How We Developed an AI Model That Transforms Customer Segmentation for eCommerce
Overview of Our AI Model for eCommerce Case Study
A mid-sized eCommerce retailer wanted to better understand their customers to improve marketing effectiveness and increase sales. They had a large amount of customer data – purchase histories, browsing behavior, and product preferences. But still they struggled to translate this data into actionable insights.
We developed a custom AI model that segmented customers based on their behaviors and predicted their purchasing preferences. This allowed the retailer to deliver personalized product recommendations and tailor marketing campaigns for each customer group. Our solution is making the shopping experience more relevant and engaging.
eCommerce
AI Model Development
Our Process of Building a Scalable AI Model for Customer Segmentation
Our AI model development followed a structured process so it can be accurate and relevant to business:
Data Aggregation and Cleaning
Collected diverse customer data from purchase records, web behavior, and customer profiles. Cleaned and normalized data to ensure quality and consistency.
Feature Engineering
Created meaningful features like purchase frequency, average spend, product categories browsed, and responsiveness to past campaigns to capture customer preferences.
Unsupervised Learning for Segmentation
Applied clustering algorithms such as K-Means to uncover natural customer groups, iteratively refining based on business insights.
Model Deployment and Integration
Deployed the AI model as a scalable service integrated with the client’s CRM for real-time customer scoring and campaign automation.
