Plan, design, and implement A/B tests and experiments with statistical rigor. Covers hypothesis frameworks, sample sizing, metrics, and result analysis.
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Design a promotions and discount engine — coupon codes, automatic discounts, BOGO, tiered/volume pricing, stacking and exclusivity rules, and gift cards. Platform-agnostic rules modeling that avoids margin leaks. Triggers: designing discounts, defining stacking rules, modeling gift cards/coupons.
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Discounts are easy to add and hard to control. Model the rules so promotions stack predictably, can't be abused, and never quietly destroy margin — independent of platform.
Gather: promotion types in use, who can stack with what, abuse history (code sharing, repeat redemption), and goal (design the engine, define stacking/exclusivity rules, or audit existing promos for leaks).
| Mode | Trigger | Output |
|---|---|---|
| Design | design | Discount engine model + rule schema |
| Rules | rules | Stacking, exclusivity, and eligibility matrix |
| Audit | audit | Margin-leak and abuse-risk review |
Model every promotion as trigger → condition → effect → constraints:
| Part | Examples |
|---|---|
| Trigger | code entered, automatic, cart threshold |
| Condition | min subtotal, specific collection, customer segment, first order |
| Effect | % off, fixed off, free shipping, BOGO, fixed price |
| Constraints | usage cap (global/per-customer), date window, exclusivity |
Rule: Discounts apply to a defined scope (line / shipping / order) and are recomputed server-side at checkout — never client-trusted.
The hard part. Define explicitly:
For the full eligibility matrix and worked stacking examples, model the matrix per the table above.
| Request | Output |
|---|---|
| Design | Rule schema + effect/scope model |
| Rules | Stacking/exclusivity matrix + application order |
| Audit | Leak/abuse findings: PASS / CONCERNS / FAIL |
A promotion engine is COMPLIANT when discounts are server-computed, stacking and application order are explicit, usage caps are enforced, and a margin floor protects every order.
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Plan, design, and implement A/B tests and experiments with statistical rigor. Covers hypothesis frameworks, sample sizing, metrics, and result analysis.
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