── ── Cognitive bias
Pricing Strategy
Price is a structural decision, not a calculated number. Cost-plus and competitor-matching both ignore 50 years of pricing research: what people pay is shaped by reference points, anchoring, loss aversion, and offer structure — not by cost. Kahneman & Tversky (Econometrica 1979): losses hit ~2× harder than equal gains. Thaler (1980): endowment effect and mental accounting drive consumer pricing behavior.
Run Pricing Strategy on a real problem
Bring something you're actually deciding — free, in the browser.
How it works
Run the Pricing Audit. Value-first, anchor, structure, frame, test.
1. Articulate value to the customer in customer units. Not "our software does X" but "saves them $50K/year in engineering-hours." If you cannot name the value in the customer's metric, pricing is guesswork. 2. Estimate WTP per segment. Use Van Westendorp Price Sensitivity Meter (4-question survey), conjoint analysis, or direct evidence from paid pilots (B2B — most reliable). 3. Choose an anchor. First price seen frames every subsequent price. Anchor high if defensibly justified; the high anchor lifts the entire tier structure. 4. Design the tier structure. 3 tiers (Starter / Pro / Enterprise): Starter makes Pro feel affordable; Enterprise anchors and captures top WTP; Pro is the target sweet spot. 5. Frame for loss aversion. "Save $X by paying annually" beats "monthly costs $Y more" ~30%. Money-back guarantee converts ~10–30% higher than free trial. Per-user vs. flat-fee: per-user for SMB, flat-fee captures more enterprise value. 6. Stress-test the anchor. Show prices to 5–10 target-segment buyers: "expensive but worth it?" / "too expensive?" / "I'd buy at this price." Adjust if segment reference point is materially below anchor. 7. Run a 60-day measurement. Track: conversion rate, upgrade rate (Starter → Pro), discount-request frequency, revenue per tier. High upgrade rate + low discount-request frequency = right structure.
When to use it
- user says 'how should we price this', 'we should just charge more', 'our competitors charge X', 'willingness to pay', 'anchor price', 'value-based pricing', 'freemium structure', setting a first price for a new product, considering a price increase and worried about churn, or needing to design tiered/usage-based pricing
When not to use it
When the decision is routine and reversible, applying a formal method costs more than it returns.
Worked example
Pricing an AI Product Under Volatile Inference Costs (2023–2026)
Where the De Beers case shows the framework executed deliberately and Qwikster shows it failing all at once, this case shows the framework applied to a moving cost base — the defining pricing problem of the 2023–2026 generative-AI wave. The distinctive feature: the input cost (model inference) is not fixed, and it moves in both directions with each model release — capability jumps up while per-token prices fall — so a price set against today's cost can be stranded within a quarter.
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
Related mental models
Most reasoning is binary: will it happen, or won't it?
The Red Queen Effect: competitors must continuously improve just to maintain relative position — because everyone else is improving simultaneously.
The representativeness heuristic is judging probability by how closely something resembles a prototype — overriding actual base rates.
Self-renewal is the structured practice of identifying which mental models have passed their expiry date and genuinely updating — not merely annotating — them.