Deepiom - Empowering Digital Growth

Product Recommendations: Personalized product suggestions and cross-selling optimization

Personalized product suggestions that drive conversions. Boost sales with smart cross-selling and upselling optimization.

Project Overview

Industry: E-commerce & Retail

Scope: Personalized recommendations across web, mobile, and email channels

Project Duration: 5 months

Team Size: 2 AI engineers, 1 data scientist, 1 product manager

Business Challenge

The retailer struggled to maximize sales opportunities due to generic product recommendations and limited personalization. Key issues included:

Low click-through and conversion rates from static “related products”
Missed cross-selling and upselling opportunities
Inconsistent personalization across customer touchpoints
Reduced customer engagement and retention

Our Approach

We implemented an AI-powered recommendation engine that delivers personalized product suggestions and optimizes cross-selling opportunities. The solution focused on:

Enhancing customer experience with individualized recommendations
Increasing average order value through optimized cross-sells
Driving higher engagement and retention across digital channels

Product Recommendation Features

AI-driven personalization based on browsing, purchase, and behavioral data
Real-time recommendation updates across web, mobile app, and email
Cross-sell and upsell optimization at checkout
Centralized analytics for tracking recommendation performance

Implementation Process

Phase 1: Data collection and integration of purchase history and browsing data
Phase 2: AI model training using collaborative filtering and deep learning techniques
Phase 3: Pilot testing on selected product categories
Phase 4: Full deployment with A/B testing and ongoing optimization

Quality Assurance

Continuous monitoring of recommendation accuracy and engagement metrics
A/B testing for conversion lift validation
Failover to default rules-based recommendations if system downtime occurs
Compliance with GDPR and data privacy regulations

Results

Customer Engagement

25% increase in click-through rates on recommended products
20% improvement in repeat purchases

Sales Performance

18% increase in average order value through cross-selling
Higher conversion rates across web and mobile channels

Business Impact

$2M annual revenue uplift from personalized recommendations
Increased customer loyalty and retention through improved shopping experience
Stronger competitive positioning as a customer-centric retailer

Technical Implementation

Machine learning models for collaborative filtering and deep personalization
Real-time data pipelines for recommendation updates
API integration with e-commerce platform and CRM system

Key Features

Personalized product recommendations across channels
Optimized cross-sell and upsell strategies
Centralized analytics for performance tracking

Client Feedback

“”

The personalized recommendations have transformed our customer experience. We’ve seen higher order values, better engagement, and customers now return more often.

Implementation Timeline

Before AI Implementation

Generic, rules-based recommendations
Low click-through and conversion rates
Limited cross-sell and upsell opportunities

After AI Implementation

25% higher CTR on recommendations
18% increase in average order value
Improved retention and customer satisfaction

Implementation Challenges

Ensuring accurate personalization with sparse data for new customers
Integrating AI models with legacy e-commerce systems
Balancing personalization with product visibility for promotions

Continuous Improvement

Ongoing retraining of models with new customer behavior data
Expansion into personalized bundles and seasonal recommendations
Deeper integration with loyalty programs and marketing automation

Future Enhancements

Hyper-personalized recommendations using real-time context and intent
AI-driven dynamic pricing for suggested products
Integration with in-store kiosks for omnichannel recommendations