A GG design with six price points around the intended premium completed concept screening and pricing in one study — 20 RMB emerged as the optimum, at peak cumulative purchase intent.
✦What price — or price rise — optimizes the business?
Pricing Compass ezPrice Test
Get price wrong and everything downstream is wasted. Four rigorous models match four pricing scenarios, consumers "actually buy" inside a simulated store, and elasticity plus cannibalization come out in one read.
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Why the old way fails
Traditional price tests process by hand and report after the market has moved; questionnaire settings sit far from real purchase moments; and one-size methods can't match different pricing scenarios.
The path
How the chain runs
- 01
Four models for four scenarios: PSM (no preset points), GG (preset points), extended GG (repricing vs competitors), CBC (multi-product, multi-competitor moves).
- 02
Simulated commerce: choice tasks, PK, CBC and MaxDiff all run inside replica Taobao/JD interfaces, as close to the real decision as testing gets.
- 03
Model-matched design: 5-6 price points for GG, 5-7 per product for CBC, 8-12 tasks per respondent.
- 04
Proprietary automated processing plus a live dashboard — results output in one click when fieldwork ends.
Key numbers
4种models: PSM, GG, extended GG, CBC
5-7个price points per product in CBC designs
≥200minimum recommended PSM sample
1键click to output results from the live dashboard
Field cases (anonymized)
A CBC across 3 own products × 10 competitors quantified how cost-driven price rises would shift share of choice — including cannibalization inside and outside the portfolio.
What you get
Optimal-price recommendation with elasticity curves, demand impact and cannibalization analysis — one click from the live dashboard.
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