Cut Prices, Fill Seats, Double Your Valuation

01 /The Concept

Australian startup EatClub just closed a $27 million funding round, pushing its valuation to $200 million – at least double what it was less than a year ago. The previous round came in May, when EatClub entered the UK market and was valued somewhere between $75 and $100 million.

The business is straightforward: EatClub gets users discounts of up to 50% at restaurants, cafés, and bars.

It's a proposition that's resonated with both diners and venues. The platform has 2 million users and more than 4,000 restaurant, bar, and café partners.

The discounts are powered by a dynamic pricing engine embedded in restaurant operations. Crucially, prices never go up during busy periods – they only go down when a venue has spare capacity.

The founder draws an analogy to hotels, which charge premium rates in peak season and drop prices in the off-season. Restaurants face the same demand swings but at a much faster cadence. Many venues earn 80–90% of their monthly revenue on weekends, and even within a single day, occupancy and revenue fluctuate sharply.

Small price reductions, it turns out, reliably pull in more customers – as long as they find out in time. That's the job of the EatClub app: show nearby venues that are currently running a live discount.

Redeeming a discount is frictionless. Users pay with a digital EatClub card that lives in their phone wallet, activated on first use. Tap the card at the terminal and the current discount applies automatically – whether dining in or ordering to go.

The platform manages discount logic entirely on its back end, which means restaurants don't need to manually adjust their POS system each time a promotion starts or ends. The prices appear, shift, and disappear automatically.

Restaurants join because it demonstrably grows revenue they wouldn't otherwise capture. According to EatClub, dynamic pricing can generate up to $10,000 in incremental monthly revenue per venue.

02 /Why It Matters

A few weeks ago, a [related review](/review/zarabatyvaj-kak-uber) covered Booko, a Y Combinator startup building a dynamic pricing platform aimed at a different audience: the long tail of appointment-based service businesses – accountants, tax consultants, fitness studios, tutors, and similar operators.

Booko goes beyond simply letting businesses adjust their prices. A built-in AI engine analyzes booking patterns, predicts slow periods, and recommends exactly how much to discount to maximize revenue rather than just fill slots.

Dynamic pricing also has serious enterprise-grade implementations. Fetcherr ([covered previously](/review/s-gibkimi-cenami-bolshe-zarabotaesh)) built a dedicated dynamic pricing system for airlines and has raised $156.5 million – including $42 million after that review was published.

The most familiar consumer example is ride-hailing surge pricing, pioneered by Uber. Unlike the restaurant model, Uber raises prices during high demand rather than lowering them during low demand – a distinction that, analysts argue, was a key driver of Uber's first profitable year in 2023.

Since 2024, other consumer-facing businesses have begun experimenting with surge-style pricing, including fast-food chains – and the consumer backlash has been significant.

EatClub and Booko have gone the other direction. They advocate reducing prices during slow periods, which may not maximize revenue as aggressively as surge pricing, but has the considerable advantage of making customers happy rather than resentful.

03 /Opportunities

Pricing is one of the most consequential levers in any competitive market. But the right price is rarely a static number – it depends on seasonal demand patterns, day-of-week and time-of-day swings, competitor pricing, advertising activity, weather, a blogger mentioning the place and sending a sudden rush of traffic, and a dozen other variables.

This means operators need to monitor their own load and prices continuously, adjust quickly, and do so in a way that maximizes profit – not just revenue. That requires predicting how specific price changes will affect actual transaction volumes, then modeling the resulting profit impact.

Doing that manually and consistently is essentially impossible, which is why the opportunity belongs to platforms trained on specific markets and industries – the closer the fit between the model and the context, the more accurate the forecasts.

As competition intensifies across virtually every consumer and B2B market, intelligent dynamic pricing platforms may finally see the broad adoption that has so far been limited to a handful of industries. Y Combinator's decision to back Booko suggests the thesis is gaining institutional recognition.

So: which slice of the business world would you build a dynamic pricing platform for – one that has never had access to this kind of tool before?