Data Analyst for DTC & Ecom Brands | Solving Churn, Profit Leaks & Growth Bottlenecks | SQL, Python, Power BI, Tableau, Looker Studio, Excel
Case Study: How Warby Parker Used Data to Reduce Returns Warby Parker is a U.S. eyewear company known for affordable glasses with a direct-to-consumer model. Their problem? • High returns. • Unsold stock. • Mismatch between what customers wanted and what was being produced. Here’s how they fixed it: They began by collecting customer behavior data like browsing patterns, abandoned carts, past purchases, style preferences and even social media mentions. All this data was centralized into a warehouse and connected to BI dashboards for the team. Machine learning models were then used to cluster customers into groups and spot which styles and colors were trending. Using these insights, they could forecast which stock keeping units (SKUs) were most likely to sell before committing to large-scale production. Finally, they tested small product batches first and only scaled up the designs that performed well. Results: • Stockouts decreased by 30% • Overstock was reduced by 25% • Returns dropped significantly For founders: You can start small by tracking what people look at but don’t buy. Even with Google Analytics and Excel, you can find hidden demand signals that guide smarter inventory decisions. #DataAnalytics #DTC #Ecommerce #RetailInnovation #ProductStrategy #Founders #StartupGrowth #BusinessInsights
