AI-Powered Customer Segmentation for an E-Commerce Startup
A U.S.-based D2C (direct-to-consumer) e-commerce startup was struggling with stagnant revenue growth despite increased marketing spend. Their one-size-fits-all campaigns were failing to convert, and customers weren’t engaging with their promotions.
Challenge:
The client lacked actionable segmentation of their growing customer base. They collected clickstream data, purchase history, and demographic information but couldn’t convert that into meaningful personalization strategies. The leadership team wanted to explore AI as a way to boost engagement and sales without dramatically increasing overhead.
Solution:
We implemented an AI-driven customer intelligence platform that automated segmentation and powered real-time personalization.
Our solution involved:
Collecting structured and unstructured data from Shopify, Klaviyo, and Google Analytics
Using unsupervised learning (K-Means and DBSCAN clustering) to segment customers based on behavioral patterns and lifetime value
Building profiles for groups like “discount-driven buyers,” “loyal repeat shoppers,” and “seasonal spenders”
Deploying a recommendation engine (collaborative + content-based filtering) that suggested relevant products and offers per segment
Integrating the system with their ESP and on-site personalization engine
We also trained their marketing team on interpreting model outputs and A/B testing personalized email flows vs. generic ones.
Impact:
25% uplift in total revenue within three months of rollout
22% improvement in click-through rate on segmented email campaigns
Reduced customer churn by 18%, with high-value users showing stronger retention
Faster content deployment: marketing team could create dynamic content blocks for segments in one click
Ongoing insight generation: the model auto-updated every week to reflect changing customer behaviors
The project enabled the startup to operate with the marketing intelligence of a much larger company without expanding headcount.