Three engagements,
told in full.

A B2B SaaS company came to us with flat revenue and a pricing page nobody had touched in years. Discounts were negotiated case by case, on instinct. Monthly revenue hovered around $420K and had for a long time.

The diagnostic took three working sessions. Billing and usage data existed but had never been joined, so nobody could say which customers were underpriced relative to the value they drew from the product. When we joined the two, the mismatch was obvious. Some of the heaviest users were on the oldest, cheapest plans.

We built an A/B testing framework so pricing changes could be tested on real cohorts instead of argued about in meetings. A few months in, a pricing engine went live: plan recommendations driven by usage, not by whoever negotiated hardest. The client's team ran every experiment themselves by the end. That was the point.

Monthly revenue reached $755K within the year. The analysis put the annual upside at $2M, and the framework keeps testing after we left.

A SaaS company kept losing accounts with no warning. Their process was an NPS survey and an exit interview, both of which happen after the decision to leave is already made. The customer success team was doing forensics, not prevention.

We profiled more than 500 accounts, active and churned, against their product usage history. The pattern was hard to miss once it was on a chart: 73% of churned customers showed declining engagement a full 90 days before they cancelled. The warning had been sitting in the data all along. Nobody was looking at it.

The fix was not a bigger dashboard. We turned the engagement signals into an early-warning score and put it where the customer success team already worked, as a ranked list of accounts to call this week. No new tool to learn, no report to remember to open.

Retention work now starts a quarter before a cancellation instead of after it. The team stopped guessing which accounts were at risk because the data tells them.

A B2B SaaS platform operating in three markets had no data people at all. Product decisions were made on opinion and seniority. Leadership knew it, disliked it, and had no idea whether the first hire should be an analyst, an engineer, or a manager.

We started with the questions the business actually needed answered, then designed the team backwards from there. We wrote the job specs, sat in the interviews, and set up the stack the first hire would walk into, so week one was productive instead of archaeological.

As the team grew we moved from building to coaching: reviewing work, setting the roadmap rhythm, and preparing the team lead to present to the board without us in the room. Our goal in every team engagement is the same, an organization that stops needing us.

Six months in, a four-person analytics team was shipping weekly insights to product leads in all three markets. It still does, without us.