I was brought in after an operator saw their revenue slide following a 5% price increase across all locations. Their leadership team was stuck on a question they couldn't answer: Did pricing do this, or was it something else?
They faced a gut-wrenching decision: Roll back the increase and potentially leave millions on the table, or hold firm and risk watching members walk out the door. Tens of millions of dollars hanging on a decision, with no real evidence to guide it.
This situation is far more common than most operators like to admit. Most meaningful changes — pricing, perks, staffing models, loyalty — eventually lead to the same debate. Someone pulls a before-and-after chart. Someone else points to seasonality. Another cites competitor moves, macro conditions, or local noise. The discussion is thoughtful and well-intentioned — and still goes nowhere.
Before-and-after comparisons are seductive because they’re easy. But they’re also deeply misleading. Too many things change at once: demand cycles, promotions, weather, competitive intensity, even consumer sentiment. The signal you’re looking for is buried under variables you didn’t control for.
Some operators try partial rollouts to reduce risk. That’s a step in the right direction — but only if the test locations actually resemble the rest of the business. Too often they don’t. The result is a test you don't trust, followed by another round of debate.
Even when I help teams run rigorous experiments during an engagement, there’s a familiar problem at the end: the capability doesn’t stick. Sustaining real experimentation usually requires data science resources most operators don’t have — and don’t want to hire. When the engagement ends, the uncertainty comes back.
That’s the gap I built ProofPod to fill. ProofPod builds a control group from locations that have tracked your test locations historically, so when they diverge after a change, you know the change caused it. You define what you want to test: a price change, a new perk, a referral incentive. Proofpod runs the experiment and gives a clear recommendation in plain language: scale it, tweak it, or kill it. No dashboards to interpret, no statistics to argue over.
For Perkville customers, this means finally resolving questions that often linger unresolved for months. Does increasing a referral bonus actually drive incremental growth? Which perks meaningfully improve retention — merchandise credits, priority booking, or experiential rewards? You’re already investing in loyalty. Now you can find even more value in the programs you're already running.
Because ProofPod integrates directly with Perkville, there's no heavy setup or data plumbing. You can design your first test the day you sign up.
What used to require a data science team or a boutique consulting engagement is now accessible to any multi-location operator. Real experiments, clear answers. Fewer debates, and better decisions.
If this sounds like a problem you've lived, I'd love to hear from you. Reach out at ryan@proofpod.ai or visit proofpod.ai.