Booko Uses Dynamic Pricing to Fill the Time Slots Nobody Wants

The Macro: Unsold Time Disappears Forever

There is a fundamental economic reality that every appointment-based business faces: unsold time slots have zero residual value. A hotel can discount an unsold room at the last minute and still make money. An airline can sell a cheap seat and at least cover marginal cost. But when a 2pm Tuesday slot at a fitness studio goes unbooked, that capacity is gone. You cannot sell it tomorrow. It simply vanishes.

Despite this, the overwhelming majority of businesses that sell bookable time still use static pricing. Your spin class costs $30 whether it is a packed Saturday morning or an empty Tuesday afternoon. Your medspa charges the same for a facial at 10am (when everyone wants one) as at 3pm on a Wednesday (when nobody does). Your tax accountant bills the same hourly rate during busy season and the slow months when the calendar is half empty.

Dynamic pricing is standard in airlines, hotels, and ride-sharing. Uber proved that consumers will accept variable pricing if the value exchange is clear. But it has barely penetrated appointment-based services, for a simple reason: the tools did not exist. Most booking platforms (Mindbody, Acuity, Calendly, Square Appointments) offer coupons and promotions, but they do not dynamically adjust prices based on predicted demand.

Booko, backed by Y Combinator (W25), is building exactly this. An AI layer that sits on top of existing booking systems, predicts which slots will not fill, and automatically adjusts pricing to maximize utilization and revenue.

The Micro: Predict, Price, Profit

Will Hall and Arjun Saluja founded Booko out of Dartmouth. Hall studied economics and computer science. Saluja comes from an engineering background with previous experience on dynamic pricing systems at Joby Aviation, working with the Uber Elevate team. That last detail matters. They have actually built dynamic pricing before, just for a different domain.

The product integrates with existing booking platforms like Mindbody and Mariana Tek, which means businesses do not have to rip out their current system. This is critical. Asking a fitness studio owner to switch booking platforms is a non-starter. Integrating with the platform they already use and adding intelligence on top is a much easier sell.

The core loop is straightforward. Booko analyzes historical booking data and current availability. It predicts which slots are likely to go unfilled. It then automatically offers targeted discounts for those specific slots. The discount is calibrated to fill the slot without giving away more margin than necessary. A 15% discount on a Tuesday afternoon class that would otherwise go empty is pure upside.

The claim is that early customers see roughly 20% revenue uplift within the first two weeks, with measurable results showing up fast. That is a bold claim, but the math makes sense if a business has significant unused capacity. Even a modest improvement in utilization at slightly lower per-slot prices can meaningfully increase total revenue.

The target verticals include fitness studios, medspas, tax accountants, consultants, hair salons, event venues, and tutoring services. This is a long list, and each vertical has different booking patterns, price sensitivity, and customer behavior. I would expect Booko to nail one or two of these verticals first and expand from there rather than trying to serve all of them equally from day one.

SOC 2 and HIPAA compliance is mentioned, which suggests they are serious about the medspa and healthcare-adjacent verticals where data handling matters.

The Verdict

Dynamic pricing for appointment-based businesses is one of those ideas that is obviously correct but has not been well executed yet. The demand is there. The technology is mature enough. The integration path through existing booking platforms is smart.

At 30 days: which vertical is performing best? I would bet on fitness studios, where the booking patterns are predictable and the price sensitivity is well understood. Medspas could be interesting too, given the higher average ticket size.

At 60 days: what is the average revenue uplift across all customers after the initial spike normalizes? The first two weeks might show dramatic improvement as pent-up empty capacity gets filled. The question is whether that holds at month two and month three.

At 90 days: are businesses keeping the system on or turning it off? Dynamic pricing requires trust. If a studio owner sees that their 6am class is being discounted and worries it devalues their brand, they might revert to static pricing even if the revenue numbers are better.

I think Booko is building the right product for the right market. The competitive moat will be in the quality of the demand prediction models, which improve with more data, which improves with more customers. That flywheel, if it spins up, could make this very hard to compete with over time.