Picture this: two of your delegates book the exact same conference pass, on the exact same day, for the exact same event. One of them pays more than the other. Not because of an early-bird window, not because of a VIP tier, but because of which web browser they happened to open. Welcome to the curious new world of personalised pricing, where the meter is set less by what you are buying and more by who the algorithm thinks you are.
That is the uncomfortable possibility raised by Dr Barry Kahn, the man who helped bring dynamic pricing to live entertainment two decades ago, in a viewpoint piece for TheTicketingBusiness. Over roughly six months, Kahn says he documented more than 100 purchase flows on resale giant StubHub and found that the price of an identical ticket shifted depending on the browser, the device, and what the site appeared to know about the shopper.
The receipts (quite literally)
Kahn's headline example involved front row tickets to Bruno Mars at SoFi Stadium in Los Angeles. Same seats, same moment, four different browsers, four different totals:
| Browser | StubHub price: Bruno Mars front row, SoFi Stadium |
|---|---|
| Google Chrome | $5,992 |
| Microsoft Edge | $6,091 |
| Mozilla Firefox | $6,042 |
| Safari | $5,646 |
That is a swing of $346 for the crime of using the "wrong" browser. The gaps were rarely enormous, typically around 5%, but Kahn reports they were consistent. Switch to incognito mode, which starves the site of personal signals, and the prices reportedly snapped back to a single figure of $5,992. Start browsing a few other events in that same private session, though, and the variations crept back in. The behaviour was not universal either: Kahn says he found no equivalent pattern on Vivid Seats, Ticketmaster or SeatGeek during his testing, with the lone exception being Viagogo, which happens to be StubHub's corporate sister.
When the same ticket costs a different amount depending on your browser, the price tag has stopped describing the product and started describing the customer.
None of this is conceptually brand new. As Kahn points out, the most infamous case dates back to 2000, when Amazon was caught charging logged-in customers more for DVDs and beat a hasty retreat once the public found out. What has changed is the sophistication. Today's systems can nudge prices up or down per shopper, in real time, on inventory the platform does not even own.
What this actually means for event organisers
If you only sell business conference passes, you might be tempted to file this under "someone else's problem over in the concert world". Resist that urge. The technology that quietly varies a Bruno Mars resale price is the same technology that will, sooner or later, be pitched to anyone selling admission to anything, summits and trade shows included.
The real risk for organisers is not the handful of dollars in margin. It is trust. Event audiences are repeat buyers, and word travels at the speed of a Slack message in professional communities. A delegate who discovers that a colleague paid less for the identical pass will not blame the algorithm, they will blame your brand. Years of careful reputation-building can be undone by one screenshot of two mismatched receipts.
There is a practical headache too. Personalised pricing muddies your own numbers. If the headline price keeps shape-shifting per visitor, your conversion reports, your A/B tests and your demand forecasting all inherit the noise. Pricing you cannot explain is pricing you cannot optimise, and "the model did it" is a poor answer when finance asks why the average order value wobbled.
The regulators are reading the receipts too
Here is the part the original viewpoint did not linger on: lawmakers have very much noticed. In January 2025 the US Federal Trade Commission published the first findings of its surveillance pricing study, concluding that a wide range of personal data is already being used to set individualised prices, and it has kept the subject on its public watch list. Since then the dominoes have kept toppling. New York's Algorithmic Pricing Disclosure Act now requires sellers to label any price set by an algorithm using personal data. Maryland passed the first state ban on surveillance pricing in certain sectors in April 2026. California's Attorney General opened an investigative sweep in January 2026, and in March 2026 the House Oversight Committee began probing data-driven pricing specifically in travel and hospitality. Across the Atlantic, the EU's proposed Digital Fairness Act and the UK's Competition and Markets Authority are circling the same question.
Translation for organisers: even where personalised pricing works, the compliance and reputational bill attached to it is climbing fast. "It is technically possible" is rapidly parting ways with "it is a good idea".
Watch this space
Kahn's own conclusion is refreshingly free of hype. He notes that StubHub and Viagogo, despite sharing data and models, sometimes react to the same signals in opposite directions, which hints that the practice may be, for now, a technical capability in search of a use case. In plainer terms: the machine can do it, but nobody has yet proven it actually pays. That is precisely the window event organisers should use wisely. The platforms that win the next few years will be the ones that treat a clear, single, defensible price as a feature rather than a limitation.
At eventcloud we are firmly in the boring-on-purpose camp on this one: one flat fee, the same number for everyone, no detective work required from your attendees. You can see exactly how that flat-fee pricing works, because the only thing your browser should ever reveal about you is that you have excellent taste in ticketing platforms.
