Transforming Retail Product Recommendations at Scale with ML

Client

An American Automotive Retailer

The client is a leading retailer of automotive parts and accessories in the United States, serving millions of customers through an extensive network of stores and digital platforms. Known for its commitment to quality, convenience, and exceptional customer service, our client offers a diverse range of products and solutions to help drivers maintain and enhance their vehicles.

Challenge

Stagnant Growth in a Dynamic Marketplace

The client faced mounting challenges in driving growth and customer engagement. Despite having a rule-based recommendation engine, the retailer struggled with ineffective upsell and cross-sell strategies, generic product suggestions, and fragmented data. Operational complexity and high maintenance costs further slowed innovation, while the lack of personalization eroded customer trust and conversion rates. These barriers highlighted the urgent need for an intelligent, scalable solution to transform retail recommendations.

• Ineffective Upsell & Cross-Sell
  The rule-based recommendation engine delivered no significant increase in cross-sell or upsell revenue and failed to leverage purchase histories or adapt to market changes.
• Lack of Personalization
  Recommendations were generic, ignoring buying patterns, regional nuances, and seasonal factors, which reduced customer trust and sales effectiveness.
• Operational Complexity
  Maintaining and updating the engine across hundreds of stores was labor-intensive and prone to errors, resulting in inconsistent performance and delayed rollouts.
• Siloed Data
  Customer transactions, store performance, and third-party data remained fragmented, preventing the creation of a unified analytics ecosystem.
• Missed Revenue Opportunities
  Store associates could not drive incremental sales without personalized recommendations.
• Low Conversion Rates
  Generic add-on suggestions led to poor acceptance and a decline in customer confidence.
• High Maintenance Costs
  Manual processes increased operational overhead and delayed innovation.

Solution

Intelligent, Layered ML-powered Recommendations

To unlock new growth, Hexaware introduced a machine learning (ML)– powered retail product recommendation system, revolutionizing how the client engaged with its customers at checkout.

• Personalized Recommendation Engine
  Leveraged ML models to analyze purchase history, product affinities, and demographics, delivering tailored recommendations for each shopper.
• Context-Aware Insights
  Integrated external data such as weather and seasonality to dynamically adjust recommendations, e.g., promoting wipers during rainy spells.
• Automated, Scalable MLOps
  Built custom MLOps pipelines on Google Cloud Vertex AI to streamline model training, deployment, and monitoring across all locations.
• Seamless Store Integration
  Designed a real-time interface for associates, ensuring intuitive delivery of recommendations at checkout for high adoption.
• Continuous Feedback and Governance
  Implemented feedback loops and governance frameworks to enable ongoing model improvements and transparent performance tracking.
• Simplified Workflows
  Conducted in-store observations and workshops to keep processes simple and unobtrusive.
• Lightweight Architecture
  Deployed a lightweight system for rapid rollouts with minimal downtime.
• Associate Interaction Tracking
  Enabled associates to mark recommendations as “accepted” or “rejected,” refining the system continuously.
• Centralized Performance Dashboard
  Provided real-time, region-wise performance reviews and A/B testing through a centralized dashboard.

Benefits

Driving Measurable Impact Across Revenue, Engagement, and Efficiency

The implementation of ML-powered recommendations and MLOps delivered measurable business impact across key performance areas. From revenue growth to operational efficiency, the results speak for themselves.

• Revenue Uplift – 10% Growth in 3 Months
  Pilot stores achieved a 10% increase in cross-sell and upsell revenue by delivering precise, relevant recommendations.
• Higher Engagement – 22% Lift in Acceptance
  Weather and seasonality data made suggestions more relevant, driving a 22% increase in acceptance rates.
• Operational Efficiency – 60% Faster Deployment
  Deployment timelines for updates were reduced by 60%, shortening the rollout from weeks to days.
• Consistent Regional Performance
  Nine out of ten pilot regions reported revenue growth, with the highest gains in weather-sensitive categories.
• Enhanced Customer Experience – 15% More Helpful Interactions
  Customer surveys revealed a 15% increase in the perceived helpfulness of associate suggestions, which in turn boosted satisfaction and trust.

Summary

Client’s Journey to Intelligent Retail

Today, the client is experiencing a fundamental transformation in its retail operations. The AI/ML-powered recommendation engine drives measurable revenue growth, delivers personalized experiences, and enables rapid innovation at scale. Operational complexity has been significantly reduced, store associates are empowered, and customers are receiving more relevant and timely product suggestions. With Hexaware’s solution, the client continues to lead the way in intelligent automotive retail, leveraging data and technology to delight every customer, every time.

Ready to Transform Your Retail Recommendations?

Unlock measurable growth, personalized customer experiences, and operational efficiency with ML-powered retail product recommendations and MLOps. Learn more about our services for Retail here.