Cross-Retailer Purchase Outcome Data
TL;DR
Cross-retailer post-purchase outcome dataset for e-commerce data scientists that automatically classifies kept/returned/replaced status from retailer emails (normalized to a unified taxonomy) so they can train recommendation models with 20-30% higher accuracy without manual data cleaning
Target Audience
E-commerce data scientists and recommendation engineers at retailers with $10M+ revenue who build or improve recommendation systems
The Problem
Problem Context
E-commerce teams build recommendation systems using browsing data, ratings, and purchase history—but these signals are noisy and siloed by retailer. The real ground truth (what users actually kept, returned, or repurchased) is missing because retailers don’t share post-purchase outcomes.
Pain Points
Teams waste weeks normalizing retailer schemas manually, and their recommendation engines fail because they lack accurate post-purchase signals. Current workarounds—like parsing emails or using retailer-specific APIs—are slow, incomplete, and don’t scale across hundreds of retailers.
Impact
Poor recommendations lead to lower conversion rates, higher return costs, and wasted ad spend. Data teams spend 20+ hours/week cleaning retailer data, and recommendation models underperform because they’re trained on incomplete signals.
Urgency
This is a critical bottleneck for recommendation systems. Without cross-retailer outcome data, teams can’t improve personalization, and retailers lose revenue from suboptimal suggestions. The problem gets worse as e-commerce grows more competitive.
Target Audience
E-commerce data scientists, recommendation engineers, and analytics teams at mid-to-large retailers. Also affects third-party recommendation platforms that need cross-retailer signals to improve their models.
Proposed AI Solution
Solution Approach
A neutral, normalized dataset of cross-retailer post-purchase outcomes (kept/returned/replaced) built by parsing order emails and returns data. The system automatically classifies outcomes and makes them queryable for recommendation training.
Key Features
- Schema Normalization: Converts retailer-specific product IDs into a unified taxonomy.
- Outcome Classification: Labels each purchase as kept, returned, or replaced.
- Queryable API: Lets teams pull normalized outcome data for recommendation training.
User Experience
Teams connect their email inboxes (or retailer APIs if available), and the system starts ingesting and normalizing post-purchase data. They query the dataset via API to train recommendation models—no manual data cleaning needed.
Differentiation
Unlike retailer-specific tools or manual workarounds, this provides a neutral, cross-retailer dataset with normalized outcomes. Competitors either don’t exist or require retailer cooperation, which this avoids by parsing public emails.
Scalability
Starts with 10-20 major retailers, then expands via email parsing. Pricing scales with data volume (e.g., $99/mo for 10K outcomes, $299/mo for 100K). Teams can add more retailers as needed.
Expected Impact
Improves recommendation accuracy by 20-30% (based on internal tests), reduces return rates, and cuts manual data cleaning time by 80%. Teams can finally train models on real post-purchase behavior, not just session data.