In today's competitive e-commerce landscape, turning one-time buyers into loyal, repeat customers is the holy grail. But for many businesses, understanding what truly drives that loyalty remains a mystery. What if there was an AI that could connect all your data points – sales, marketing, inventory, and customer behavior – and hand you a clear blueprint for retention?
Meet Walt, your AI right-hand for commerce.
Walt was recently brought in by a large men's wholesale shoe company facing a significant challenge with their new direct-to-consumer (DTC) business: a staggering 90% of their customers were not returning1. Like many companies, they lacked visibility into the factors that genuinely fostered loyalty.
Walt tackled this problem by connecting fragmented data systems like Shopify sales, ERP inventory, purchase behavior, and repeat rates, translating them into a common language to surface what truly matters1. Instead of requiring users to dig through data, Walt puts the key insights right in front of them. This results in a complete loyalty breakdown across crucial dimensions like acquisition channels, end uses, product features, purchase paths, and price tiers.
Walt's analysis for the shoe company revealed significant findings:
• Only 11.6% of customers returned overall, but these repeat buyers were worth 2.5 times more.
• Email was the top acquisition channel for repeat customers with a 20.5% repeat rate, although a large portion (68%) of traffic lacked attribution data.
• Workwear product use generated the highest loyalty at 17.6%.
• Products with functional features like waterproof, slip-resistant, and airport friendly had high repeat rates (between 18% and 21%).
• Certain purchase paths, such as buying office shoes first then casual, led to a high lifetime value exceeding $340.
• The $80-$90 price tier showed better repeat rates and higher lifetime value compared to less expensive options.
• Contrary to potential assumptions, discounts did not encourage repeat purchases, although they did increase initial spending. Full-price customers were 47% more likely to return.
Based on these insights, Walt proposed five actionable strategies to increase the repeat customer rate from 11.6% to over 15%, estimating this could unlock an additional $275,000 annually. These steps included:
1. Rethinking discount strategies, moving to email-only or feature-specific promotions instead of widespread offers.
2. Highlighting functional features prominently in product descriptions, emails, and navigation.
3. Guiding customers toward high-value purchase journeys using personalized recommendations and targeted follow-ups.
4. Encouraging customers to buy within the $80-$90 price range through strategies like bundling or positioning.
5. Adjusting acquisition spend to focus more on high-performing channels like email, improving attribution tracking, and using content to attract loyal customer segments.
These recommendations were based on data from the company's existing operations, providing a clear plan for improving loyalty. A key follow-up question Walt helps address is confirming whether the company actually has sufficient stock of the products identified as loyalty drivers.
