {
        "text": "Federal class action alleging DraftKings, FanDuel and the NFL use addictive technology and personalised bonus targeting against compulsive-gambling patterns.",
        "src": "Insurance Journal, March 2026",
        "tier": "B"
      },
      {
        "text": "Industry tracking confirms first ~20 bets are used to risk-profile new customers for downstream limit-setting.",
        "src": "DarkHorse Odds / industry analysis",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "outlogic",
    "name": "Outlogic (formerly X-Mode)",
    "sector": "Location Data Broker",
    "personalization": 72,
    "data": 80,
    "harm": 90,
    "opacity": 90,
    "flags": [
      "ftc",
      "litig"
    ],
    "summary": "Outlogic is a mass-mobile-location data broker that collects raw GPS pings via SDKs embedded in third-party apps and resells the data for pricing, advertising, and (until the 2024 FTC settlement) intelligence-adjacent uses. The FTC's January 2024 settlement was the first against a data broker over sensitive location data and prohibits Outlogic from selling location data tied to medical facilities, religious organizations, and other sensitive sites.",
    "basis": {
      "personalization": "Location is one of the strongest available personalization signals; ZIP-code-based price discrimination is demonstrated in the public record.",
      "data": "Collected via SDKs embedded in numerous third-party apps; aggregate data sold to retailers, ad-tech firms, and (historically) government contractors.",
      "harm": "FTC settlement is the strongest available signal in the location-broker category. Outlogic is also a named co-defendant in multiple privacy class actions.",
      "opacity": "Consumers usually have no awareness their app's SDK is sending location to Outlogic; the data flow is invisible at the consumer surface."
    },
    "evidence": [
      {
        "text": "FTC settlement prohibiting Outlogic from selling sensitive location data; first such settlement with a data broker.",
        "src": "FTC press release, January 2024",
        "tier": "A"
      },
      {
        "text": "FTC blog detailing the unfair-and-deceptive-practices basis for the action.",
        "src": "FTC business guidance, January 2024",
        "tier": "A"
      },
      {
        "text": "Outlogic's Cyber Security Location data product was publicly listed at $240,000 per year on Datarade.",
        "src": "Datarade marketplace listing",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "task",
    "name": "Task Software",
    "sector": "Pricing Intermediary",
    "personalization": 85,
    "data": 78,
    "harm": 80,
    "opacity": 90,
    "flags": [
      "ftc"
    ],
    "summary": "Task Software is a customer-data and pricing intermediary. FTC 6(b) respondent. Clients include McDonald's and Starbucks.",
    "basis": {
      "personalization": "Customer-data and pricing intermediary serving quick-serve retail; individualization is the value proposition.",
      "data": "Client relationships with McDonald's and Starbucks imply access to loyalty-scale behavioral data.",
      "harm": "FTC 6(b) subject; major QSR client list documents downstream deployment at scale.",
      "opacity": "QSR pricing displayed as menu price; personalized offers routed through app with no reference price."
    },
    "evidence": [
      {
        "text": "Subject of FTC 6(b) order in July 2024.",
        "src": "FTC press release, July 23 2024",
        "tier": "A"
      },
      {
        "text": "Client list reportedly includes McDonald's and Starbucks.",
        "src": "CNBC, July 2024",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "fanduel",
    "name": "FanDuel (Flutter)",
    "sector": "Sports Betting",
    "personalization": 82,
    "data": 86,
    "harm": 78,
    "opacity": 86,
    "flags": [
      "litig",
      "ag"
    ],
    "summary": "FanDuel is a co-defendant in Massachusetts Gaming Commission scrutiny and federal class actions over VIP-program targeting of loss-chasing customers. Behavioural data including late-night logins and repeated session activity feeds the bonus-targeting layer that operators including FanDuel use to keep losing players engaged.",
    "basis": {
      "personalization": "Same VIP program infrastructure and targeted incentive logic as DraftKings; pricing is per-user via bonus and free-bet offers rather than published odds.",
      "data": "Comparable behavioural-data intake to DraftKings across the US legal sportsbook market.",
      "harm": "Massachusetts inquiry plus federal class action provide the same evidentiary basis as DraftKings.",
      "opacity": "Bonus targeting is not labelled as algorithmic or as personalised by behaviour."
    },
    "evidence": [
      {
        "text": "Subject of Massachusetts Gaming Commission analysis covering both DraftKings and FanDuel on VIP-program targeting of losing customers.",
        "src": "WBUR / Massachusetts Gaming Commission, October 2025",
        "tier": "A"
      },
      {
        "text": "Co-defendant in federal class action alleging operators use addictive technology and personalised behavioural targeting.",
        "src": "Insurance Journal, March 2026",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "greystar",
    "name": "Greystar",
    "sector": "Housing / Rental",
    "personalization": 80,
    "data": 76,
    "harm": 88,
    "opacity": 86,
    "flags": [
      "ag",
      "litig"
    ],
    "summary": "Greystar is the largest landlord in the United States with about 950,000 rental units under management. The DOJ filed a proposed consent decree in August 2025 to resolve antitrust claims that Greystar shared competitively sensitive data with other landlords through RealPage software, generating coordinated rent recommendations. A separate $7M multistate settlement led by California AG Rob Bonta closed in November 2025 with nine state AGs participating.",
    "basis": {
      "personalization": "Direct user of RealPage and other algorithmic rent-pricing software at portfolio scale. Coordinated parameter selection alleged in the DOJ complaint.",
      "data": "Shared competitively sensitive non-public rent and occupancy data into a pooled algorithmic engine and discussed pricing strategies directly with competitors at RealPage-hosted user meetings.",
      "harm": "DOJ proposed consent decree plus $7M multistate AG settlement establish the harm pathway. Greystar is the largest US apartment manager so the addressable harm is at the scale of millions of renters.",
      "opacity": "Tenants were not told their rents were generated by an algorithm pooling competitor data; the rent quote appeared as the landlord's own asking price."
    },
    "evidence": [
      {
        "text": "DOJ proposed consent decree filed August 2025 against Greystar to end participation in algorithmic pricing scheme; covers data use, monitor requirement, and ban on RealPage user-meeting attendance.",
        "src": "U.S. Department of Justice, Aug 2025",
        "tier": "A"
      },
      {
        "text": "$7M multistate AG settlement led by California AG Bonta with 9 state AGs participating, November 2025.",
        "src": "California Attorney General press release, Nov 2025",
        "tier": "A"
      },
      {
        "text": "Massachusetts AG Campbell joint statement framing the settlement as a multistate enforcement coalition against algorithmic price-fixing in rentals.",
        "src": "Mass.gov press release, Nov 2025",
        "tier": "A"
      },
      {
        "text": "ProPublica reporting on the Greystar settlement contextualises the deal within the broader RealPage co-defendant cluster (Cortland, LivCor, Camden, Cushman & Wakefield, Willow Bridge).",
        "src": "ProPublica, 2025",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-05-02",
    "substackUrl": "",
    "thinEvidence": false,
    "severity": "Largest US landlord at roughly 950,000 units. $7M multistate AG settlement with 9 state AGs in November 2025; DOJ proposed consent decree pending court approval."
  },
  {
    "id": "mastercard",
    "name": "Mastercard",
    "sector": "Financial Services",
    "personalization": 78,
    "data": 95,
    "harm": 72,
    "opacity": 88,
    "flags": [
      "ftc"
    ],
    "summary": "Mastercard is a data-services provider whose Test & Learn and SessionM units sell consumer-behavior analytics used in retail pricing and personalization. FTC 6(b) respondent.",
    "basis": {
      "personalization": "Not a direct price-setter; scores reflect role as a data-analytics supplier to personalization systems.",
      "data": "Transaction-level data on a substantial share of US consumer spending; Test & Learn and SessionM units productize it.",
      "harm": "FTC 6(b) inclusion is the primary harm signal; downstream effect at retailer clients is inferred.",
      "opacity": "Consumer has no visibility into how card-network data is resold to retailers for pricing."
    },
    "evidence": [
      {
        "text": "Named in FTC 6(b) order on surveillance pricing.",
        "src": "FTC press release, July 23 2024",
        "tier": "A"
      },
      {
        "text": "Public response: will cooperate with the FTC inquiry.",
        "src": "CNBC, July 2024",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "k51",
    "name": "84.51° (Kroger)",
    "sector": "Retail Media / Data Analytics",
    "personalization": 84,
    "data": 92,
    "harm": 64,
    "opacity": 82,
    "flags": [
      "cong"
    ],
    "summary": "84.51° is Kroger's wholly owned analytics subsidiary, monetising loyalty-card and POS data from the Kroger banner family at scale. It is the analytic engine behind Kroger Precision Marketing and a primary source of grocery shopper insights resold to consumer-packaged-goods brands and adtech intermediaries. The Warren and Tlaib congressional letters on Kroger's ESL deployment apply to the data-monetisation infrastructure 84.51° operates.",
    "basis": {
      "personalization": "Builds and licences shopper segments at SKU-by-household precision; same monetisation infrastructure that congressional letters flagged.",
      "data": "Loyalty-card-tied purchase data across one of the largest US grocery footprints, plus app, location, and inferred-trait signals.",
      "harm": "Inherits the Kroger congressional-letter exposure; specific 84.51° enforcement has not been brought.",
      "opacity": "Shoppers are not told what 84.51° has built about them or which brands have purchased access."
    },
    "evidence": [
      {
        "text": "Kroger Precision Marketing introduces programmatic targeting of purchase-based audiences via The Trade Desk DSP.",
        "src": "Path to Purchase Institute, 2024",
        "tier": "B"
      },
      {
        "text": "EPIC analysis of Kroger Precision Marketing as a primary monetisation vector for purchase, demographic, and inferred-health-condition data.",
        "src": "EPIC, 2024",
        "tier": "B"
      },
      {
        "text": "August 2024 Warren / Casey congressional letter to Kroger covers the data-monetisation infrastructure 84.51° operates.",
        "src": "Warren / Casey letter, August 2024",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "doordash",
    "name": "DoorDash",
    "sector": "Delivery / Gig",
    "personalization": 78,
    "data": 86,
    "harm": 70,
    "opacity": 86,
    "flags": [
      "litig"
    ],
    "summary": "DoorDash deploys cohort-level and user-level pricing on delivery fees, service fees, and surge. New York City 2023 investigation found iPhone users charged more than Android users on average.",
    "basis": {
      "personalization": "NYC DCWP study established device-based differential pricing as a documented practice in food delivery.",
      "data": "App behavior, device, payment, and location data at restaurant-level granularity.",
      "harm": "NYC DCWP study is the strongest direct consumer-harm evidence in the delivery sector.",
      "opacity": "Fee structure shown but split across service, delivery, and small-order fees; reference price not shown."
    },
    "evidence": [
      {
        "text": "NYC Department of Consumer and Worker Protection 2023 report documented price disparities by device type.",
        "src": "NYC DCWP study, 2023",
        "tier": "A"
      },
      {
        "text": "Class actions have challenged undisclosed fee and pricing practices.",
        "src": "Multiple filed 2023–2024",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false,
    "severity": "NYC Department of Consumer and Worker Protection 2023 study found iPhone users paid an average of more than Android users for the same delivery, with neighbourhood-level variation that did not track to operating cost."
  },
  {
    "id": "chase",
    "name": "JPMorgan Chase",
    "sector": "Financial Services",
    "personalization": 72,
    "data": 92,
    "harm": 68,
    "opacity": 86,
    "flags": [
      "ftc"
    ],
    "summary": "Chase is named in the FTC 6(b) order for its role supplying consumer transaction data and analytics that feed personalized pricing systems at retail partners.",
    "basis": {
      "personalization": "Not a direct consumer-facing price-setter; scored on role as a transaction-data intermediary.",
      "data": "Consumer transaction data across one of the largest card portfolios in the US.",
      "harm": "FTC 6(b) inclusion is the primary signal; specific downstream pricing effects are not yet documented publicly.",
      "opacity": "Bank-retailer data-sharing relationships are not disclosed to consumers."
    },
    "evidence": [
      {
        "text": "Subject of FTC 6(b) order on surveillance pricing intermediaries.",
        "src": "FTC press release, July 23 2024",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "unitedhealth",
    "name": "UnitedHealth Group / Optum",
    "sector": "Healthcare",
    "personalization": 70,
    "data": 95,
    "harm": 64,
    "opacity": 88,
    "flags": [
      "litig"
    ],
    "summary": "UnitedHealth and its Optum unit are developing AI-driven Medicare risk scoring and claims-processing systems that price coverage and adjudicate claims at the individual member level. Multiple federal class actions allege algorithmic claim-denial systems systematically reject medically necessary care. Optum sits at one of the largest concentrations of US health data.",
    "basis": {
      "personalization": "Optum is publicly developing a next-generation Medicare risk-scoring system that uses AI rather than diagnosis codes; risk score is the input to per-member premium calculations.",
      "data": "Among the largest integrated health-data sets in the US, spanning claims, pharmacy, lab, and care-delivery records.",
      "harm": "Federal class actions on algorithmic care-denial systems plus Optum's expansion of AI into claims processing make harm pathways concrete.",
      "opacity": "Claim adjudication and risk-scoring logic are protected as proprietary; consumers cannot see what model assigned their score or denied their claim."
    },
    "evidence": [
      {
        "text": "Optum is developing an AI-based Medicare risk-scoring system in partnership with the Duke-Margolis Institute, replacing diagnosis-code-based coding.",
        "src": "STAT News, May 2025",
        "tier": "B"
      },
      {
        "text": "Optum Real, an AI claims-processing system, launched in 2025 with UnitedHealthcare as the first adopter.",
        "src": "Healthcare Dive / Bloomberg, October 2025",
        "tier": "B"
      },
      {
        "text": "Multiple federal class actions allege UnitedHealth's nH Predict and similar algorithms systematically deny medically necessary care.",
        "src": "Multiple federal court filings, 2023-2025",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "ubereats",
    "name": "Uber Eats",
    "sector": "Delivery / Gig",
    "personalization": 76,
    "data": 86,
    "harm": 66,
    "opacity": 86,
    "flags": [],
    "summary": "Shares pricing infrastructure with Uber core platform; individualized delivery fees and service-fee variability are consistent capabilities.",
    "basis": {
      "personalization": "Shares Uber pricing infrastructure; individualized fees and surge follow the same pattern.",
      "data": "Cross-platform Uber data advantage is the primary asset.",
      "harm": "Inferred from Uber platform practices; no direct Uber Eats harm study yet.",
      "opacity": "Same fee-stack opacity as DoorDash and other delivery peers."
    },
    "evidence": [
      {
        "text": "Uber investor disclosures reference cross-platform pricing-optimization systems.",
        "src": "Uber 10-K filings",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "delta",
    "name": "Delta Air Lines",
    "sector": "Airlines",
    "personalization": 82,
    "data": 70,
    "harm": 74,
    "opacity": 84,
    "flags": [
      "cong"
    ],
    "summary": "Delta confirmed in 2025 earnings communications that its Fetcherr-powered AI pricing tool is now active on roughly 3 percent of the domestic network and on track to reach 20 percent by year end. Delta continues to deny per-passenger personalisation, framing the tool as analyst-supervised demand modelling. The Gallego, Blumenthal, and Warner letters remain open.",
    "basis": {
      "personalization": "AI tool deployed on 3 percent of the domestic network in 2025 with stated trajectory to 20 percent by year end. The Fetcherr Generative Pricing Engine is publicly marketed as an individualized fare-optimisation product.",
      "data": "SkyMiles program plus booking-channel data is substantial; narrower than tech platforms but deep in the vertical.",
      "harm": "Gallego letter provides a direct congressional harm signal; no enforcement action yet.",
      "opacity": "Airline pricing is the archetypal opaque-pricing sector; individualization layered on top deepens it."
    },
    "evidence": [
      {
        "text": "Senator Ruben Gallego and others raised formal objections in 2025 to Delta's plans to use AI for individualized ticket pricing.",
        "src": "Gallego letter to Delta, 2025",
        "tier": "A"
      },
      {
        "text": "Delta's partnership with Fetcherr explicitly markets AI-based individualized fare optimization.",
        "src": "Delta / Fetcherr public announcements, 2024",
        "tier": "B"
      },
      {
        "text": "Delta CIO post confirms Fetcherr-built AI pricing tool now active on 3 percent of domestic and scaling to 20 percent by year end.",
        "src": "CIO.inc, 2025",
        "tier": "B"
      },
      {
        "text": "Fetcherr raises $42M Series C led by Salesforce Ventures to expand AI pricing tools internationally; customer list now includes Delta, Virgin Atlantic, Azul, Viva Aerobus, WestJet, and Royal Air Maroc.",
        "src": "PhocusWire, 2025",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-05-02",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "liveramp",
    "name": "LiveRamp",
    "sector": "Identity Resolution",
    "personalization": 78,
    "data": 92,
    "harm": 56,
    "opacity": 86,
    "flags": [],
    "summary": "LiveRamp operates RampID, an identity graph spanning approximately 2.6 billion verified global IDs. It is the layer that translates anonymous browser, device, and email signals into a single per-person identifier that downstream pricing and targeting systems can act on. The company partners directly with Acxiom, Experian, and Oracle to enrich its graph.",
    "basis": {
      "personalization": "Not a price-setter; scored on its role as the substrate that makes per-user pricing executable across channels.",
      "data": "RampID claims ~2.6 billion verified IDs spanning online and offline identifiers; one of the largest deterministic identity graphs in the US.",
      "harm": "No direct US enforcement against LiveRamp; harm is downstream at retailer and platform clients that act on the resolved identity.",
      "opacity": "The match logic that links a browser cookie to a household record is not disclosed; consumers cannot see what RampID is associated with their device."
    },
    "evidence": [
      {
        "text": "LiveRamp documentation and partner directory disclose the RampID identity graph and integrations with Acxiom, Experian, and Oracle.",
        "src": "LiveRamp / Acxiom partner directory",
        "tier": "B"
      },
      {
        "text": "Combined Omni and Acxiom Real ID platform encompasses approximately 2.6 billion verified global IDs.",
        "src": "LiveRamp investor materials, 2024-2025",
        "tier": "B"
      },
      {
        "text": "LiveRamp Data Marketplace catalog publicly lists third-party audience segments available for activation.",
        "src": "LiveRamp documentation",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "acxiom",
    "name": "Acxiom (IPG)",
    "sector": "Data Broker",
    "personalization": 72,
    "data": 95,
    "harm": 58,
    "opacity": 86,
    "flags": [],
    "summary": "Acxiom is the canonical US consumer-data broker. It maintains demographic, household-composition, life-event, and inferred-trait records on hundreds of millions of US adults and licenses them to retailers, financial-services firms, and adtech intermediaries. The Real ID identifier feeds LiveRamp's broader identity graph; data is bundled into pricing and audience products at clients including FTC 6(b) respondents.",
    "basis": {
      "personalization": "Provides the demographic and inferred-trait inputs to per-user pricing decisions; not the price-setter itself.",
      "data": "One of the largest consumer-data files in the US, spanning hundreds of millions of records and thousands of attributes per record.",
      "harm": "Acxiom has not been the target of US surveillance-pricing enforcement; consumer-facing harm is downstream at clients.",
      "opacity": "Data attributes are licensed under non-disclosed terms; consumers cannot see what Acxiom holds about them outside of state-mandated rights requests."
    },
    "evidence": [
      {
        "text": "Acxiom maintains thousands of attributes per consumer record and licenses to major retailers, financial firms, and adtech intermediaries.",
        "src": "Acxiom corporate disclosures; Wikipedia summary",
        "tier": "C"
      },
      {
        "text": "2018 sale of marketing-solutions division to IPG for $2.3B; remainder rebranded as LiveRamp.",
        "src": "IPG / Acxiom press release, 2018",
        "tier": "A"
      },
      {
        "text": "FTC 2014 study identifying nine major data brokers including Acxiom; called for legislative reform of the data-broker industry.",
        "src": "FTC, May 2014",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "kroger",
    "name": "Kroger",
    "sector": "Grocery",
    "personalization": 68,
    "data": 88,
    "harm": 72,
    "opacity": 78,
    "flags": [
      "cong",
      "litig"
    ],
    "summary": "Kroger has deployed electronic shelf labels across 500+ stores since 2018 with plans to expand to 2,600. Congressional letters from Senators Warren and Casey and Rep. Tlaib raised concerns about facial-recognition-enabled personalized pricing. Kroger denies using facial recognition for pricing.",
    "basis": {
      "personalization": "Electronic shelf label infrastructure, Cooler Screens deployment, and loyalty-tied offers point to individualization capability whether or not it is currently fully deployed.",
      "data": "Kroger Precision Marketing monetizes shopper data at a scale comparable to ad-tech platforms.",
      "harm": "Multiple congressional letters plus documented infrastructure partnerships make the harm pathway the most concrete in grocery.",
      "opacity": "ESL price changes are not disclosed as personalized; consumer cannot see whether their price matches the shelf."
    },
    "evidence": [
      {
        "text": "August 2024 Warren/Casey letter to CEO Rodney McMullen raised concerns ESLs could enable dynamic pricing based on individual willingness to pay.",
        "src": "Warren/Casey letter, Aug 5 2024",
        "tier": "A"
      },
      {
        "text": "October 2024 Rep. Tlaib letter cited risk of discriminatory pricing via facial recognition.",
        "src": "Tlaib letter, Oct 2024",
        "tier": "A"
      },
      {
        "text": "Partnership with Microsoft on EDGE smart shelves and Cooler Screens.",
        "src": "The Record, Recorded Future News, Oct 2024",
        "tier": "B"
      },
      {
        "text": "Kroger monetizes shopper data via Kroger Precision Marketing, selling insights on purchase history, demographics, and inferred health conditions.",
        "src": "EPIC analysis, 2024",
        "tier": "B"
      },
      {
        "text": "Interim CEO Ron Sargent reaffirmed the company-wide ESL rollout commitment on the September 11, 2025 earnings call.",
        "src": "Grocery Dive, Sept 2025",
        "tier": "B"
      },
      {
        "text": "Recipient of the May 11, 2026 House Energy and Commerce Ranking Member Pallone letter to 25 food retailers and pharmacies asking how each company uses personal data to set prices. Responses due May 26, 2026.",
        "src": "Energy and Commerce Democrats press release, May 11 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false,
    "severity": "Multiple congressional letters to CEO Rodney McMullen (Warren/Casey August 2024, Tlaib October 2024). Kroger Precision Marketing monetises shopper data including inferred health-condition signals."
  },
  {
    "id": "shein",
    "name": "Shein",
    "sector": "E-commerce",
    "personalization": 76,
    "data": 88,
    "harm": 60,
    "opacity": 84,
    "flags": [
      "ag"
    ],
    "summary": "Shein operates one of the most aggressive AI-driven dynamic pricing systems in fast fashion. The platform monitors browsing patterns, click-through rates, and social signals to adjust prices and personalise the catalog per user. The company is under EU Commission investigation for deceptive practices and was fined by the New York Attorney General in 2022 over a delayed disclosure of a 39M-user data breach.",
    "basis": {
      "personalization": "Proprietary algorithm continuously monitors user behaviour and social signals; each user sees a personalised catalog and price set.",
      "data": "Browsing, click, social and demographic signals are aggregated at scale across one of the largest e-commerce footprints among 2026 fast-fashion peers.",
      "harm": "EU Commission investigation and 2022 NY AG settlement establish the regulatory pattern; direct price-discrimination harm is not yet documented in US enforcement actions.",
      "opacity": "Personalised pricing is not disclosed to consumers; discount framing has been flagged in EU investigation as deceptive."
    },
    "evidence": [
      {
        "text": "European Commission opened formal investigation into Shein for deceptive practices including fake discounts and dark-pattern design.",
        "src": "European Commission, 2025",
        "tier": "A"
      },
      {
        "text": "New York Attorney General 2022 settlement and $1.9M penalty over delayed disclosure of a data breach affecting 39M users.",
        "src": "NY AG, October 2022",
        "tier": "A"
      },
      {
        "text": "Public reporting on Shein's AI-driven hyper-personalisation business model.",
        "src": "The Conversation; New Digital Age, 2025",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "pricefx",
    "name": "Pricefx",
    "sector": "Pricing Intermediary",
    "personalization": 86,
    "data": 76,
    "harm": 58,
    "opacity": 88,
    "flags": [],
    "summary": "Pricefx is a SaaS price-optimization vendor competing directly with FTC 6(b) respondents Revionics, Bloomreach, and PROS. The platform serves manufacturing, retail, and distribution clients with AI-driven dynamic pricing and is named alongside the 6(b) respondents in Gartner's B2B price-optimization market guide. Pricefx was not named in the FTC's 2024 order, but the product category and business model are identical.",
    "basis": {
      "personalization": "Core product is AI-driven price personalization sold as a service; same product category as Revionics.",
      "data": "Aggregates client transaction and competitor data; multi-tenant data structure raises the same intermediary-data concerns as the FTC 6(b) respondents.",
      "harm": "Not named in current US enforcement; harm is inferred from category membership and parallel client deployments.",
      "opacity": "Same B2B-vendor opacity pattern as named 6(b) respondents."
    },
    "evidence": [
      {
        "text": "Pricefx is identified alongside Vendavo, PROS, Revionics, and Zilliant in Gartner's B2B price-optimization market guide.",
        "src": "Gartner, 2024-2026",
        "tier": "B"
      },
      {
        "text": "Public product documentation describes AI-driven dynamic pricing and per-customer price optimization.",
        "src": "Pricefx product materials",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "progressive",
    "name": "Progressive",
    "sector": "Insurance",
    "personalization": 74,
    "data": 84,
    "harm": 62,
    "opacity": 80,
    "flags": [],
    "summary": "Progressive's Snapshot telematics program collects driving-behavior data used in individualized premium setting. Questions raised about proxy variables and disparate impact.",
    "basis": {
      "personalization": "Snapshot program is explicit behavior-based individualized pricing; transparent in naming, less transparent in math.",
      "data": "Driving behavior, location, time-of-day telematics at enrolled-driver scale.",
      "harm": "State regulators (notably CA) have raised disparate-impact concerns; no enforcement yet.",
      "opacity": "Snapshot disclosure is above sector norm; actual pricing formula is not disclosed."
    },
    "evidence": [
      {
        "text": "Snapshot program collects acceleration, braking, location, and time-of-day driving data.",
        "src": "Progressive Snapshot disclosures",
        "tier": "C"
      },
      {
        "text": "State insurance regulators (notably CA DOI) have scrutinized algorithmic underwriting for disparate impact.",
        "src": "CA Department of Insurance guidance, 2022",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "allstate",
    "name": "Allstate",
    "sector": "Insurance",
    "personalization": 72,
    "data": 84,
    "harm": 64,
    "opacity": 78,
    "flags": [
      "litig"
    ],
    "summary": "Allstate's Drivewise telematics program and alleged price-optimization practices have drawn regulatory scrutiny and litigation over rate-setting transparency.",
    "basis": {
      "personalization": "Drivewise plus alleged price-optimization practices create the individualization profile.",
      "data": "Telematics plus claims history plus demographic data.",
      "harm": "CFA and state-regulator reports document price-optimization concerns across auto insurers including Allstate.",
      "opacity": "Rate-setting logic is protected as proprietary; disparate-impact concerns have been raised but not adjudicated here."
    },
    "evidence": [
      {
        "text": "Consumer Federation of America and state regulators have investigated price-optimization practices used by major auto insurers including Allstate.",
        "src": "CFA reports, 2015–2023",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "salesforce",
    "name": "Salesforce (Data Cloud / Einstein)",
    "sector": "Customer Data Platform",
    "personalization": 80,
    "data": 88,
    "harm": 52,
    "opacity": 78,
    "flags": [],
    "summary": "Salesforce Data Cloud and Commerce Cloud Einstein are the largest enterprise customer-data and personalization platforms in US retail. Einstein generates real-time pricing recommendations, predictive merchandising, and per-shopper offers across the Commerce Cloud and Marketing Cloud surfaces. Pricing is a percentage of gross merchandise value, aligning Salesforce's revenue with the volume of pricing decisions executed.",
    "basis": {
      "personalization": "Einstein products explicitly market real-time price personalization, predictive offers, and per-customer next-best-action decisioning.",
      "data": "Aggregates first-party retailer data plus Marketing Cloud and Service Cloud signals into unified per-shopper profiles at enterprise scale.",
      "harm": "No US surveillance-pricing enforcement has been brought against Salesforce; harm is downstream at retailer clients.",
      "opacity": "Recommendation and pricing-suggestion logic is not disclosed to consumers and is delivered as the retailer's own price."
    },
    "evidence": [
      {
        "text": "Salesforce Commerce Cloud and Einstein product documentation describes real-time personalisation, predictive merchandising, and price recommendations.",
        "src": "Salesforce product materials, 2025-2026",
        "tier": "B"
      },
      {
        "text": "Commerce Cloud pricing model is a percentage of gross merchandise value (1-3% B2C, 1-2% B2B), aligning Salesforce revenue with executed pricing decisions.",
        "src": "Salesforce pricing pages",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "meta",
    "name": "Meta Platforms",
    "sector": "Ad-Tech / Data Supplier",
    "personalization": 56,
    "data": 95,
    "harm": 62,
    "opacity": 84,
    "flags": [],
    "thinEvidence": true,
    "summary": "Meta does not set consumer-facing product prices but supplies the targeting and audience-inference infrastructure that feeds personalized-pricing systems at advertiser clients.",
    "basis": {
      "personalization": "Not a price-setter but the targeting infrastructure underneath most retail personalization; scored on upstream role.",
      "data": "Among the largest behavioral datasets in the US consumer economy.",
      "harm": "FTC consent orders establish the data-practice pattern; direct pricing harm is downstream at advertiser clients.",
      "opacity": "Ad-targeting logic is not disclosed to consumers; lookalike and custom audiences used in pricing decisions at clients."
    },
    "evidence": [
      {
        "text": "Meta's ad-platform inference on lookalike audiences and custom audiences is widely used in retail pricing and promotion decisioning.",
        "src": "Meta Business documentation",
        "tier": "C"
      },
      {
        "text": "FTC consent orders (2019, 2023) have targeted Meta's privacy and data-use practices.",
        "src": "FTC v. Meta filings",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "google",
    "name": "Google / Alphabet",
    "sector": "Ad-Tech / Data Supplier",
    "personalization": 58,
    "data": 96,
    "harm": 60,
    "opacity": 82,
    "flags": [],
    "thinEvidence": true,
    "summary": "Similar to Meta, Google's role sits upstream of direct consumer pricing but downstream effects on retail pricing systems are material. Public search-ranking and targeting systems shape what prices consumers see.",
    "basis": {
      "personalization": "Same upstream role as Meta; Shopping and Ads infrastructure shape retail pricing strategy.",
      "data": "Search, browsing, location, and cross-device data at platform scale.",
      "harm": "No direct pricing-harm case; downstream effect inferred through retail pricing strategy.",
      "opacity": "Ranking and ad-auction logic not disclosed; influence on prices shown to consumers not traceable."
    },
    "evidence": [
      {
        "text": "Google Shopping ranking and ads infrastructure factor into retail pricing strategy at scale.",
        "src": "Google Ads / Merchant Center documentation",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "affirm",
    "name": "Affirm",
    "sector": "Fintech / BNPL",
    "personalization": 82,
    "data": 78,
    "harm": 56,
    "opacity": 80,
    "flags": [],
    "summary": "Affirm sets per-loan APRs ranging from 0% to 30% based on a soft-credit-check plus internal proprietary risk model. Buy-now-pay-later providers more broadly have been flagged by NBC News and the Consumer Financial Protection Bureau for hidden risk-based pricing that consumers cannot compare across users.",
    "basis": {
      "personalization": "Public APR range of 0% to 30% on identical merchandise is a textbook personalised-pricing range driven by individual data.",
      "data": "Internal-model inputs include credit history, transaction history, merchant context, and behavioural signals.",
      "harm": "CFPB and NBC reporting establish the consumer-protection concern; specific algorithmic-discrimination enforcement has not yet been brought against Affirm directly.",
      "opacity": "The borrower sees a single APR offer; the model that produced it is not disclosed and cannot be appealed."
    },
    "evidence": [
      {
        "text": "Affirm publicly discloses APR range of 0% to 30% on identical merchandise depending on internal model output.",
        "src": "Affirm public disclosures, 2025-2026",
        "tier": "B"
      },
      {
        "text": "Consumer Financial Protection Bureau study and NBC News reporting on hidden risk-based pricing in BNPL.",
        "src": "CFPB; NBC News, 2024-2025",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "adobe",
    "name": "Adobe (Real-Time CDP / Sensei)",
    "sector": "Customer Data Platform",
    "personalization": 78,
    "data": 86,
    "harm": 50,
    "opacity": 76,
    "flags": [],
    "summary": "Adobe Real-Time CDP and Adobe Target are the second-largest enterprise customer-data and personalization stack in US retail and media. Sensei AI runs experiment, segment, and price/offer-optimization workloads against Adobe Analytics signals. As with Salesforce, Adobe sits one layer back from the price-setter and supplies the decisioning engine.",
    "basis": {
      "personalization": "Adobe Target and Sensei explicitly market price testing, A/B price experimentation, and per-segment offer optimisation.",
      "data": "Adobe Analytics is the largest enterprise web/app analytics surface alongside Google Analytics; signals feed Target and Real-Time CDP.",
      "harm": "No direct enforcement; harm is downstream at retailer and media clients.",
      "opacity": "Test-and-learn experiments are typically run without disclosure to the affected user."
    },
    "evidence": [
      {
        "text": "Adobe Real-Time CDP and Sensei product documentation describes per-segment offer optimisation and price-test workflows.",
        "src": "Adobe product materials, 2025-2026",
        "tier": "B"
      },
      {
        "text": "Adobe Target case studies disclose A/B price-testing programmes at enterprise retail and media clients.",
        "src": "Adobe case-study library",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "accenture",
    "name": "Accenture",
    "sector": "Consulting / Systems Integrator",
    "personalization": 70,
    "data": 74,
    "harm": 60,
    "opacity": 84,
    "flags": [
      "ftc"
    ],
    "summary": "Accenture builds and deploys dynamic pricing systems on behalf of retailer and consumer-goods clients. FTC 6(b) respondent.",
    "basis": {
      "personalization": "Systems integrator that builds and deploys pricing ML at client retailers; personalization is a service output.",
      "data": "Data access is client-scoped but cumulative; deploys across many retail clients simultaneously.",
      "harm": "FTC 6(b) subject; specific client-level harm evidence is limited in public record.",
      "opacity": "Consulting engagements are not publicly disclosed; client pricing changes appear as the retailer's own."
    },
    "evidence": [
      {
        "text": "Named in FTC 6(b) compulsory order, July 2024.",
        "src": "FTC press release, July 23 2024",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "foursquare",
    "name": "Foursquare",
    "sector": "Location Data Broker",
    "personalization": 70,
    "data": 86,
    "harm": 52,
    "opacity": 80,
    "flags": [],
    "summary": "Foursquare's Pilgrim SDK is embedded in 500+ third-party mobile apps and feeds a location-intelligence graph used in retail attribution, audience targeting, and pricing-adjacent decisioning. The company has voluntarily adopted industry guidelines restricting use of sensitive location data, but remains one of the largest sources of US mobile location signals.",
    "basis": {
      "personalization": "Provides location and visit-pattern signals to downstream pricing and audience systems.",
      "data": "Pilgrim SDK in 500+ apps yields a substantial share of US mobile-location pings.",
      "harm": "Voluntary restraint on sensitive locations and no direct enforcement to date; harm pathway is real but indirect.",
      "opacity": "Same SDK invisibility as other location brokers; consumer awareness is effectively zero."
    },
    "evidence": [
      {
        "text": "Foursquare Pilgrim SDK installed in 500+ apps with insights on 20M+ devices, per public marketing.",
        "src": "Foursquare / Placer.ai industry reporting",
        "tier": "C"
      },
      {
        "text": "Foursquare voluntary guidelines on restricting sensitive-location data sales.",
        "src": "Foursquare public commitments",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "instacart",
    "name": "Instacart",
    "sector": "Delivery / Gig",
    "personalization": 70,
    "data": 78,
    "harm": 58,
    "opacity": 80,
    "flags": [
      "litig"
    ],
    "summary": "Instacart surfaces item-level price markups over retailer shelf price without consistent disclosure. Class action filed 2022 over undisclosed price markups.",
    "basis": {
      "personalization": "Item-level markups over retailer shelf prices are the primary individualization; Busick class action raised the transparency question.",
      "data": "Shopper behavior plus retailer shelf-price data plus location.",
      "harm": "Class action filings document alleged undisclosed markups.",
      "opacity": "Shown in-app prices not flagged as different from in-store prices; this is the core opacity."
    },
    "evidence": [
      {
        "text": "Class action (Busick v. Instacart, 2022) alleging undisclosed markups on in-app product prices.",
        "src": "Busick v. Instacart, N.D. Cal.",
        "tier": "A"
      },
      {
        "text": "Maryland HB 895, signed by Governor Wes Moore on April 28 2026, bans third-party food-delivery platforms from setting prices based on surveillance personal data and requires an algorithmic-pricing disclosure for other merchants. Effective October 1, 2026; civil penalties up to $10K per violation ($25K for repeats). First U.S. state ban on surveillance pricing in food retail and delivery.",
        "src": "Maryland Governor's Office press release, April 28 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "mckinsey",
    "name": "McKinsey & Company",
    "sector": "Consulting / Systems Integrator",
    "personalization": 68,
    "data": 72,
    "harm": 58,
    "opacity": 86,
    "flags": [
      "ftc"
    ],
    "thinEvidence": true,
    "summary": "McKinsey advises retailers on pricing-optimization strategy and implementation. FTC 6(b) respondent.",
    "basis": {
      "personalization": "Advisory role rather than software vendor; scored on strategic influence over client pricing design.",
      "data": "Access varies by engagement; no standing dataset of its own.",
      "harm": "FTC 6(b) subject; attribution to specific harm requires inference about advised clients.",
      "opacity": "Advisory outputs are confidential by contract; public record of specific pricing recommendations is effectively zero."
    },
    "evidence": [
      {
        "text": "Subject of FTC 6(b) order in July 2024.",
        "src": "FTC press release, July 23 2024",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "comcast",
    "name": "Comcast / Xfinity",
    "sector": "Telecom / Cable",
    "personalization": 70,
    "data": 76,
    "harm": 52,
    "opacity": 80,
    "flags": [],
    "summary": "Comcast operates a tenure-and-tier-based loyalty system (Xfinity Membership: Silver, Gold, Platinum, Diamond) and a documented retention pricing track that varies offers by churn risk and tenure. Personalised retention pricing has been industry-standard at Comcast for over a decade and is a primary tool for managing US broadband and cable customer lifetime value.",
    "basis": {
      "personalization": "Membership tier and retention offers are customer-specific and depend on tenure, services subscribed, and inferred churn risk.",
      "data": "Service usage, viewership (where applicable), and account-level history feed the offer engine.",
      "harm": "Industry-wide retention pricing is not yet a US enforcement priority; harm is inferred from documented price differences between long-tenured and new-tenant customers.",
      "opacity": "Retention pricing is reachable only via the retention department; standard customers receive different prices than callers who threaten to leave."
    },
    "evidence": [
      {
        "text": "Xfinity Membership program tiers (Silver / Gold / Platinum / Diamond) are publicly disclosed as based on tenure plus service mix.",
        "src": "Comcast / Xfinity public disclosures, 2025",
        "tier": "B"
      },
      {
        "text": "Industry reporting on retention-department-only pricing and tenure-tied discounts as a standard ISP playbook.",
        "src": "TheStreet, Xfinity community forums, 2026",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "united",
    "name": "United Airlines",
    "sector": "Airlines",
    "personalization": 72,
    "data": 68,
    "harm": 54,
    "opacity": 80,
    "flags": [],
    "thinEvidence": true,
    "summary": "United operates one of the most data-rich loyalty programs in travel. Standard airline revenue-management systems make wide use of predictive demand modeling.",
    "basis": {
      "personalization": "Standard airline revenue management with growing data sophistication; no announced individualization program.",
      "data": "MileagePlus is large and data-rich; data integration with partners is extensive.",
      "harm": "No direct documented harm beyond sector-standard revenue management; lower score reflects evidence state.",
      "opacity": "Same sector-wide opacity; fare-class logic is not consumer-facing."
    },
    "evidence": [
      {
        "text": "MileagePlus program aggregates travel, payment, and partner-purchase behavior.",
        "src": "United privacy policy",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "target",
    "name": "Target",
    "sector": "Retail",
    "personalization": 64,
    "data": 86,
    "harm": 50,
    "opacity": 72,
    "flags": [
      "cong"
    ],
    "summary": "Target pioneered predictive analytics on shopper data (the famous pregnancy-prediction reporting). Circle loyalty program and app deliver personalized offers and pricing.",
    "basis": {
      "personalization": "Circle app drives personalized offers; the 2012 NYT pregnancy-analytics reporting remains the canonical example of inference at scale.",
      "data": "Purchase, location, app-usage, and inferred-trait data combined in one ecosystem.",
      "harm": "Historic Duhigg reporting establishes the template; current practice is less publicly documented.",
      "opacity": "Circle offers are framed as deals; consumer does not see the inferred-trait layer underneath."
    },
    "evidence": [
      {
        "text": "New York Times 2012 reporting on Target's predictive pregnancy analytics established the template for consumer-data inference at scale.",
        "src": "Duhigg, NYT Magazine, Feb 2012",
        "tier": "B"
      },
      {
        "text": "Target Circle loyalty program collects purchase, location, and app-usage data used in personalized promotions.",
        "src": "Target privacy policy",
        "tier": "C"
      },
      {
        "text": "Recipient of the May 11, 2026 House Energy and Commerce Ranking Member Pallone letter to 25 food retailers and pharmacies asking how each company uses personal data to set prices. Responses due May 26, 2026.",
        "src": "Energy and Commerce Democrats press release, May 11 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "walmart",
    "name": "Walmart",
    "sector": "Grocery",
    "personalization": 64,
    "data": 82,
    "harm": 54,
    "opacity": 70,
    "flags": [
      "cong"
    ],
    "summary": "Walmart announced in March 2026 that digital shelf labels will be deployed to every US store by year end, scaling from the existing 2,300-store base. The company has also secured two ESL patents, including one describing an AI-driven system that analyses purchasing patterns to suggest pricing adjustments. Walmart continues to publicly disavow personalised dynamic pricing.",
    "basis": {
      "personalization": "100 percent ESL rollout by end of 2026 plus two recently disclosed ESL patents (one AI-driven for pricing adjustments) raise the structural capability significantly even though the company denies personalised deployment.",
      "data": "Walmart Connect retail media network and loyalty/payment data are among the largest retail first-party datasets in the US.",
      "harm": "No documented direct consumer harm to date. The score lifts because the deployed infrastructure reaches every US Walmart store and federal grocery-pricing legislation now explicitly targets the technology Walmart is rolling out at scale.",
      "opacity": "Disclosure is stronger than most peers due to public statements, but mechanism-level transparency is still limited."
    },
    "evidence": [
      {
        "text": "Walmart SVP Greg Cathey stated at June 2024 shareholder meeting that ESLs would not be used for hour-to-hour price swings.",
        "src": "Reuters, June 2024",
        "tier": "B"
      },
      {
        "text": "ESL deployment creates the infrastructure for surveillance pricing even if not currently used for it.",
        "src": "Multiple analyses, 2024–2025",
        "tier": "C"
      },
      {
        "text": "Walmart commits to digital shelf labels in every US store by end of 2026, scaling from 2,300 stores.",
        "src": "CNBC, March 21, 2026",
        "tier": "B"
      },
      {
        "text": "Walmart corporate post detailing the operations savings of the ESL rollout. Reaffirms no plans to use ESLs for dynamic personalised pricing.",
        "src": "Walmart corporate news, March 2, 2026",
        "tier": "C"
      },
      {
        "text": "Walmart has secured two patents on ESL technology, one of which describes AI-driven pricing-adjustment recommendations from purchasing patterns.",
        "src": "Bankrate / Inc reporting on Walmart patent filings, 2026",
        "tier": "C"
      },
      {
        "text": "Recipient of the May 11, 2026 House Energy and Commerce Ranking Member Pallone letter to 25 food retailers and pharmacies asking how each company uses personal data to set prices. Responses due May 26, 2026.",
        "src": "Energy and Commerce Democrats press release, May 11 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "statefarm",
    "name": "State Farm",
    "sector": "Insurance",
    "personalization": 68,
    "data": 78,
    "harm": 52,
    "opacity": 72,
    "flags": [],
    "summary": "State Farm's Drive Safe & Save program represents the broadest US deployment of behavior-based insurance pricing.",
    "basis": {
      "personalization": "Drive Safe & Save is the broadest US telematics deployment; individualization by behavior is the product.",
      "data": "Mobile-phone telematics across a large share of the US auto-insurance market.",
      "harm": "No direct documented harm; sector-level scrutiny applies.",
      "opacity": "Telematics disclosure is above the norm for insurance; full formula remains proprietary."
    },
    "evidence": [
      {
        "text": "Drive Safe & Save uses mobile-phone telematics at scale across the policy base.",
        "src": "State Farm disclosures",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "grubhub",
    "name": "Grubhub",
    "sector": "Delivery / Gig",
    "personalization": 66,
    "data": 74,
    "harm": 52,
    "opacity": 78,
    "flags": [
      "litig"
    ],
    "summary": "December 2024 FTC settlement of $25M over deceptive pricing and delivery-fee disclosure practices.",
    "basis": {
      "personalization": "FTC settlement addressed deceptive pricing and fee disclosure; personalization layer is less specific.",
      "data": "Standard delivery-platform data intake.",
      "harm": "December 2024 FTC settlement is the primary harm evidence.",
      "opacity": "Fee disclosure remedies imposed by FTC settlement address part of the historic opacity."
    },
    "evidence": [
      {
        "text": "FTC and Illinois AG $25M settlement with Grubhub, December 2024, covering deceptive pricing practices.",
        "src": "FTC press release, Dec 2024",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false,
    "severity": "$25 million FTC and Illinois AG settlement in December 2024 covering deceptive pricing and undisclosed delivery-fee practices."
  },
  {
    "id": "stubhub",
    "name": "StubHub",
    "sector": "Ticketing",
    "personalization": 68,
    "data": 62,
    "harm": 56,
    "opacity": 82,
    "flags": [],
    "summary": "StubHub's secondary-market pricing uses real-time demand and user-behavior signals. Recent FTC action on undisclosed fees increased sector regulatory attention.",
    "basis": {
      "personalization": "Secondary market means the pricing signal is constant; personalization is stronger on the buyer side.",
      "data": "Session, demand, and historical search data across live events.",
      "harm": "FTC Junk Fees Rule covers the sector; specific individualization harm is limited in public record.",
      "opacity": "Fee breakdowns historically opaque; now subject to FTC rule."
    },
    "evidence": [
      {
        "text": "FTC Junk Fees Rule finalized December 2024 covering live-event ticket sellers.",
        "src": "FTC Final Rule on Junk Fees",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "homedepot",
    "name": "Home Depot",
    "sector": "Retail",
    "personalization": 60,
    "data": 72,
    "harm": 54,
    "opacity": 74,
    "flags": [],
    "summary": "Named Revionics client (FTC 6(b) respondent), giving Home Depot direct exposure to surveillance-pricing intermediary infrastructure.",
    "basis": {
      "personalization": "Revionics client relationship is the primary individualization vector; in-store ESL deployment varies.",
      "data": "First-party loyalty plus B2B Pro Xtra data.",
      "harm": "Indirect FTC 6(b) exposure via Revionics; no direct harm documented.",
      "opacity": "Standard big-box pricing opacity; no consumer-facing disclosure of ML-driven pricing."
    },
    "evidence": [
      {
        "text": "Identified as Revionics client in CNBC reporting on FTC 6(b) order.",
        "src": "CNBC, July 2024",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "aa",
    "name": "American Airlines",
    "sector": "Airlines",
    "personalization": 68,
    "data": 66,
    "harm": 48,
    "opacity": 78,
    "flags": [],
    "thinEvidence": true,
    "summary": "American's AAdvantage program, the largest airline loyalty program globally, concentrates significant consumer data used in revenue management.",
    "basis": {
      "personalization": "Largest loyalty program globally creates capability; no announced individualization program.",
      "data": "AAdvantage scale is the primary data asset.",
      "harm": "No direct documented harm beyond sector-standard revenue management.",
      "opacity": "Sector-standard opacity in fare-class assignment."
    },
    "evidence": [
      {
        "text": "Revenue-management systems use predictive demand modeling tied to fare-class allocation.",
        "src": "Industry standard practice, public 10-K disclosures",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "freshdirect",
    "name": "FreshDirect",
    "sector": "Grocery",
    "personalization": 62,
    "data": 68,
    "harm": 52,
    "opacity": 76,
    "flags": [],
    "summary": "Disclosed Bloomreach client (FTC 6(b) respondent). New York grocery operator with direct state legislative exposure (S8616).",
    "basis": {
      "personalization": "Bloomreach client; e-commerce personalization is the explicit product.",
      "data": "Delivery-window, household, and basket data in a New York geographic concentration.",
      "harm": "Indirect FTC 6(b) exposure via Bloomreach; NY S8616/A9396 direct legislative exposure.",
      "opacity": "Online grocery pricing is opaque by default relative to in-store shelf price."
    },
    "evidence": [
      {
        "text": "Identified as Bloomreach client in FTC 6(b) reporting.",
        "src": "CNBC, July 2024",
        "tier": "B"
      },
      {
        "text": "New York S8616/A9396 targets grocery-pharmacy personalized algorithmic pricing.",
        "src": "NY State Senate, 2025",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "marriott",
    "name": "Marriott",
    "sector": "Hotels",
    "personalization": 64,
    "data": 72,
    "harm": 46,
    "opacity": 76,
    "flags": [
      "ag"
    ],
    "summary": "Marriott uses dynamic pricing across Bonvoy properties. Named sector in California AG January 2026 surveillance pricing inquiry.",
    "basis": {
      "personalization": "Bonvoy-linked dynamic pricing is standard; individualization layer is the question.",
      "data": "Stay, spend, and partner-brand data across the largest hotel portfolio.",
      "harm": "CA AG sector inclusion is the main signal; no direct harm documented yet.",
      "opacity": "Hotel rates are displayed per room per night; personalization differential to a reference price is not disclosed."
    },
    "evidence": [
      {
        "text": "California AG inquiry sweep (January 2026) targets grocers, hotels, and retailers.",
        "src": "CA AG press release, Jan 27 2026",
        "tier": "A"
      },
      {
        "text": "Bonvoy loyalty program aggregates stay, spend, and partner-brand data.",
        "src": "Marriott privacy policy",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "goodrx",
    "name": "GoodRx",
    "sector": "Healthcare",
    "personalization": 52,
    "data": 70,
    "harm": 60,
    "opacity": 72,
    "flags": [
      "litig"
    ],
    "summary": "GoodRx settled $1.5M FTC enforcement (2023) over sharing health data with advertising platforms. Price-comparison outputs are partially personalized to user signals.",
    "basis": {
      "personalization": "Price-comparison output is partially responsive to user signals; individualization is in the offer, not the list price.",
      "data": "Health-adjacent search and usage data; FTC enforcement established the data-sharing concern.",
      "harm": "2023 FTC Health Breach Notification Rule enforcement is the strongest signal in the consumer-health pricing space.",
      "opacity": "Price-comparison framing masks that shown prices are personalized to user signals."
    },
    "evidence": [
      {
        "text": "FTC first-ever enforcement action under the Health Breach Notification Rule against GoodRx in February 2023.",
        "src": "FTC v. GoodRx, Feb 2023",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false,
    "severity": "$1.5M FTC penalty under the Health Breach Notification Rule, February 2023 — the first-ever enforcement under the rule."
  },
  {
    "id": "walmartcom",
    "name": "Walmart.com",
    "sector": "E-commerce",
    "personalization": 62,
    "data": 82,
    "harm": 44,
    "opacity": 68,
    "flags": [],
    "thinEvidence": true,
    "summary": "Walmart's e-commerce unit uses personalized search ranking and promotions driven by first-party loyalty and payment data.",
    "basis": {
      "personalization": "Personalized ranking and promotions are standard; whether price itself is individualized is less clear.",
      "data": "Inherits Walmart first-party data at the enterprise level.",
      "harm": "Low documented harm; scoring reflects capability and sector exposure rather than proven practice.",
      "opacity": "Same opacity as any major e-commerce platform; ranking and promotion logic not disclosed."
    },
    "evidence": [
      {
        "text": "Walmart Connect retail media network leverages shopper data across online and in-store channels.",
        "src": "Walmart Connect disclosures",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "hilton",
    "name": "Hilton",
    "sector": "Hotels",
    "personalization": 62,
    "data": 70,
    "harm": 44,
    "opacity": 74,
    "flags": [
      "ag"
    ],
    "summary": "Hilton Honors program data supports personalized pricing and offer strategies. Included in California AG sweep by sector.",
    "basis": {
      "personalization": "Honors-linked personalized offers; dynamic rate-setting standard to the sector.",
      "data": "Honors data is substantial but smaller than Marriott.",
      "harm": "CA AG sector inclusion is the main signal.",
      "opacity": "Same hotel-sector opacity pattern."
    },
    "evidence": [
      {
        "text": "Hotel sector targeted in California AG January 2026 surveillance pricing inquiry.",
        "src": "CA AG press release, Jan 27 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "albertsons",
    "name": "Albertsons",
    "sector": "Grocery",
    "personalization": 52,
    "data": 78,
    "harm": 44,
    "opacity": 72,
    "flags": [
      "ag",
      "cong"
    ],
    "summary": "Albertsons operates a major loyalty program (just for U) collecting purchase-level household data. Growing ESL pilot. Failed Kroger merger highlighted its data assets.",
    "basis": {
      "personalization": "just for U loyalty infrastructure plus growing ESL pilot; capability is present, deployment is partial.",
      "data": "Household-level purchase history plus app and location data across a major grocery footprint.",
      "harm": "CA AG inquiry sector inclusion is the main signal; no direct documented differential pricing yet.",
      "opacity": "Loyalty-linked price differences are not disclosed as personalized; consumer cannot compare to a reference price."
    },
    "evidence": [
      {
        "text": "just for U loyalty program aggregates household-level purchase history.",
        "src": "Albertsons privacy policy",
        "tier": "C"
      },
      {
        "text": "Named in California AG January 2026 surveillance pricing inquiry sweep targeting grocers.",
        "src": "CA AG announcement, Jan 27 2026",
        "tier": "A"
      },
      {
        "text": "Recipient of the May 11, 2026 House Energy and Commerce Ranking Member Pallone letter to 25 food retailers and pharmacies asking how each company uses personal data to set prices. Responses due May 26, 2026.",
        "src": "Energy and Commerce Democrats press release, May 11 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "cvs",
    "name": "CVS Health",
    "sector": "Healthcare",
    "personalization": 48,
    "data": 78,
    "harm": 44,
    "opacity": 70,
    "flags": [
      "cong"
    ],
    "summary": "ExtraCare loyalty program is one of the largest in retail pharmacy, enabling personalized coupons and pricing. Pharmacy pricing exposed to state surveillance-pricing legislation on grocery/pharmacy.",
    "basis": {
      "personalization": "ExtraCare-linked coupons and pricing differentials; pharmacy pricing is the sensitive variable.",
      "data": "One of the largest retail-pharmacy loyalty programs; purchase plus prescription data.",
      "harm": "NY legislative exposure to grocery/pharmacy personalized algorithmic pricing; no direct harm documented.",
      "opacity": "Personalized coupons layered on list price; consumer cannot compare."
    },
    "evidence": [
      {
        "text": "New York S8616/A9396 would prohibit grocery stores and pharmacies from using personalized algorithmic pricing.",
        "src": "NY State Senate, 2025",
        "tier": "A"
      },
      {
        "text": "Recipient of the May 11, 2026 House Energy and Commerce Ranking Member Pallone letter to 25 food retailers and pharmacies asking how each company uses personal data to set prices. Responses due May 26, 2026.",
        "src": "Energy and Commerce Democrats press release, May 11 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "mcdonalds",
    "name": "McDonald's",
    "sector": "Quick-Serve Food",
    "personalization": 56,
    "data": 70,
    "harm": 42,
    "opacity": 72,
    "flags": [],
    "summary": "McDonald's client relationship with FTC 6(b) respondent Task Software, combined with the MyMcDonald's Rewards data infrastructure, creates material personalized-pricing capability.",
    "basis": {
      "personalization": "MyMcDonald's Rewards app routes offers individually; whether menu price varies by consumer is less clear.",
      "data": "Task Software client relationship plus app data is substantial.",
      "harm": "FTC 6(b) indirect exposure via Task; no direct harm documented.",
      "opacity": "App-based offers presented as deals; reference price not shown to consumer."
    },
    "evidence": [
      {
        "text": "Identified as Task Software client in CNBC reporting on FTC 6(b) order.",
        "src": "CNBC, July 2024",
        "tier": "B"
      },
      {
        "text": "MyMcDonald's Rewards app collects order history, location, and device data.",
        "src": "McDonald's privacy policy",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "tractorsupply",
    "name": "Tractor Supply",
    "sector": "Retail",
    "personalization": 58,
    "data": 62,
    "harm": 46,
    "opacity": 72,
    "flags": [],
    "thinEvidence": true,
    "summary": "Disclosed Revionics client (FTC 6(b) respondent).",
    "basis": {
      "personalization": "Revionics client; individualization is the intermediary's product running inside Tractor Supply.",
      "data": "Neighbors Club loyalty plus in-store purchase data.",
      "harm": "Indirect FTC 6(b) exposure via Revionics.",
      "opacity": "Standard retail pricing opacity."
    },
    "evidence": [
      {
        "text": "Named as Revionics client in CNBC reporting on FTC 6(b) order.",
        "src": "CNBC, July 2024",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "starbucks",
    "name": "Starbucks",
    "sector": "Quick-Serve Food",
    "personalization": 54,
    "data": 74,
    "harm": 40,
    "opacity": 70,
    "flags": [],
    "summary": "Starbucks Rewards app and Deep Brew AI platform drive personalized offers and pricing. Client of FTC 6(b) respondent Task Software.",
    "basis": {
      "personalization": "Deep Brew AI platform explicitly markets individualized customer experience; price effects of this are less clear.",
      "data": "Rewards program is one of the most data-rich in QSR.",
      "harm": "Task Software client exposure; no direct documented harm.",
      "opacity": "App-based offers and challenges layered on standard menu price; personalization is not labeled as such."
    },
    "evidence": [
      {
        "text": "Task Software client disclosure in FTC-related reporting.",
        "src": "CNBC, July 2024",
        "tier": "B"
      },
      {
        "text": "Deep Brew AI platform publicly marketed as driving individualized customer experiences.",
        "src": "Starbucks investor materials",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "wendys",
    "name": "Wendy's",
    "sector": "Quick-Serve Food",
    "personalization": 50,
    "data": 48,
    "harm": 62,
    "opacity": 72,
    "flags": [
      "cong"
    ],
    "summary": "Wendy's February 2024 announcement of digital-menu dynamic pricing drew immediate public backlash and political pressure. Company walked back the rollout.",
    "basis": {
      "personalization": "Announced capability was rolled back; scoring reflects announced intent plus ESL infrastructure, not current deployment.",
      "data": "Loyalty app data is modest compared to grocery or platform peers.",
      "harm": "Congressional letters were issued before rollout; walk-back limits confirmed consumer harm.",
      "opacity": "Digital menu boards create infrastructure for opaque per-customer price variation even if not in use."
    },
    "evidence": [
      {
        "text": "February 2024 earnings call announcement on dynamic pricing prompted Senators Warren and Fetterman to raise formal concerns.",
        "src": "Warren/Fetterman letter to Wendy's, Feb 2024",
        "tier": "A"
      },
      {
        "text": "Wendy's subsequently clarified plans would not raise prices during high-demand periods.",
        "src": "Wendy's public statement, Feb 2024",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "bestbuy",
    "name": "Best Buy",
    "sector": "Retail",
    "personalization": 54,
    "data": 70,
    "harm": 40,
    "opacity": 68,
    "flags": [],
    "thinEvidence": true,
    "summary": "My Best Buy program combined with geographic and competitor-responsive pricing. No FTC 6(b) inclusion but consistent dynamic-pricing deployment.",
    "basis": {
      "personalization": "My Best Buy program plus competitor-responsive pricing; no announced surveillance-pricing ML.",
      "data": "Loyalty data is modest relative to grocery or platform peers.",
      "harm": "No direct harm documented; scoring reflects capability more than practice.",
      "opacity": "Pricing logic not disclosed; competitor-match framing is standard electronics-retail practice."
    },
    "evidence": [
      {
        "text": "Public pricing-tool disclosures in investor materials reference competitive repricing systems.",
        "src": "Best Buy 10-K",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "ebay",
    "name": "eBay",
    "sector": "E-commerce",
    "personalization": 58,
    "data": 72,
    "harm": 38,
    "opacity": 62,
    "flags": [],
    "thinEvidence": true,
    "summary": "eBay uses dynamic pricing tools for sellers and deploys personalization in search ranking and promoted listings. Less evidence of direct surveillance pricing on the consumer side.",
    "basis": {
      "personalization": "Marketplace dynamics limit direct individualization; seller-side pricing tools are the main vector.",
      "data": "Session and search data is rich but less integrated across purchase channels than first-party retailers.",
      "harm": "Limited public evidence of direct consumer-side differential pricing beyond standard personalization.",
      "opacity": "Seller tools are disclosed; buyer-side personalization in search ranking is not."
    },
    "evidence": [
      {
        "text": "eBay provides sellers with AI-powered pricing recommendation tools.",
        "src": "eBay seller documentation",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "hannaford",
    "name": "Hannaford",
    "sector": "Grocery",
    "personalization": 54,
    "data": 62,
    "harm": 44,
    "opacity": 70,
    "flags": [],
    "thinEvidence": true,
    "summary": "Disclosed Revionics client (FTC 6(b) respondent).",
    "basis": {
      "personalization": "Revionics client; regional Northeast grocer with standard loyalty infrastructure.",
      "data": "My Hannaford Rewards data at regional scale.",
      "harm": "Indirect FTC 6(b) exposure via Revionics.",
      "opacity": "Same grocery-sector opacity pattern."
    },
    "evidence": [
      {
        "text": "Named as Revionics client in CNBC reporting on FTC 6(b) order.",
        "src": "CNBC, July 2024",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "hyatt",
    "name": "Hyatt",
    "sector": "Hotels",
    "personalization": 58,
    "data": 64,
    "harm": 38,
    "opacity": 70,
    "flags": [
      "ag"
    ],
    "thinEvidence": true,
    "summary": "Hyatt personalizes rates and offers through the World of Hyatt program. Sector exposure from California AG sweep.",
    "basis": {
      "personalization": "World of Hyatt personalization layered on standard dynamic pricing; lower public documentation of individualization.",
      "data": "Smaller footprint than Marriott or Hilton.",
      "harm": "CA AG sector inclusion is the main signal; no specific harm documented.",
      "opacity": "Same hotel-sector opacity pattern."
    },
    "evidence": [
      {
        "text": "Included in California AG January 2026 sector inquiry.",
        "src": "CA AG press release, Jan 27 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "walgreens",
    "name": "Walgreens",
    "sector": "Healthcare",
    "personalization": 46,
    "data": 74,
    "harm": 40,
    "opacity": 68,
    "flags": [
      "cong"
    ],
    "summary": "Walgreens operated in-store Cooler Screens with ad personalization based on demographic inference. myWalgreens rewards program collects extensive behavioral data.",
    "basis": {
      "personalization": "Cooler Screens deployment was the key surveillance-pricing infrastructure; rewards-linked offers are standard.",
      "data": "myWalgreens rewards data plus Cooler Screens demographic inference.",
      "harm": "EPIC and reporting on Cooler Screens established the pattern; direct pricing harm is not yet documented.",
      "opacity": "In-store digital displays enable demographic-responsive content without consumer disclosure."
    },
    "evidence": [
      {
        "text": "Cooler Screens partnership touted demographic-inference capabilities in its initial public materials.",
        "src": "Cooler Screens founding disclosures; EPIC analysis",
        "tier": "B"
      },
      {
        "text": "Recipient of the May 11, 2026 House Energy and Commerce Ranking Member Pallone letter to 25 food retailers and pharmacies asking how each company uses personal data to set prices. Responses due May 26, 2026.",
        "src": "Energy and Commerce Democrats press release, May 11 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "carvana",
    "name": "Carvana",
    "sector": "Auto Retail",
    "personalization": 48,
    "data": 64,
    "harm": 40,
    "opacity": 68,
    "flags": [],
    "summary": "Personalized vehicle pricing and financing offers. State-level attention to auto-finance pricing and BIPA-adjacent biometric concerns.",
    "basis": {
      "personalization": "Personalized vehicle and financing offers; reseller dynamics create wider pricing variance than traditional dealers.",
      "data": "Vehicle, credit, and behavioral data combined for financing personalization.",
      "harm": "State AG scrutiny of pricing and title-transfer practices; no surveillance-pricing specific enforcement.",
      "opacity": "Auto-finance pricing is sector-standard opaque; algorithmic layer on top deepens the pattern."
    },
    "evidence": [
      {
        "text": "Multiple state AGs have scrutinized Carvana's pricing and title-transfer practices, 2022–2024.",
        "src": "State AG actions",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "netflix",
    "name": "Netflix",
    "sector": "Streaming",
    "personalization": 38,
    "data": 82,
    "harm": 28,
    "opacity": 64,
    "flags": [],
    "summary": "Heavy personalization on the recommendation side but uniform subscription pricing by plan tier. Price-testing across A/B cohorts has occurred but is not persistent individualized pricing.",
    "basis": {
      "personalization": "Heavy content personalization but pricing is uniform by tier; A/B price tests have occurred but are cohort-level.",
      "data": "Viewing behavior and household-level signals at a scale among the largest in streaming.",
      "harm": "No persistent individualized pricing; cohort experiments surfaced but did not become the pricing model.",
      "opacity": "Tier structure is transparent; A/B price testing historically was not disclosed to the tested users."
    },
    "evidence": [
      {
        "text": "Reported A/B price-test experiments surfaced publicly in 2019 and 2024.",
        "src": "Multiple tech reporting",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "zillow",
    "name": "Zillow",
    "sector": "Housing",
    "personalization": 42,
    "data": 72,
    "harm": 28,
    "opacity": 58,
    "flags": [],
    "thinEvidence": true,
    "summary": "Zillow's Zestimate and pricing tools shape home-sale expectations but the company is not a direct price-setter for consumer transactions. Lower harm-evidence tier.",
    "basis": {
      "personalization": "Not a direct price-setter; Zestimates shape expectations but do not set transaction prices.",
      "data": "Property, search, and session data at large scale but different use case.",
      "harm": "Limited direct consumer-pricing harm; industry scrutiny focused on Zestimate accuracy claims.",
      "opacity": "Zestimate methodology is partially disclosed; personalization of Zestimate display to individual users is less clear."
    },
    "evidence": [
      {
        "text": "FTC and state consumer-protection attention has focused on Zestimate accuracy claims.",
        "src": "Industry reporting",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "spotify",
    "name": "Spotify",
    "sector": "Streaming",
    "personalization": 32,
    "data": 80,
    "harm": 22,
    "opacity": 60,
    "flags": [],
    "thinEvidence": true,
    "summary": "Tiered uniform pricing. Personalization is heavy on content recommendation but not on the price itself.",
    "basis": {
      "personalization": "Recommendation personalization is core; price personalization is not.",
      "data": "Listening behavior, device, and location data at large scale.",
      "harm": "No documented individualized pricing harm.",
      "opacity": "Pricing disclosure is strong relative to the sector; recommendation logic is not disclosed but does not affect price."
    },
    "evidence": [
      {
        "text": "Pricing is currently uniform within each plan tier across a region.",
        "src": "Spotify public pricing",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-19",
    "substackUrl": ""
  },
  {
    "id": "camden",
    "name": "Camden Property Trust",
    "sector": "Housing / Rental",
    "personalization": 72,
    "data": 78,
    "harm": 86,
    "opacity": 86,
    "flags": [
      "doj",
      "litig"
    ],
    "summary": "Camden Property Trust is one of six landlords added to DOJ's January 2025 amended complaint against RealPage. Operates approximately 60,000 apartment units; received non-public rent and acceptance data directly from Greystar according to the complaint.",
    "basis": {
      "personalization": "Used RealPage products to set unit-level rents and renewal pricing.",
      "data": "Documented receipt of Greystar non-public renewal-rate data and pricing approach per DOJ filing.",
      "harm": "DOJ defendant in active antitrust litigation; private class action exposure.",
      "opacity": "Algorithmic rent-setting opaque to tenants; competitor-coordination element opaque to public."
    },
    "evidence": [
      {
        "text": "Named in DOJ amended complaint of January 7, 2025.",
        "src": "DOJ press release, January 2025",
        "tier": "A"
      },
      {
        "text": "DOJ alleges Camden received non-public competitively sensitive information from Greystar including upcoming-quarter pricing approach.",
        "src": "DOJ amended complaint, January 2025",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "livcor",
    "name": "LivCor (Blackstone)",
    "sector": "Housing / Rental",
    "personalization": 70,
    "data": 78,
    "harm": 84,
    "opacity": 88,
    "flags": [
      "doj",
      "litig"
    ],
    "summary": "LivCor, an apartment-management subsidiary of Blackstone, is one of six landlords added to DOJ's January 2025 amended complaint against RealPage for participating in algorithmic rent-alignment.",
    "basis": {
      "personalization": "Used RealPage tools to set rents at unit level.",
      "data": "Participation in RealPage data-sharing pool with competitor non-public data.",
      "harm": "DOJ defendant; Blackstone parent exposure adds reputational dimension.",
      "opacity": "Algorithmic input invisible to tenants; corporate-parent structure adds further layer."
    },
    "evidence": [
      {
        "text": "Named in DOJ amended complaint of January 7, 2025.",
        "src": "DOJ press release, January 2025",
        "tier": "A"
      },
      {
        "text": "ProPublica reporting on Blackstone-affiliated landlord's RealPage usage cited in subsequent state-AG actions.",
        "src": "ProPublica, 2025",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "cushwake",
    "name": "Cushman & Wakefield / Pinnacle",
    "sector": "Housing / Rental",
    "personalization": 70,
    "data": 76,
    "harm": 82,
    "opacity": 86,
    "flags": [
      "doj",
      "litig"
    ],
    "summary": "Cushman & Wakefield's apartment-management subsidiary Pinnacle Property Management Services is named in DOJ's January 2025 amended complaint against RealPage and six landlords.",
    "basis": {
      "personalization": "Used RealPage products to set rents across managed portfolio.",
      "data": "Participated in RealPage non-public data-sharing pool per DOJ.",
      "harm": "DOJ defendant; private litigation exposure.",
      "opacity": "Pricing logic obscured both by algorithmic layer and by management-company arrangement."
    },
    "evidence": [
      {
        "text": "Named in DOJ amended complaint of January 7, 2025.",
        "src": "DOJ press release, January 2025",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "willowbridge",
    "name": "Willow Bridge Property Company",
    "sector": "Housing / Rental",
    "personalization": 68,
    "data": 74,
    "harm": 80,
    "opacity": 84,
    "flags": [
      "doj",
      "litig"
    ],
    "summary": "Willow Bridge (formerly Lincoln Property Residential) is one of six landlords added to DOJ's January 2025 amended complaint against RealPage.",
    "basis": {
      "personalization": "Used RealPage products to set unit-level rents.",
      "data": "Participated in RealPage data-sharing pool.",
      "harm": "Active DOJ defendant.",
      "opacity": "Renter-facing pricing process not disclosed."
    },
    "evidence": [
      {
        "text": "Named in DOJ amended complaint of January 7, 2025.",
        "src": "DOJ press release, January 2025",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "cortland",
    "name": "Cortland Management",
    "sector": "Housing / Rental",
    "personalization": 68,
    "data": 74,
    "harm": 80,
    "opacity": 84,
    "flags": [
      "doj",
      "litig"
    ],
    "summary": "Cortland Management is one of six landlords added to DOJ's January 2025 amended complaint against RealPage.",
    "basis": {
      "personalization": "Used RealPage products to set unit-level rents.",
      "data": "Participated in RealPage data-sharing pool.",
      "harm": "Active DOJ defendant.",
      "opacity": "No tenant-facing disclosure of pricing inputs."
    },
    "evidence": [
      {
        "text": "Named in DOJ amended complaint of January 7, 2025.",
        "src": "DOJ press release, January 2025",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "appfolio",
    "name": "AppFolio",
    "sector": "Housing / Rental",
    "personalization": 72,
    "data": 64,
    "harm": 50,
    "opacity": 76,
    "flags": [],
    "summary": "AppFolio markets a property management platform with revenue management features comparable in design to RealPage and Yardi. Has not been named in DOJ action or major class action as of April 2026 but appears on watch lists of advocacy groups.",
    "basis": {
      "personalization": "Revenue management module recommends per-unit rents.",
      "data": "Aggregates property-level data across clients in same metropolitan markets.",
      "harm": "No active enforcement; harm signal is comparative — methodology overlap with sued vendors.",
      "opacity": "Renters and prospective renters never see the recommendation logic."
    },
    "evidence": [
      {
        "text": "Methodology overlap with RealPage and Yardi flagged by housing-policy advocates.",
        "src": "EPIC and ProPublica commentary, 2024-2025",
        "tier": "C"
      },
      {
        "text": "Self-marketed revenue management product for residential property managers.",
        "src": "AppFolio product page",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "multiplan",
    "name": "MultiPlan / Claritev",
    "sector": "Healthcare",
    "personalization": 86,
    "data": 92,
    "harm": 94,
    "opacity": 92,
    "flags": [
      "litig"
    ],
    "summary": "MultiPlan (rebranded Claritev in 2026) is the centerpiece of MDL 3121, an antitrust action by hundreds of healthcare providers against MultiPlan and roughly 700 health plans for using a proprietary algorithm (Data iSight) to suppress out-of-network reimbursement. Survived motion to dismiss June 2025; first bellwether scheduled December 2027.",
    "basis": {
      "personalization": "Data iSight algorithm sets per-claim out-of-network reimbursement.",
      "data": "Processes 80%+ of commercial out-of-network reimbursement claims nationally; aggregates competitively sensitive insurer pricing data.",
      "harm": "Active antitrust MDL; AHA filed amicus brief; AMA and ISMS publicly oppose; allegations cover decade of systematic underpayment.",
      "opacity": "Providers and patients see only the final reimbursement; underlying logic and competitive-data inputs are not disclosed."
    },
    "evidence": [
      {
        "text": "Judge Kennelly denied motions to dismiss antitrust claims in MDL 3121 on June 3, 2025.",
        "src": "King & Spalding alert; HFMA reporting",
        "tier": "A"
      },
      {
        "text": "MDL involves Aetna, Cigna, UnitedHealth, Blue Cross Blue Shield Association and approximately 700 plans.",
        "src": "Court filings; HFMA",
        "tier": "A"
      },
      {
        "text": "First bellwether trial scheduled December 7, 2027.",
        "src": "MDL 3121 docket",
        "tier": "A"
      },
      {
        "text": "AHA amicus brief challenges MultiPlan motion to dismiss.",
        "src": "AHA, March 11, 2025",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "expressscripts",
    "name": "Express Scripts (Cigna)",
    "sector": "Healthcare / PBM",
    "personalization": 78,
    "data": 92,
    "harm": 96,
    "opacity": 94,
    "flags": [
      "ftc",
      "litig"
    ],
    "summary": "Express Scripts is the second-largest pharmacy benefit manager. Reached landmark FTC settlement on February 4, 2026, requiring fundamental changes to rebate-driven drug pricing. The complaint had alleged ESI, Caremark, and OptumRx colluded with manufacturers to inflate list prices on insulin and other drug categories.",
    "basis": {
      "personalization": "Formulary placement and member out-of-pocket cost depend on plan and patient identity; rebate steering is per-drug and per-plan.",
      "data": "Processes prescription claims for tens of millions of members; aggregates negotiation data with drug manufacturers.",
      "harm": "FTC settlement projects $7B in patient out-of-pocket savings over ten years; structural reform of business model.",
      "opacity": "Rebate flows historically opaque to plan sponsors and patients; settlement requires transparency by 2028."
    },
    "evidence": [
      {
        "text": "FTC announced landmark Express Scripts settlement on February 4, 2026.",
        "src": "FTC press release, February 2026",
        "tier": "A"
      },
      {
        "text": "Settlement requires ESI to delink manufacturer payments from list prices and base member out-of-pocket costs on net prices.",
        "src": "FTC consent order; Goodwin Law analysis",
        "tier": "A"
      },
      {
        "text": "ESI must reshore Ascent group purchasing organization from Switzerland; brings $750B+ in purchasing back to U.S.",
        "src": "FTC announcement",
        "tier": "A"
      },
      {
        "text": "FTC enforcement action against Caremark and OptumRx remains pending.",
        "src": "FTC docket",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "jetblue",
    "name": "JetBlue Airways",
    "sector": "Airlines",
    "personalization": 74,
    "data": 70,
    "harm": 78,
    "opacity": 86,
    "flags": [
      "litig"
    ],
    "summary": "First U.S. airline named in surveillance-pricing class actions. Phillips v. JetBlue (E.D.N.Y., filed April 22 2026) and Squire v. JetBlue (E.D.N.Y., filed May 1 2026) allege JetBlue.com tracked user browsing via FullStory's session-replay pipeline and fed those signals into PROS Holdings's pricing algorithm to set per-user fares without disclosure. Squire adds Virginia Consumer Protection Act and Virginia wiretap claims; both cases stack ECPA and state consumer-protection theories.",
    "basis": {
      "personalization": "Complaint alleges JetBlue website tracks user browsing and uses it to set the displayed fare.",
      "data": "Personal browsing data collected via website tracking technology.",
      "harm": "Active class action; first carrier-specific surveillance-pricing suit.",
      "opacity": "Customers do not see the inputs the algorithm uses, nor the price they would have been offered absent the data."
    },
    "evidence": [
      {
        "text": "Class action filed by NY plaintiff Andrew Phillips alleges JetBlue's website tracks browsing data and uses it to set ticket prices.",
        "src": "CBS News; Captain Compliance summary, 2025",
        "tier": "A"
      },
      {
        "text": "Lawsuit cited as test case for whether airline surveillance pricing violates state consumer protection statutes.",
        "src": "Captain Compliance, 2025",
        "tier": "B"
      },
      {
        "text": "Phillips v. JetBlue Airways Corp., No. 1:26-cv-02405 (E.D.N.Y., filed April 22, 2026). First U.S. surveillance-pricing class action against an airline. Alleges JetBlue.com's session-replay and behavior-tracking pipeline (via FullStory) fed PROS Holdings's pricing algorithm to set per-user fares without disclosure.",
        "src": "Phillips v. JetBlue complaint, April 22 2026",
        "tier": "A"
      },
      {
        "text": "Squire v. JetBlue Airways Corp., No. 1:26-cv-02629 (E.D.N.Y., filed May 1, 2026), a second class action by a Virginia plaintiff. Adds Virginia Consumer Protection Act and Virginia wiretap claims to the ECPA and NY GBL theories from Phillips. Names FullStory (behavioral analytics) and PROS Holdings (pricing algorithm) as data recipients.",
        "src": "Squire v. JetBlue complaint, May 1 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "fetcherr",
    "name": "Fetcherr",
    "sector": "Pricing Intermediary",
    "personalization": 92,
    "data": 78,
    "harm": 80,
    "opacity": 90,
    "flags": [
      "cong"
    ],
    "summary": "Fetcherr is an Israeli AI vendor whose 'Large Market Model' generative pricing engine is the centerpiece of Delta's announced AI fare-setting program, and is also used by Azul, Virgin Atlantic, WestJet, Viva Aerobus, and Royal Air Maroc. Direct subject of the July 2025 Senate letter to Delta's CEO.",
    "basis": {
      "personalization": "Markets near-continuous fare adjustment per route, segment, and arguably per session.",
      "data": "Ingests airline booking, search, and external market data; precise scope of personal-data use is the open regulatory question.",
      "harm": "Subject of Senate letter and DOT investigation in 2025.",
      "opacity": "Pricing logic invisible to passengers; airline does not disclose Fetcherr usage on most fare displays."
    },
    "evidence": [
      {
        "text": "Delta CFO Hugh Johnston disclosed goal of pricing 20% of domestic capacity through Fetcherr by end of 2025.",
        "src": "Delta earnings call; PhocusWire, 2025",
        "tier": "A"
      },
      {
        "text": "Senators Blumenthal, Gallego, Warner sent letter to Delta CEO Bastian on July 21, 2025.",
        "src": "Blumenthal Senate press release, July 2025",
        "tier": "A"
      },
      {
        "text": "DOT investigation announced August 5, 2025 into AI-driven airfare practices.",
        "src": "Adept Travel; multiple outlets, August 2025",
        "tier": "A"
      },
      {
        "text": "Fetcherr customers include Azul, Virgin Atlantic, WestJet, Viva Aerobus, Royal Air Maroc.",
        "src": "Travel and Tour World; Fetcherr case study",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "disney",
    "name": "Walt Disney Company",
    "sector": "Theme Parks / Entertainment",
    "personalization": 64,
    "data": 70,
    "harm": 60,
    "opacity": 78,
    "flags": [],
    "summary": "Disney's CFO Hugh Johnston confirmed in November 2025 that the company is investing in real-time, demand-based pricing for U.S. theme parks. Rollout planned for 2026 (Paris parks ran a year-long pilot). Single-day peak-day pricing has reached $219, up from $199 in 2025.",
    "basis": {
      "personalization": "Variable-day pricing already in effect; demand-based extension planned 2026; per-person personalization not yet documented.",
      "data": "MyDisneyExperience app, MagicBand, and on-property sensor data create the substrate for personalization.",
      "harm": "No active enforcement; consumer harm visible in $219 peak-day prices and 69% increase over decade vs 36% inflation.",
      "opacity": "Guests do not see the inputs that determine the day's price; pricing logic publicly opaque."
    },
    "evidence": [
      {
        "text": "Disney CFO Hugh Johnston confirmed real-time, demand-based pricing for U.S. theme parks in November 2025.",
        "src": "AllEars; The Wrap, 2025",
        "tier": "A"
      },
      {
        "text": "Single-day peak-day pricing reached $219 in 2026; up from $199 in 2025.",
        "src": "Mickey Visit reporting",
        "tier": "B"
      },
      {
        "text": "Disney already deploys variable-day pricing across U.S. parks; single-day costs vary by date and demand.",
        "src": "Disney Tourist Blog; WDW Magazine",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "lexisnexisrisk",
    "name": "LexisNexis Risk Solutions",
    "sector": "Insurance Data Broker",
    "personalization": 86,
    "data": 96,
    "harm": 78,
    "opacity": 90,
    "flags": [
      "ag"
    ],
    "summary": "LexisNexis Risk Solutions operates the C.L.U.E. (Comprehensive Loss Underwriting Exchange) database — seven years of auto and home insurance claims used by 98 of the top 100 U.S. personal-lines carriers for pricing and underwriting. Telematics OnDemand collects per-trip driving behavior; Kia, Mitsubishi, and Subaru are documented OEM data partners. Subject of CFPB consumer-tools listing for the C.L.U.E. database.",
    "basis": {
      "personalization": "C.L.U.E. data and Telematics OnDemand data feed individualized insurance premiums.",
      "data": "Seven years of claims; over 10 million telematics-equipped vehicles by 2022; roughly 600 U.S. general insurer partners.",
      "harm": "Documented impact on premium calculations; OEM data-sharing has triggered FTC complaints about other parties (Allstate Arity).",
      "opacity": "Policyholders rarely understand how C.L.U.E. or telematics data affect their premium."
    },
    "evidence": [
      {
        "text": "CFPB consumer-tools page identifies C.L.U.E. as a major consumer reporting database.",
        "src": "CFPB consumer reporting list",
        "tier": "A"
      },
      {
        "text": "Inside EVs investigation documents data sharing with Kia, Mitsubishi, Subaru.",
        "src": "Inside EVs, 2024",
        "tier": "B"
      },
      {
        "text": "Self-disclosed: 98 of top 100 U.S. personal-lines carriers and 21 of top 25 global insurers.",
        "src": "LexisNexis Risk Solutions corporate page",
        "tier": "C"
      },
      {
        "text": "Named as a buyer of names, contact information, geolocation, and driving-behavior data on hundreds of thousands of Californians sold by General Motors and OnStar between 2020 and 2024. The California Attorney General's $12.75M GM CCPA settlement on May 8, 2026 is the first state AG action that names LexisNexis Risk Solutions as a downstream purchaser of consumer driver data.",
        "src": "California Attorney General press release, May 8 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "verisk",
    "name": "Verisk Analytics",
    "sector": "Insurance Data Broker",
    "personalization": 80,
    "data": 92,
    "harm": 72,
    "opacity": 86,
    "flags": [
      "ag"
    ],
    "summary": "Verisk is the LexisNexis Risk counterpart in the property-casualty insurance data layer. Provides underwriting, rating, claims, and catastrophe modeling. Documented telematics partnerships with Ford, Honda, Hyundai.",
    "basis": {
      "personalization": "Underwriting and rating products feed individualized premium decisions.",
      "data": "Telematics, claims, property, and risk data sold to industry-leading carriers.",
      "harm": "Used by majority of U.S. property-casualty carriers; impact on individualized pricing is structural.",
      "opacity": "Policyholders not generally aware their telematics data flows through Verisk to insurers."
    },
    "evidence": [
      {
        "text": "Inside EVs documents data sharing partnerships with Ford, Honda, Hyundai.",
        "src": "Inside EVs, 2024",
        "tier": "B"
      },
      {
        "text": "Self-marketed underwriting and rating solutions for the global insurance industry.",
        "src": "Verisk corporate materials",
        "tier": "C"
      },
      {
        "text": "Named as a buyer of names, contact information, geolocation, and driving-behavior data on hundreds of thousands of Californians sold by General Motors and OnStar between 2020 and 2024. The California Attorney General's $12.75M GM CCPA settlement on May 8, 2026 is the first state AG action that names Verisk as a downstream purchaser of consumer driver data.",
        "src": "California Attorney General press release, May 8 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "earnix",
    "name": "Earnix (with Akur8)",
    "sector": "Pricing Intermediary",
    "personalization": 92,
    "data": 84,
    "harm": 86,
    "opacity": 88,
    "flags": [
      "ag"
    ],
    "summary": "Earnix is the leading insurance price-optimization vendor, now combined with Akur8. Subject of regulatory bulletins prohibiting or restricting price optimization in eleven states (CA, DE, FL, IN, ME, MD, OH, PA, RI, VT, WA) plus DC. Earnix's own survey found 45% of large auto insurers using price optimization.",
    "basis": {
      "personalization": "Optim and risk-modeling modules explicitly produce individualized premiums based on price elasticity and risk.",
      "data": "Consumes claims, telematics, and demographic data; delivers per-policy price recommendations.",
      "harm": "Eleven state insurance bulletins finding the practice unfairly discriminatory under state insurance code.",
      "opacity": "Policyholders not informed when price-optimization rather than pure-risk modeling drives a renewal."
    },
    "evidence": [
      {
        "text": "Eleven states plus DC have issued bulletins prohibiting or restricting price optimization in personal lines.",
        "src": "Insurance Journal; Carrier Management; CFA report",
        "tier": "A"
      },
      {
        "text": "Earnix-conducted survey found 45% of insurers with $1B+ premium using price optimization techniques.",
        "src": "CFA press release citing Earnix survey",
        "tier": "B"
      },
      {
        "text": "Akur8 Optim module marketed as available only in markets where it complies with local regulatory requirements.",
        "src": "Akur8 product page",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "vusiongroup",
    "name": "VusionGroup (formerly SES-imagotag)",
    "sector": "Pricing Hardware / ESL",
    "personalization": 56,
    "data": 70,
    "harm": 68,
    "opacity": 78,
    "flags": [
      "cong"
    ],
    "summary": "VusionGroup is the dominant electronic shelf label vendor for U.S. retail. Walmart's contract extension covers all 4,600 U.S. stores by end-2026. Direct target of the Lujan-Merkley Stop Price Gouging in Grocery Stores Act, which would ban ESLs in groceries larger than 10,000 square feet.",
    "basis": {
      "personalization": "EdgeSense and VusionCloud platforms support remote dynamic pricing per-shelf in real time.",
      "data": "Centralized device management collects real-time customer interactions and inventory signals.",
      "harm": "Subject of Senate Warren-Casey letter (via Kroger) and standalone Lujan-Merkley legislation.",
      "opacity": "Shoppers cannot see when a price changed or what triggered it."
    },
    "evidence": [
      {
        "text": "Walmart contract extension to deploy VusionGroup ESLs across all 4,600 U.S. stores by end of 2026.",
        "src": "P2PI; Retail Dive; Walmart corporate",
        "tier": "A"
      },
      {
        "text": "Subject of Lujan-Merkley Stop Price Gouging in Grocery Stores Act banning ESLs in stores over 10,000 sq ft.",
        "src": "Albuquerque Journal; Senate press release",
        "tier": "A"
      },
      {
        "text": "EdgeSense product collects real-time customer interaction data alongside pricing and inventory.",
        "src": "VusionGroup product materials",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "invenda",
    "name": "Invenda Group",
    "sector": "Smart Vending / Surveillance",
    "personalization": 78,
    "data": 72,
    "harm": 70,
    "opacity": 82,
    "flags": [],
    "summary": "Invenda is a Swiss smart-vending vendor partnered with Coca-Cola and Mars Wrigley. Cameras on its retrofit kits perform demographic estimation (age and gender) for advertising; remote pricing capability is built into the same console. Identified by University of Waterloo students in February 2024.",
    "basis": {
      "personalization": "Luna platform features dynamic pricing controlled remotely from a single dashboard alongside facial-recognition demographics.",
      "data": "Cameras estimate age and gender; company defends as anonymous and on-device but acknowledges feature exists.",
      "harm": "Privacy investigation in Canada; news coverage in Wired, EFF, Born's City; symbolic of the integration of surveillance and pricing.",
      "opacity": "Consumers not informed cameras are present; presence discovered by accidental error message at a university machine."
    },
    "evidence": [
      {
        "text": "University of Waterloo machines surfaced 'Invenda.Vending.FacialRecognitionApp.exe' error message exposing camera feature in February 2024.",
        "src": "Born's City reporting; Wired",
        "tier": "B"
      },
      {
        "text": "Invenda partners include Coca-Cola, Mars Wrigley, Selecta, Valora.",
        "src": "Invenda corporate materials; Vending Connection",
        "tier": "B"
      },
      {
        "text": "Luna platform self-marketed to support remote dynamic pricing alongside camera-based audience analytics.",
        "src": "Invenda Luna platform page",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "eversight",
    "name": "Eversight (Instacart, discontinued)",
    "sector": "Pricing Intermediary",
    "personalization": 92,
    "data": 80,
    "harm": 82,
    "opacity": 88,
    "flags": [
      "ftc"
    ],
    "summary": "Eversight, acquired by Instacart in 2022, ran 'item price tests' that surfaced multiple prices for the same product to different shoppers in the same store at the same time. Consumer Reports + Groundwork Collaborative found 74% of items in tests offered at multiple price points; up to five distinct prices observed; up to 23% spread. Discontinued December 22, 2025 after FTC scrutiny and Klobuchar-Booker referral.",
    "basis": {
      "personalization": "Tested per-customer price discrimination via experimentation infrastructure; same item shown at different prices simultaneously.",
      "data": "Used Instacart purchase, search, and behavioral data; integrated with retailer client data.",
      "harm": "Concluded after FTC investigation; Klobuchar-Booker letter to FTC; Consumer Reports investigation documenting up to 23% price spread.",
      "opacity": "Shoppers shown a single price each, with no awareness others saw a different one."
    },
    "evidence": [
      {
        "text": "Instacart announced shutdown of Eversight item-price tests on December 22, 2025.",
        "src": "CNBC; Instacart press release, December 2025",
        "tier": "A"
      },
      {
        "text": "FTC investigation underway; Klobuchar-Booker letter to FTC requesting investigation.",
        "src": "Klobuchar Senate press release; Lexology summary",
        "tier": "A"
      },
      {
        "text": "Consumer Reports + Groundwork Collaborative report: 74% of items tested at multiple price points; up to 5 distinct prices same item same store same time; 13% average spread, 23% maximum.",
        "src": "Consumer Reports; Groundwork Collaborative, 2025",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "vendavo",
    "name": "Vendavo",
    "sector": "Pricing Intermediary",
    "personalization": 76,
    "data": 64,
    "harm": 50,
    "opacity": 80,
    "flags": [],
    "summary": "Vendavo provides B2B price optimization, deal management, and CPQ tooling. Markets itself for B2B and B2C pricing managers. Not named in any FTC or DOJ action as of April 2026 but deployed broadly across industrial distribution.",
    "basis": {
      "personalization": "Deal-specific guidance produces customer-segment and account-level price differentiation.",
      "data": "Aggregates client transaction data; less consumer-data-heavy than Revionics or Bloomreach.",
      "harm": "No active enforcement; harm signal is potential rather than actual.",
      "opacity": "B2B model means end-buyer sees only the negotiated price, not the optimization logic."
    },
    "evidence": [
      {
        "text": "Self-marketed AI-driven deal optimization for B2B revenue growth.",
        "src": "Vendavo product materials",
        "tier": "C"
      },
      {
        "text": "Listed as major pricing software platform alongside Pricefx, PROS in industry comparisons.",
        "src": "Gartner Peer Insights; Symson; Withorb",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "blueyonder",
    "name": "Blue Yonder",
    "sector": "Pricing Intermediary",
    "personalization": 80,
    "data": 76,
    "harm": 60,
    "opacity": 82,
    "flags": [],
    "summary": "Blue Yonder's AI-Enabled Pricing markets a 15% in-season profitability lift and is widely deployed in U.S. grocery and apparel. Acquired by Panasonic; positions itself directly against Revionics and PROS. Not currently named in FTC or DOJ action.",
    "basis": {
      "personalization": "Demand forecasting at SKU-day-store level; Pricing Real Time module supports continuous price changes.",
      "data": "Granular client retailer data combined with public competitive intelligence.",
      "harm": "Methodology overlap with FTC 6(b) targets; no active enforcement.",
      "opacity": "Pricing logic invisible to shoppers; vendor sits one layer behind retailer."
    },
    "evidence": [
      {
        "text": "Blue Yonder self-markets 15% in-season profitability lift via AI pricing.",
        "src": "Blue Yonder marketing materials",
        "tier": "C"
      },
      {
        "text": "Listed alongside Revionics and Manhattan Associates in retailer pricing-software comparisons.",
        "src": "Goodfirms; Gartner",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "competera",
    "name": "Competera",
    "sector": "Pricing Intermediary",
    "personalization": 74,
    "data": 70,
    "harm": 50,
    "opacity": 78,
    "flags": [],
    "summary": "Competera is an AI retail-pricing vendor focused on competitive intelligence and category-level optimization. Less consumer-data-heavy than Revionics or Eversight but plays in the same broader category.",
    "basis": {
      "personalization": "Optimizes prices at category and SKU level; per-customer personalization more limited.",
      "data": "Web-scraped competitor pricing combined with client retailer data.",
      "harm": "No active enforcement.",
      "opacity": "Retailer-side only; consumer never sees the optimization layer."
    },
    "evidence": [
      {
        "text": "Self-marketed AI pricing platform for competitor intelligence and price optimization.",
        "src": "Competera product materials",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "experian-marketing",
    "name": "Experian Marketing Services",
    "sector": "Data Broker",
    "personalization": 74,
    "data": 92,
    "harm": 60,
    "opacity": 84,
    "flags": [],
    "summary": "Distinct from Experian Credit, Experian Marketing Services sells transactional and consumer-view data to retail, insurance, and financial-services pricing teams. Long-running ProPublica reporting flagged Experian as a major shadow-profile builder.",
    "basis": {
      "personalization": "ConsumerView product designed to segment and personalize at individual level.",
      "data": "Approximately 300M U.S. consumer records; transactional, demographic, and lifestyle attributes.",
      "harm": "No active enforcement on Experian Marketing specifically; harm is structural via downstream pricing customers.",
      "opacity": "Consumers cannot easily see what Experian Marketing has on them or how it is used in pricing."
    },
    "evidence": [
      {
        "text": "ProPublica documented Experian among the largest shadow-profile data brokers in U.S.",
        "src": "ProPublica, 2014 (still cited)",
        "tier": "B"
      },
      {
        "text": "Experian ConsumerView marketed for retailer segmentation, personalization, and prospect targeting.",
        "src": "Experian Marketing Services product page",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "epsilon",
    "name": "Epsilon (Publicis)",
    "sector": "Data Broker",
    "personalization": 72,
    "data": 90,
    "harm": 58,
    "opacity": 84,
    "flags": [],
    "summary": "Epsilon, owned by Publicis Groupe, is a major loyalty-data broker dating to 1969. Operates the Conversant ID graph used for cross-device targeting and price personalization. Public Acxiom-style segmentation including 'Asian Achievers' and similar demographic-lifestyle bundles.",
    "basis": {
      "personalization": "ID-graph-driven segmentation enables per-individual targeting.",
      "data": "Loyalty programs, online tracking, email interaction, transaction logs.",
      "harm": "Settled with DOJ in 2021 over mailing-list sales facilitating fraud; no current surveillance-pricing-specific enforcement.",
      "opacity": "Consumers not informed of loyalty-data transit through Epsilon."
    },
    "evidence": [
      {
        "text": "Identified by Tom Kemp and Built In as one of the major identity-resolution and audience brokers feeding personalized pricing.",
        "src": "Tom Kemp Medium analysis; Built In top-data-brokers piece",
        "tier": "B"
      },
      {
        "text": "Conversant ID graph publicly marketed for cross-device matching.",
        "src": "Epsilon corporate materials",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "equifax",
    "name": "Equifax",
    "sector": "Data Broker",
    "personalization": 66,
    "data": 92,
    "harm": 56,
    "opacity": 82,
    "flags": [],
    "summary": "Equifax operates a regulated credit bureau plus non-credit subsidiaries (Equifax IXI / Workforce Solutions) that sell income proxies, employment data, and identity data used in price segmentation outside the credit-reporting context.",
    "basis": {
      "personalization": "IXI income-proxy products are explicitly designed for individualized targeting.",
      "data": "Combined credit, income, and employment data.",
      "harm": "2017 breach; ongoing concerns about employment-verification monopoly; no current surveillance-pricing-specific enforcement.",
      "opacity": "Non-credit data sales are less regulated and less visible to consumers than credit-side activity."
    },
    "evidence": [
      {
        "text": "Identified as major data broker selling income and employment data outside the credit-reporting framework.",
        "src": "Tom Kemp Medium; Webopedia analysis",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "transunion",
    "name": "TransUnion",
    "sector": "Data Broker",
    "personalization": 70,
    "data": 92,
    "harm": 60,
    "opacity": 82,
    "flags": [],
    "summary": "TransUnion operates a regulated credit bureau plus non-credit subsidiaries (Neustar, TLO, TruAudience) that sell identity-resolution and audience data used in price segmentation and ad targeting.",
    "basis": {
      "personalization": "Neustar identity graph and TLOxp investigative products designed for individualized targeting.",
      "data": "Combined credit, identity, location, and audience-attribute data.",
      "harm": "TransUnion has settled FTC and CFPB actions over consumer-reporting practices; surveillance-pricing-specific actions not yet active.",
      "opacity": "Non-credit data businesses are less regulated and less visible than credit reporting."
    },
    "evidence": [
      {
        "text": "Identified as major data broker; Neustar acquisition expanded identity-resolution role.",
        "src": "Tom Kemp Medium; Built In; Webopedia",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "safegraph",
    "name": "SafeGraph / Veraset",
    "sector": "Location Data Broker",
    "personalization": 80,
    "data": 90,
    "harm": 76,
    "opacity": 86,
    "flags": [],
    "summary": "SafeGraph and its 2019 spinoff Veraset are among the most prominent precision-location data brokers in the U.S. EFF has flagged both repeatedly. Veraset sells raw, disaggregated, per-device location data; SafeGraph sells points-of-interest and aggregated foot-traffic data.",
    "basis": {
      "personalization": "Per-device location data is a precise willingness-to-pay proxy (e.g., Target parking-lot pricing).",
      "data": "Mobility data from 575M+ devices; over 6M U.S. commercial properties documented.",
      "harm": "EFF documentation; banned from Google Play Store in 2020; Illinois purchasing controversy.",
      "opacity": "Consumers do not consent to or see the SDK-derived location-data flow."
    },
    "evidence": [
      {
        "text": "EFF documented SafeGraph and Veraset's role in mobile location-data brokering.",
        "src": "EFF Deeplinks, 2021-2022",
        "tier": "B"
      },
      {
        "text": "Illinois state government purchase of SafeGraph data became scandal in 2021.",
        "src": "EFF reporting",
        "tier": "B"
      },
      {
        "text": "Veraset sells raw, disaggregated, per-device location data; pricing $0.10-$30,000.",
        "src": "Datarade; Dewey Data documentation",
        "tier": "C"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "placer",
    "name": "Placer.ai",
    "sector": "Location Data Broker",
    "personalization": 72,
    "data": 86,
    "harm": 60,
    "opacity": 82,
    "flags": [],
    "summary": "Placer.ai sells foot-traffic analytics for retailers, real-estate, and consumer-research. Markets data as anonymized but reverse-identifiable in published academic studies; standard input for retail pricing and store-placement analysis.",
    "basis": {
      "personalization": "Audience overlays support per-segment marketing and pricing decisions.",
      "data": "Mobility data from third-party SDK partners; aggregate dashboards covering U.S. retail.",
      "harm": "No active enforcement; harm signal is structural — feeds pricing decisions for retailers.",
      "opacity": "Consumers do not see SDK embedding in the apps that supply the data."
    },
    "evidence": [
      {
        "text": "Listed by SafeGraph as competitor in U.S. location-intelligence market.",
        "src": "SafeGraph competitor pages; Datarade",
        "tier": "C"
      },
      {
        "text": "Reverse-identification of supposedly anonymous location data documented in academic literature on mobility data.",
        "src": "EFF; academic studies of mobility data anonymization",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-04-30",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "gm",
    "name": "General Motors",
    "sector": "Automotive",
    "personalization": 72,
    "data": 84,
    "harm": 86,
    "opacity": 92,
    "flags": [
      "ag"
    ],
    "summary": "General Motors and its OnStar telematics service sold names, contact information, geolocation, and driving-behavior data on hundreds of thousands of Californians to Verisk Analytics and LexisNexis Risk Solutions between 2020 and 2024, who in turn built driver-rating products marketed to auto insurers. On May 8, 2026 the California Attorney General, joined by the San Francisco, Los Angeles, Napa, and Sonoma district attorneys, secured a $12.75M CCPA settlement, the largest CCPA penalty to date. GM must stop selling driving data to consumer reporting agencies for five years and delete retained driving data within 180 days absent express consent.",
    "basis": {
      "personalization": "Per-vehicle telematics produces an individualized driving-behavior profile tied to identity and location, which downstream insurance carriers use to set premiums.",
      "data": "OnStar collected continuous trip-level telemetry and identity, sold via Verisk and LexisNexis Risk Solutions to roughly 100 auto insurers.",
      "harm": "Largest CCPA settlement to date; first state AG action naming both Verisk and LexisNexis Risk as named purchasers of consumer driving data.",
      "opacity": "Drivers were not given clear notice that smart-driver and trip-summary features fed into a saleable insurance-rating product."
    },
    "evidence": [
      {
        "text": "California AG Bonta, with SF, LA, Napa, and Sonoma DAs, secured a $12.75M CCPA settlement against General Motors and OnStar for the sale of names, contact information, geolocation, and driving-behavior data on hundreds of thousands of Californians to Verisk and LexisNexis Risk Solutions between 2020 and 2024. Largest CCPA penalty to date.",
        "src": "California Attorney General press release, May 8 2026",
        "tier": "A"
      },
      {
        "text": "Settlement requires GM to stop selling driving data to consumer reporting agencies for five years and delete retained driving data within 180 days absent express consent.",
        "src": "TechCrunch, May 9 2026",
        "tier": "B"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "kochava",
    "name": "Kochava",
    "sector": "Data Broker",
    "personalization": 56,
    "data": 94,
    "harm": 84,
    "opacity": 90,
    "flags": [
      "ftc"
    ],
    "summary": "Mobile-attribution and location-data broker. Subject of the August 2022 FTC complaint alleging sale of precise mobile-location data on hundreds of millions of devices, including visits to sensitive venues. On May 4, 2026 the FTC announced a proposed settlement banning Kochava and its successor Collective Data Solutions from selling, sharing, or disclosing sensitive-location data without affirmative express consent. The settlement requires that consumers be able to request the names of third parties that bought their precise location data, a first-of-its-kind transparency obligation against a mobile-location broker.",
    "basis": {
      "personalization": "Location feeds are an upstream input to personalized pricing, even though Kochava itself does not set prices.",
      "data": "Self-described scale of hundreds of millions of mobile devices.",
      "harm": "FTC enforcement under unfair-or-deceptive-practices authority; second major federal action against a mobile-location broker after Outlogic.",
      "opacity": "Consumers historically had no visibility into which apps fed Kochava or which downstream parties received the data."
    },
    "evidence": [
      {
        "text": "FTC proposed settlement bans Kochava and successor Collective Data Solutions from selling, sharing, or disclosing sensitive location data without affirmative express consent; requires consumer-facing disclosure of named third-party buyers.",
        "src": "FTC press release, May 4 2026",
        "tier": "A"
      },
      {
        "text": "FTC v. Kochava Inc., U.S. District Court of Idaho, original complaint filed August 2022 alleging sale of mobile-location data covering hundreds of millions of devices, including visits to sensitive venues.",
        "src": "FTC case timeline, May 4 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": false
  },
  {
    "id": "fullstory",
    "name": "FullStory",
    "sector": "Behavioral Analytics",
    "personalization": 70,
    "data": 80,
    "harm": 64,
    "opacity": 88,
    "flags": [
      "litig"
    ],
    "summary": "Session-replay and digital-experience analytics vendor named as a data recipient in the two pending JetBlue surveillance-pricing class actions (Phillips, filed April 22 2026, and Squire, filed May 1 2026, both E.D.N.Y.). Plaintiffs allege FullStory's session-replay technology recorded mouse movement, clicks, scroll behavior, and form inputs on JetBlue.com and that those signals flowed downstream into the pricing engine. Public evidence to date is limited to the complaints; no direct enforcement against FullStory.",
    "basis": {
      "personalization": "Session-replay produces per-user behavior streams that downstream pricing systems can plausibly use as willingness-to-pay signals.",
      "data": "Documented deployment on consumer-facing brands across travel, retail, and finance.",
      "harm": "Named in active surveillance-pricing class actions but no direct regulatory action.",
      "opacity": "Consumers generally do not see disclosures that their on-site mouse and scroll behavior is replayed and retained."
    },
    "evidence": [
      {
        "text": "Named alongside PROS Holdings as a data recipient in Phillips v. JetBlue Airways Corp., No. 1:26-cv-02405 (E.D.N.Y., filed April 22, 2026), the first surveillance-pricing class action against a U.S. airline.",
        "src": "Phillips v. JetBlue complaint, April 22 2026",
        "tier": "A"
      },
      {
        "text": "Also named in Squire v. JetBlue Airways Corp., No. 1:26-cv-02629 (E.D.N.Y., filed May 1, 2026), a parallel class action adding Virginia Consumer Protection Act and Virginia wiretap claims.",
        "src": "Squire v. JetBlue complaint, May 1 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "whole-foods",
    "name": "Whole Foods Market",
    "sector": "Grocery",
    "personalization": 56,
    "data": 64,
    "harm": 50,
    "opacity": 70,
    "flags": [
      "cong"
    ],
    "summary": "Amazon-owned grocery chain named in the May 11, 2026 House Energy and Commerce inquiry into food-retailer surveillance pricing practices. Letter from Ranking Member Pallone asks how the chain collects personal data, whether it uses that data to set prices, what third-party data it purchases, and whether opt-out is available. Response due May 26, 2026. Public file beyond the letter is light; the surveillance-pricing capability sits upstream in Amazon's advertising and loyalty infrastructure.",
    "basis": {
      "personalization": "Integration with the Amazon Prime / advertising stack creates structural capacity for individualized pricing, though no documented in-store deployment yet.",
      "data": "Pallone letter covers purchase history, app data, location, and inferred attributes.",
      "harm": "Congressional inquiry only; no enforcement.",
      "opacity": "Customers receive standard Amazon retail disclosures; no surveillance-pricing-specific notice."
    },
    "evidence": [
      {
        "text": "Recipient of House Energy and Commerce Ranking Member Pallone letter on retail surveillance pricing, asking for documentation of data inputs, AI / ML pricing use, third-party data purchases, and consumer opt-out. Response due May 26, 2026.",
        "src": "Energy and Commerce Democrats press release, May 11 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "wegmans",
    "name": "Wegmans Food Markets",
    "sector": "Grocery",
    "personalization": 50,
    "data": 60,
    "harm": 48,
    "opacity": 66,
    "flags": [
      "cong"
    ],
    "summary": "Northeast US grocery chain named in the May 11, 2026 House Energy and Commerce inquiry into food-retailer surveillance pricing practices. Letter from Ranking Member Pallone asks how the chain collects personal data, whether it uses that data to set prices, what third-party data it purchases, and whether opt-out is available. Response due May 26, 2026. Public surveillance-pricing file outside the letter is thin.",
    "basis": {
      "personalization": "Loyalty program and digital-coupon app produce a per-customer purchase profile, but no documented surge or per-shopper pricing.",
      "data": "Pallone letter covers purchase history, app data, location, and inferred attributes.",
      "harm": "Congressional inquiry only; no enforcement.",
      "opacity": "Customers receive standard loyalty disclosures."
    },
    "evidence": [
      {
        "text": "Recipient of House Energy and Commerce Ranking Member Pallone letter on retail surveillance pricing, asking for documentation of data inputs, AI / ML pricing use, third-party data purchases, and consumer opt-out. Response due May 26, 2026.",
        "src": "Energy and Commerce Democrats press release, May 11 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": true
  },
  {
    "id": "stop-and-shop",
    "name": "Stop & Shop",
    "sector": "Grocery",
    "personalization": 50,
    "data": 60,
    "harm": 48,
    "opacity": 66,
    "flags": [
      "cong"
    ],
    "summary": "Ahold Delhaize-owned grocery chain named in the May 11, 2026 House Energy and Commerce inquiry into food-retailer surveillance pricing practices. Letter from Ranking Member Pallone asks how the chain collects personal data, whether it uses that data to set prices, what third-party data it purchases, and whether opt-out is available. Response due May 26, 2026. Public surveillance-pricing file outside the letter is thin; Ahold Delhaize has piloted electronic shelf labels in adjacent banners.",
    "basis": {
      "personalization": "Loyalty program plus the broader Ahold Delhaize ESL pilots create structural capability, but no documented per-shopper pricing.",
      "data": "Pallone letter covers purchase history, app data, location, and inferred attributes.",
      "harm": "Congressional inquiry only; no enforcement.",
      "opacity": "Customers receive standard loyalty disclosures."
    },
    "evidence": [
      {
        "text": "Recipient of House Energy and Commerce Ranking Member Pallone letter on retail surveillance pricing, asking for documentation of data inputs, AI / ML pricing use, third-party data purchases, and consumer opt-out. Response due May 26, 2026.",
        "src": "Energy and Commerce Democrats press release, May 11 2026",
        "tier": "A"
      }
    ],
    "lastReviewed": "2026-05-13",
    "substackUrl": "",
    "thinEvidence": true
  }
]