Subscriptions, Web Development, AI, Pick System, Logistics, Automation, RPA, ERP, R&D
Trendy Butler
2014
AI-POWERED,PERSONALIZED SUBSCRIPTION
TrendyButler delivers a personalized shopping experience by providing the newest styles and trends from top brands while delivering value to the customer.
Variety, exclusivity, and unique experiences attract & retain customers. Further engagement is ensured through exclusive offerings in partnership with the most prestigious legacy brands.
TrendyButler does this by leveraging a proprietary data science model. The data platform provides TB an enormous competitive edge in product matching to subscribers' style profiles, thanks to a "pick system" based on an advanced recommendation engine and other machine learning approaches, which provide operational benefits, a personalized experience for the customer, and actionable feedback data from NLP-based customer service encounters. All of this constantly improves the accuracy of delivering products that subscribers love and allows TB to do a better job of keeping customers "happy."In addition, the model will enable them to measure KPIs and execute on minimizing churn, increasing retention, and maximizing LTV - ultimately driving organic new customers through WoM from the satisfied subscribers
CLIENT CHALLENGES
• High CapEx and OpEx dueto the need for an expensive human stylists
• Complex product acquisition based on user style profile and variety
• Low margin for error because of compressed profitability
• Industry wide average return rate of 25%-35%
• Low customer retention / LTV
OUR INNOVATION & SOLUTIONS
• Over 250 data points with autonomous style customization creates a member’s perfect monthly package.
• As TB learn more about the customers, brands and products are selected based on the personalized recommendation engine.
• Members can login to their style profile and adjust style settings to affect their next package.
• Advanced learning and synaptic adjustments occurs based on real-time customer feedback
• Self-initiatedRMA based on product review
RESULTS
250+ meaningful data points collected for each customer
3,380+ unique packages generated per month by the “Pick System” style recommendation engine
3.5%-6% total return rate (industry standard 25%-35%)
All products in the inventory turned in 21-40 days (industry standard 60-90 days)
9.5% churn rate (other subscription services 12.5%-16%)
Based on smart feedback model, LTV increased by double
Related Work
