How to Evaluate a Pricing Change
Pricing decisions get made on anxiety more often than evidence — fear of losing customers on one side, fear of leaving money on the table on the other.
The situation
A business is considering a pricing change — a price increase, a new tier, or a shift in pricing model (usage-based, seat-based, flat). The upside is clearer revenue per customer; the downside is unclear until customers actually react.
Why it's hard
Pricing sits at the intersection of psychology and math, and the math is often invisible without real customer data. A price increase that loses 5% of customers but raises average revenue per remaining customer by 20% is a clear win — but most teams don't know their actual price elasticity, so the decision defaults to gut feel or competitor benchmarking that may not reflect the business's own customer base.
Factors that matter
Price sensitivity
How elastic is demand for this specific customer base — is there evidence, or just assumption?
Competitive positioning
Does the new price still make sense relative to substitutes customers could switch to?
Existing customer impact
Will the change apply to current customers or only new ones, and what's the churn risk either way?
Revenue math
What churn rate would make the change net-negative, and how likely is that scenario?
A decision framework
- Calculate the churn threshold. Work out exactly what percentage of customers would need to leave for the change to be a net loss — this number is often more reassuring than it feels.
- Segment the impact. A flat price increase affects price-sensitive and price-insensitive customers very differently; consider whether a tiered approach reduces risk.
- Test before committing fully where possible — new customers only, a subset of the market, or a smaller increase first.
- Communicate the change clearly — pricing changes handled transparently tend to retain more customers than ones that feel sprung on people.
Proceed with a grandfathered increase — 63% confidence
The calculated churn threshold is well above historical churn rates for comparable changes, and applying the increase only to new customers limits downside on the existing base. Confidence is moderate given no direct price-sensitivity testing has been run yet.
How Ascendra approaches this
Ascendra separates what's known about customer behavior from what's assumed, calculates the churn threshold the decision actually depends on, and models the scenarios where the change succeeds or backfires — so pricing isn't decided on anxiety in either direction.