Menu Creep, and What We Did About It

By Luke Bujarski  ·  April 2026  ·  4 min read

It rarely happens all at once. A device rep comes in with a compelling ROI story and a limited-time offer. A nearby competitor starts promoting a new treatment and patients start asking about it. A slow quarter creates pressure to open a new revenue line. Someone on staff gets certified in something adjacent. Each decision, made individually, seems reasonable. Nobody sits down and decides to build a 24-service menu. It accumulates.

That is exactly what happened at Chrystal Clinic. Over several years of operating, our service list grew well past what the team could deliver with consistent quality and well past what any patient could easily navigate. And somewhere in the middle of that expansion, the economics of the clinic got harder to read. We didn't have a name for what was happening. We just knew the schedule was full and the margin picture kept being harder to explain.

What the menu was hiding

When we built the economic model and ran the service analysis, the picture that came back was clarifying in the way that uncomfortable findings tend to be. A new service creates a new line in a revenue report. That line is visible, trackable, and easy to point to in a team meeting. What doesn't appear in that same report is the margin picture underneath it: whether the service is profitable at the provider hour level, whether it is pulling time away from higher-margin work, and whether the patients it attracts are actually staying.

Total revenue by service is what we had been tracking. It told us what was popular. Revenue per provider hour by service told us what was profitable. Those two lists were not the same, and we had only ever looked at the first one.

A handful of services were carrying the economics of the whole menu. The rest were being quietly subsidized by the work that actually performed. The subsidy was invisible in our aggregate revenue figures. It only became visible when we separated out what each service produced per hour of provider time committed to delivering it.

The capacity problem we hadn't named

Adding a service does not add hours. It redistributes the hours that already exist. Provider time is the fixed resource in any clinic, and every service on the menu competes for a share of it.

What had happened at Chrystal Clinic was that lower-margin services were filling available slots because they were easier to book, faster to deliver, or more actively promoted. The schedule looked full. Utilization looked strong. But the revenue per hour being generated across that full schedule had quietly declined because the mix had shifted toward work that produced less per unit of provider time.

A clinic can be genuinely busy and genuinely under-earning at the same time. We lived that for longer than I'd like to admit before the model gave us language for it.

New services hadn't fixed retention either

There had been an assumption embedded in our expansion logic: that new services would bring in patients who stayed. Sometimes that was true. More often, a trending service attracted patients whose primary interest was that specific treatment, and whose likelihood of becoming a loyal, multi-visit patient was lower than the patients our core services were already retaining.

The visit-one-to-visit-two conversion problem in our core business didn't improve because we added something new to the menu. In some cases it got harder to see, because new services were generating first visits that made acquisition numbers look healthy while the underlying retention rate stayed flat.

Retention is an economic problem, not a menu problem. Adding services is a supply-side move. Retention lives in the behavior of the patients already in the system, and no new service line touches that directly.

What we actually did

Once the service economics were visible, the data made a clear case for simplification. The bottom thirty percent of our service variations, measured by revenue per provider hour, were not carrying their weight. Some were running at margins that made them actively dilutive to the overall economics. The analysis was not ambiguous.

Acting on it was harder than reading it. Some of those services had champions on staff who had built their practice around them. There were patients with strong preferences. Equipment had been purchased with the expectation of utilization. The switching costs were real, and the conversations required to make the changes were not comfortable ones.

We cut roughly thirty percent of our service variations anyway. The result was both operational and economic. Provider hours that had been scattered across a long menu concentrated toward the services that were actually performing. The patient conversation simplified. Booking became more straightforward. And the margin picture, which had been getting harder to explain for years, started making sense again.

The simplification felt like a contraction from the inside. From the outside, and in the numbers, it was a growth move.

The question to take back to your data

If you removed the three lowest-performing services on your menu by revenue per provider hour, what would that free up, and where would those hours go?

Most founders can't answer that from their current reporting. The number doesn't exist in any dashboard they're looking at. That gap, between what the reports show and what the economics actually are, is usually where the real growth conversation starts.

Luke Bujarski is the founder of LUFT and co-founder of Chrystal Clinic. LUFT builds economic models for cash-pay health clinics. luft.net