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Target Audience: Small–mid D2C founders & operators.

Episode Promise: Turn your PDPs into high-converting, low-return sales reps using AI to mine reviews/support tickets for real objections, rebuild PDPs with objection-crushing modules, and track profit (not just clicks).

The Problem: Your PDP is your primary salesperson. Bad PDPs kill conversion and spike returns. Solution: "PDP Rehab" using AI to mine Voice of Customer data.

1. The Scoreboard (KPIs)
   • PDP → ATC Rate
   • Return Rate by PDP Variant
   • Refund Reasons & ATC → Checkout Drop-off Golden Rule: 2–4 week baseline before changes.
2. AI-Assisted Mining Build an "Objection Library" per SKU from 1–3 star reviews, 5 star reviews, support tickets, and return reasons. Prompt: "Cluster and Rank Objections by Frequency and Severity."
3. Mapping Objections to Modules
   • Fit/Size → Precision Block: Size badges, model specs, brand comparisons
   • Material/Quality → Sensory Block: Translate specs to feel, care instructions
   • Value/Price → Comparison Block: Good/better/best, cost per wear
4. Mobile-First PDP Template
   • Above Fold: Gallery, title, price, ratings, trust snippets, sticky ATC
   • Body: 3 benefit bullets, sizing/specs, materials
   • Validation: Shoppable reviews, UGC gallery
   • Cross-Sell: Bundle suggestions
5. Advanced Modules
   • Zero-Party Quiz: 3 questions to direct to correct variant
   • Q&A Search: Searchable past questions
6. Production Pipeline AI: FAQs, benefit bullets, localization Humans: Compliance, accuracy verification Performance: LCP under 2.5s
7. Testing Plan (30/60/90 Rollout)
   • Days 1–30: Mine data, build Library, baselines
   • Days 31–60: Rehab top 3 SKUs, A/B test
   • Days 61–90: Scale to top 20 SKUs Success Metric: Profit Per Visit (Conversion + AOV - Returns)