Don't debate the price rise in a meeting. Test it in the market, safely.
Don't debate the price rise in a meeting; test it in the market. The planner walks you from hypothesis to control group to stop-rules, and assembles the one-page plan your team can execute.
A 8% price increase on SKU-X loses less than 15% volume, raising contribution
Contribution per week
2 comparable territories keep current price
Stop if volume falls >15% for 2 consecutive weeks
4 weeks
Not defined.
Readout: compare metric per week, test vs control. Launch if the hypothesis holds AND guardrails stayed intact; iterate if mixed; reverse if guardrails broke. Sample sizes decide confidence — for meaningful volume tests, check with the sample-size calculator.
A valid pricing test needs: a falsifiable hypothesis, one success metric, a control or baseline, guardrails that stop the test, a duration, and a pre-agreed decision rule.
Interference warnings are built in: customers talk, channels compare, and festive weeks corrupt both legs. B2B needs account-level splits; D2C needs cohort or geo splits.
Statistical confidence depends on volume — for small samples, the planner points you to the sample-size calculator before you trust the readout.
Illustrative figures, not a client outcome.
Long enough to cover at least two full purchase cycles of your typical customer — four weeks is a floor for weekly-purchase categories, not a default.
A failed test is a successful experiment: you learned the price sensitivity for the cost of a small cohort instead of the whole market.
Version 1.0 · Reviewed 2026-10 · Methodology is fixed and version-controlled; results depend on your inputs.
Design the test. Run it with someone who's done it before.
BLEZZINGS runs this analysis inside a focused engagement — with your data, your market and a plan to act on it.