Retail and e-commerce scenarios
Retail and e-commerce scenarios — product Q&A, recommendations, pricing, customer service, returns/fraud. Industry patterns, not customer case studies.
Five canonical scenarios where retailers and marketplaces use Stratix evaluation to gate AI features into production.
1. Product discovery and Q&A
Setup: Customer asks "Will this jacket fit a 6'2" 220lb frame?" "Is this dishwasher compatible with my hardwired connection?" AI synthesizes the product spec sheet, prior reviews, manufacturer Q&A; produces an answer with source citations.
Why it's hard:
Product attributes may be missing or inconsistent across the catalog
Reviews can contradict spec sheets
Customers act on the answer — wrong fit info drives returns; wrong compatibility drives serious damage
Stakes:
Returns directly hit margin; some categories see 30%+ return rates
Compatibility errors on regulated/installed products (appliances, automotive parts) can trigger product-liability exposure
FTC and state AGs flag misleading product claims even when AI-generated
Stratix evaluation criteria:
Spec grounding
Output cites the spec sheet
Citation existence
Review-spec conflict
Output flags conflicts vs. picking arbitrarily
Code assertion
Compatibility hard rule
High-risk categories require explicit compatibility-check disclaimer
Hard rule
Hallucination rate
Confident wrong claims about specs
Faithfulness judge
Reading level
Comparable to category baseline
Code scorer
2. Personalized recommendations
Setup: Recommendation engine surfaces products. AI-augmented version uses LLM to expand "similar products," generate copy, or rerank.
Why it's hard:
Stale catalog — recommendation surfaces a discontinued SKU
Sensitive-category guardrails — no recommending alcohol/firearms/adult on a kid's account
Personalization fairness — same product should not vary in price/availability based on protected-class proxies
Stakes:
Bait-and-switch perception when out-of-stock items dominate recommendations
FTC scrutiny on dark patterns and personalized pricing
State AG actions on sensitive categories (alcohol, tobacco, firearms) reaching wrong audience
Stratix evaluation criteria:
Catalog freshness rule — Hard rule: every recommended SKU must be in-stock and live in the catalog as of trace time
Sensitive-category gate — Hard rule by account / age band
Personalization fairness — Group fairness on availability and surfaced price
Diversity scorer — Recommendation-set diversity to avoid filter-bubble effects
Conversion-correlation watch — Soft drift signal; recommend regenerating evals if conversion drops
3. Customer service chatbot
Setup: Order status, returns, exchanges, escalation. Order data + policy data + customer history.
Why it's hard:
Authoritative policy must be reflected (return windows, restocking fees)
Refund-promise accuracy — bot promises a refund the policy doesn't allow
Multi-channel consistency — same answer in chat, email, voice
Stakes:
Refund-promise mismatch → regulatory and reputational exposure
CCPA / GDPR data subject requests must route correctly
FTC focuses heavily on dark-pattern returns/cancellation flows
Stratix evaluation criteria:
Policy citation rule — Every refund/return decision cites the relevant policy section
Refund-promise gate — Hard rule: bot cannot promise refunds outside policy parameters
DSR routing — Privacy requests recognized and routed to the right intake
Tone and de-escalation — Judge for customer-experience quality
Channel parity — Same query produces same answer across chat/email/voice
4. Dynamic pricing and promotion
Setup: AI surfaces pricing or promotion suggestions. Human merchandiser approves; AI does not autonomously change prices.
Why it's hard:
Personalized-pricing legal exposure — if it correlates with protected class, it's discriminatory
Bait pricing — promotion that's never actually realized
Cross-region price-parity expectations
Stakes:
FTC and state AG actions on personalized pricing have multiplied in 2025-2026
Class-action exposure under state UDAP statutes
Marketplace platform-policy violations carry delisting risk
Stratix evaluation criteria:
Personalized-price fairness — Group fairness on offered price across protected proxies
Realizability rule — Hard rule: any "promotion" must be actually achievable, not bait
Cross-region parity — Code scorer flagging unjustified regional spread
Audit trail — Every price suggestion logged with inputs, model version, approver
5. Returns and fraud detection
Setup: AI scores returns for fraud markers (wardrobing, empty-box returns, serial returners, organized retail crime patterns). Outputs go to fraud team for action.
Why it's hard:
False positives wrongly accuse legitimate customers and drive churn
Disparate impact on legitimate but high-return categories (apparel, gifts)
Privacy boundaries on what the score can use as features
Stakes:
Retail loss to returns fraud reaches multi-billions annually across the industry
Wrongful denials drive class-action exposure
State consumer-protection AGs investigate algorithmic denial of returns
Stratix evaluation criteria:
Top-decile precision — Tracked against confirmed-fraud labels
Fairness scorer — Group-level denial rate parity
Reason-code coverage — Hard rule: every flagged return cites contributing factors
Customer-explanation rule — Hard rule: customer-facing denial reason is plain-language
Override rate watch — Drift signal if humans override AI more than threshold
See also
Last updated
Was this helpful?