The Situation
- A growing technology startup selling a B2B product, priced off an early number the founders picked before product-market fit and never revisited.
- Deals were closing easily and almost no prospects pushed back on price, a classic sign of leaving money on the table.
- The team wanted to raise prices but feared churn and stalled growth, and had no way to size the risk.
- They needed a model, not a gut call, before touching the pricing page.
What We Built
- A pricing model built on the company's real numbers: current price, customers, gross margin, monthly churn, and CAC.
- A price-elasticity layer estimating how many customers a given increase would cost, tested across a range of assumptions.
- Outputs showing MRR, gross profit, LTV:CAC, and payback at price changes from a cut to a large increase, with the profit-maximizing price highlighted.
- A break-even cushion for every price point: how many customers the company could lose and still hold current revenue.
The Outcome
- The model showed the company was materially underpriced and more inelastic than the founders had feared.
- It quantified a safe increase, one where the break-even cushion far exceeded the churn the elasticity assumption predicted.
- The team shipped a price increase to new customers first, grandfathering existing accounts, and watched win rates hold.
- Higher ARR per customer flowed almost entirely to gross profit, with no new headcount or pipeline required.
Why It Worked
- Pricing is the highest-leverage lever in the model because it has almost no marginal cost, yet it is the least-tested.
- Modeling the change first, including the break-even cushion, turns a scary decision into a measured one.
- Rolling the increase out to new customers first proved the market accepted the price before touching a single existing relationship.
