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Decision PlaybookFramework

Pricing Playbook

Which price should we test?

Explore pricing scenarios before committing to a market position.

Playbook diagram: price positions, segment willingness, the story each implies, responses including rivals, test narrowly.Playbook diagram: price positions, segment willingness, the story each implies, responses including rivals, test narrowly.

Why this decision goes wrong

Pricing decisions rarely fail on arithmetic. They fail because a number was chosen without deciding which customers it invites, which it declines, and what claim has to be true for it to feel fair.

The five moves
  1. 01Turn the price into positionsBuild two or three complete price positions — number, packaging, claim, audience — instead of a single candidate number.
  2. 02Segment willingness, don’t average itTreat willingness to pay as a spread. State explicitly which part of it each position serves and which part it gives up.
  3. 03Write the story each price impliesWhat does the buyer have to believe? What comparison does the price invite? What has to be visibly better than the cheaper option?
  4. 04Explore responses, including competitorsExamine how segments and rivals could respond to each position — not only whether customers accept the number.
  5. 05Test the most consequential position narrowlyOne segment, one market, one channel. Reprice everything only after the narrow test.
What you bring
  • Current price and packaging
  • Candidate price positions
  • Segment definitions and alternatives
  • Competitor set and their claims
  • Margin and channel constraints
What you get out
  • Comparison of price positions
  • Which segments each position invites or loses
  • Possible competitor responses
  • The narrow test to run first
Common failure modes
  • Copying a competitor’s number and inheriting their constraints
  • Averaging willingness to pay across segments
  • Changing price without changing the story
  • Assuming rivals hold still
Running it in ATLASIO.ai

Set the decision up as a scenario, add the evidence you hold, and explore how simulated customers and market actors could respond to each alternative. The output is decision support — possible outcomes and the assumptions behind them, not a guarantee.

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