Project Summary
Company
Online Retailer
Industry
E-commerce
Services
Data Science, AI Solution Consulting
Location
Europe
A rapidly growing online retailer approached us to design and implement a personalized recommender system to enhance customer engagement and boost sales. They sought a scalable solution that could adapt to their expanding customer base and product catalog while being simple to maintain.
Our Solution Delivered:
A personalized recommender system that improved click-through rates (CTR) by 30% and increased sales conversions by 15%.
Automated retraining and deployment pipelines to ensure sustained performance and relevance over time.
Training for the retailer’s engineering team to manage and maintain the system, ensuring long-term sustainability.
The Challenge
The retailer relied on a simple baseline strategy of recommending their top-selling products to all users. While this approach was straightforward, it failed to account for individual preferences, leading to missed opportunities for engaging customers and driving higher sales.
The project objectives were:
Increase Customer Engagement: By offering recommendations tailored to each shopper’s preferences.
Boost Sales Performance: Through a system that could outperform the existing baseline approach.
Scalability and Automation: Build a solution that could scale with their growth and automatically adapt to new data.
Ease of Maintenance: Ensure the system could be maintained without requiring constant external support.
The Approach
We collaborated closely with the retailer to design and deploy a custom solution that met the objectives. Our approach consisted of four core phases:
1. Data Preparation and Analysis
Collaborated with the client to integrate their historical sales, customer interaction, and product catalog data.
Conducted exploratory data analysis to identify patterns in user behavior and preferences.
Engineered features to make the data machine-learning-ready.
2. Personalized Recommender System
Developed a system combining collaborative filtering and content-based recommendations to suggest products based on user behavior and product similarities.
Introduced a hybrid approach to ensure robust performance across a variety of customer segments.
3. Automated Retraining Pipeline
Designed an automated retraining pipeline to ensure the model stayed updated with evolving user preferences and new product data.
Integrated performance monitoring to alert the team if recommendation accuracy dropped below a predefined threshold.
4. Client Training and Handoff
Provided hands-on training for the retailer’s in-house engineers to manage and maintain the system.
Delivered clear documentation and a troubleshooting guide to reduce reliance on external expertise.
The Impact
The deployment of the recommender system delivered measurable results and positioned the retailer for sustained growth:
Increased Engagement and Sales:
Sales driven by recommendations increased by 15% compared to the baseline.
30% increase in click-through rates (CTR).
20% growth in average order value (AOV) as customers discovered more relevant products.
Efficient Automation:
Automated retraining ensured that recommendations stayed relevant as customer preferences evolved, and eliminated the need for manual updates, saving the team 5+ hours per week.
The pipeline’s monitoring tools allowed the client to maintain confidence in the system’s reliability.
Empowered Client Team:
After training, the client’s engineers could manage the system independently, reducing dependency on external support.
This project transformed the retailer’s approach to personalization, enhancing the shopping experience for their customers and driving key business metrics. By automating critical processes and empowering the client’s team, we ensured the solution was scalable and sustainable.
Our collaboration highlighted the potential of tailored AI solutions to drive growth in e-commerce and provided the retailer with a foundation for future innovation. With this recommender system in place, the retailer is now better positioned to compete in a dynamic market while delivering value to their customers.
