Personalised pricing software and the return of haggling, run from the seller's side

This came from a question about Canadian living costs: are the prices of necessities still set by markets at all? The answer starts with something everyone takes for granted. The price tag, one number for every buyer, is a 19th-century invention, and software is replacing it with prices worked out for each buyer from what the seller knows about them. That brings back the haggle, except that now only the seller gets to bargain.

1The price tag is a 19th-century invention

The price on the tag feels like the natural state of things. It is not. Before the mid-19th century, most retail buying in America was done by haggling; merchants intentionally withheld a fixed price (if a merchant thought he could get $10 for an axe, he would not let you see a $7 price).Key findings The one-price system began with conviction. Philadelphia's Quaker merchants decided all should pay the sameKey findings; a New York merchant made it commercial around 1846Key findings; and John Wanamaker, who opened his first store in 1861 and formalized fixed pricing and price tags at his Grand Depot department store (1875–1876)Key findings, made it ordinary. Scale is what made it stick: The department store and the fixed price were the same event: mass scale made person-by-person bargaining impossible, and the posted price was the technology that resolved it.Key findings

Treat the tag as a technology and the question changes. Technologies get replaced when something cheaper does the job, and bargaining with each customer one at a time is now cheap. Online prices already move faster than any shelf could follow: in 2013 Amazon was found to make more than 2.5 million price changes per dayDetails, a figure the research calls real but a decade oldKey findings.

2A price worked out for you is not a faster price tag

Two different things get called dynamic pricing, and the distinction carries the argument. A price that moves with supply and demand is still one public number. The research's rule: Concede dynamic pricing is faster discovery; insist personalized pricing is something else.Details In the personalised case there is no longer a single public price to discover; the seller computes a private number from your dataDetails. New York's law draws the same line. Its required notice reads "THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA."Key findings and pricing varying solely on supply/demand or time of day is exemptKey findings.

The data behind it is detailed. A US consumer-protection study of pricing intermediaries found they "worked with at least 250 clients that sell goods or services ranging from grocery stores to apparel retailers,"Key findings and that details like a person's precise location or browser history can be frequently used to target individual consumers with different prices for the same goods and servicesDetails. The agency's chair at the time described prices set "from a person's location and demographics, down to their mouse movements on a webpage."Details The research notes the study's limits: its findings are preliminary staff observations, not a finding of illegalityKey findings.

3The haggle comes back, with the seller holding the information

The research's sharpest description of where this leads is the market the price tag replaced: surveillance pricing is the return of the haggle, but with the asymmetry inverted — the seller now knows everything about the buyer.Details In a bazaar the buyer could read the seller's face and walk away; here the seller reads the buyer's data and the buyer sees nothing.

The incentive is not subtle. In one field experiment, personalized pricing "improves the firm's expected posterior profits by an additional 19%, relative to the optimized uniform price, and by 86%, relative to the firm's unoptimized status quo price"The pricing algorithm. A merchant who can earn a fifth more by reading each customer will not keep posting one number for long. The research names the end point too: The limit case is perfect (first-degree) price discrimination, where consumer surplus is driven to zero.The pricing algorithm

Who gains is contested, and the research does not pretend otherwise. One study of personalised pricing found that "over 60% of consumers benefit from lower prices under personalization,"Details but also that "total consumer surplus declines 23% under personalized pricing relative to uniform pricing"Details, while firm profits rise. The research places the objection accordingly: The honest position concedes ambiguous welfare and locates the objection in distribution, asymmetry, and autonomy — not in pure efficiency.Details

4When every landlord uses the same software

Rent shows what happens when the same software serves every seller in a market. In the US litigation, RealPage's clients comprised nearly 90% of the market for multifamily rental housing units of 50 units or more, and court documents cited landlords accepting the software's pricing recommendations 80–90% of the time.Key findings The company's own words for the goal were "driving every possible opportunity to increase price"Key findings. The US Justice Department's antitrust chief put it in one sentence: "RealPage was replacing competition with coordination, and renters paid the price."Key findings

The US government's framing is that the meeting has moved into the tool: where collusion once required "a formal handshake in a clandestine meeting," now "algorithms are the new frontier."Key findings Prosecutors allege one landlord raised rents more than 25% within 11 months of adopting the softwareKey findings. The settlement filed in November 2025 imposes no fine and no admission of wrongdoing but bars RealPage from using nonpublic, competitively sensitive data in model training and runtimeKey findings.

5Software can learn to coordinate without being told

The harder question is whether pricing programs drift toward high prices on their own. In simulation, learning algorithms "consistently learn to charge supracompetitive prices, without communicating with one another,"Details though later work showed the outcome flips with the learning protocolKey findings. The strongest real-market study, of German petrol stations, found adoption "increases margins by 9%, but only in non-monopoly markets,"Key findings and that margins "increase by 28% in markets where both [duopolists]"Key findings adopt. The timing points to learning rather than agreement: the effect appeared "about a year after market-wide adoption, suggesting that algorithms in this market learn tacitly-collusive strategies."Key findings The research's own instruction is plain: Present this as contested, not provenKey findings

6Canada is still looking for the handshake

Canadian use was real, and small. A CBC investigation found YieldStar has been used in Canada since at least 2017Key findings; several large landlords said they had used it and stopped, and the vendor said its software has never served more than 1% of the rental market in CanadaKey findings. Concentration is what makes it matter: Algorithmic rent-setting is dangerous in proportion to concentration, and Canadian concentration is high and rising even as YieldStar specifically retreats.Key findings

The software has been here for years. The earliest documentary trace is a 2017 RealPage press release naming a Canadian client reporting "2%–4% revenue outperformance"Key findings. Both attempts to hold anyone to account have now ended. The Competition Bureau discontinued its civil inquiry into RealPage Canada and Yardi Canada on November 10, 2025, finding adoption "not sufficiently widespread"Key findings, and the class action was discontinued before certification. Meanwhile, the Bureau's own discussion paper noted Canada has over 60 companies offering algorithmic pricing services across sectors.Key findings

The law explains the outcome. Section 45 (criminal conspiracy) requires proof of an agreement beyond a reasonable doubt; section 90.1 (civil) reaches anticompetitive agreements or arrangements but still demands an "agreement" — a "meeting of the minds."Key findings Neither reaches tacit algorithmic coordination, and conscious parallelism is not illegal in Canada.Key findings The research's summary: the law is built for human agreements, and the technology routes around it.Key findings A law written to catch competitors agreeing will not catch competitors who never need to.

7Price the same basket on different phones

Rent is the case with court records. Everyday shopping is the case nobody in Canada has measured. The research proposes the test: a tool that detects/documents price variation by user signal (location, device, history) across Canadian e-commerceCandidate A. If the same basket costs different amounts on different phones, the price tag is already gone, and only the tag on the shelf is left to pretend otherwise.