About client
Our client is a mid-sized, family-owned food distribution company operating in the FMCG sector, serving customers across the United States. With a catalog of over 10,000 products, they cater to both individual buyers and large accounts including multi-unit retail chains and government institutions.
As part of their digital transformation journey, Saviant previously built their cross-platform B2B mobile app and responsive web application, along with integrations to third-party systems, analytics tools, and a scalable push notification engine. These platforms now process over $100 million worth of orders and have delivered over $500,000 in cost savings through operational efficiencies.
Customers place orders through these web and mobile platforms, along with assisted ordering channels for high-volume buyers.
With a growing customer base and expanding product portfolio, the company aimed to further improve digital engagement and increase revenue per customer. However, the platform lacked intelligent recommendation capabilities, limiting its ability to influence purchase behavior, drive cross-sell, and maximize basket value.
The challenge: Why increasing basket size required a smarter approach
As customer acquisition matured, the focus shifted toward maximizing value from existing customers. However, without a recommendation system, several challenges emerged:
- Low basket size and limited cross-sell opportunities
- No structured way to recommend products based on customer behavior
- Promotions on the platform were underutilized and received low engagement
- Inability to leverage seasonality, trends, and customer patterns
- Static shopping experience with limited personalization
These challenges restricted revenue growth and prevented the platform from delivering a modern, intelligent shopping experience.
From static shopping experience to AI-driven personalization
| Before |
After |
| Lower basket size and limited cross-sell revenue as customers browsed and selected products manually without discovering additional relevant products. |
AI-driven recommendations suggest relevant products based on behavior, trends, and patterns through customer segmentation. |
| Underperforming promotional campaigns due to low visibility during the buying journey, leading to unsold inventory and poor campaign ROI. |
Promotions are embedded contextually within the buying journey, improving visibility and conversion. |
| Longer customer decision cycles and drop-offs as users had to manually explore products without guided discovery. |
Intelligent suggestions simplify product discovery and ensure smooth process and faster checkout. |
| Lack of visibility into product discovery performance, making it difficult to measure impact on revenue and optimize strategies. |
Built-in analytics track engagement and conversions to continuously optimize recommendation performance. |
Solution: AI-powered recommendation engine to unlock cross-sell and revenue growth
To address declining basket size, underperforming promotions, and limited use of customer data, the client partnered with Saviant to design and implement an AI-powered recommendation engine integrated into their existing web and mobile platforms. Our AI consulting & development team designed the solution not just as a feature, but as a revenue-driving intelligence layer - enabling real-time personalization, improved product discovery, and measurable cross-sell impact.
1. Multi-layered recommendation intelligence
To overcome limited utilization of customer data and missed cross-sell opportunities, the solution combines multiple machine learning models:
- Purchase history-based recommendations using frequency and recency signals
- Customer clustering to group similar users based on behavior, region, and buying patterns
- Cross-sell recommendations based on what similar customers purchase
- Seasonality-aware recommendations to capture evolving demand patterns
This enables deeper personalization and ensures recommendations are driven by collective intelligence, not just individual history.
2. Promotion-aware recommendation logic
To address low-performing promotions and poor visibility during the buying journey:
- Promotional products are embedded contextually within the recommendation flow
- Promotions are surfaced at high-intent moments such as checkout
- Improves conversion compared to static homepage banners
This transforms promotions from passive visibility to active revenue drivers.
3. Real-time contextual intelligence at checkout
To reduce irrelevant suggestions and improve conversion efficiency:
- Filters out items already present in the cart
- Avoids recommending substitute or similar items
- Prioritizes recommendations based on real-time cart context and inventory
This ensures recommendations remain contextually relevant and actionable, improving customer trust and engagement.
4. Continuous learning and adaptive models
To ensure long-term relevance and adaptability:
- Models are retrained periodically (weekly/monthly)
- Continuously adapt to changing customer behavior and product trends
- Maintain recommendation accuracy over time
This creates a self-evolving intelligence layer that improves with usage.
Technology foundation
The solution was built to integrate seamlessly with the client’s existing digital ecosystem without disrupting current operations:
- Machine Learning: Python-based ML models
- Architecture: API-driven integration with existing web and mobile platforms
- Data processing: Batch-based recommendation generation
- Real-time layer: UI-driven filtering and prioritization
- Deployment: Embedded into existing systems with minimal changes
Saviant’s engagement approach: Built for measurable revenue impact
- Started with value discovery and revenue levers
Saviant worked with business stakeholders to identify key drivers - basket size, cross-sell, and promotion effectiveness - and defined how AI could directly influence these outcomes.
- Designed intelligence around real customer behavior
Analyzed purchase patterns, clustering opportunities, and seasonality to design a recommendation strategy aligned with real buying behavior.
- Built what mattered first
Focused on developing high-impact recommendation models to improve product discovery and cross-sell opportunities.
- Validated through real-world scenarios
Conducted extended UAT with actual customer journeys to ensure recommendations were relevant, realistic, and value-generating.
- Delivered a scalable intelligence layer
Implemented a solution that integrates seamlessly and supports continuous optimization and future enhancements.
10 Months+
to full ROI — by transforming product discovery into an intelligent, data-driven experience, the client converted their digital platforms into a scalable revenue engine.
Result
~23% increased contribution
through online platform to total sales through smarter product discovery and recommendations.
Higher cross-sell & conversions
through personalized and cluster-based recommendations, with full visibility into clicks, add-to-cart, and conversions.
Faster customer decisions
during shopping journeys and increased engagement and conversion of promotional campaigns.
Current adoption and roadmap
The recommendation engine is fully deployed across the platform following successful validation during UAT. The client is now:
- Expanding analytics capabilities to further optimize recommendations
- Leveraging recommendation insights for business decision-making
- Strengthening digital channels as a primary revenue driver
What’s next: Expanding digital intelligence across operations
The next phase focuses on extending intelligence beyond the digital storefront:
- Development of a sales representative application to digitize field operations
- Replacement of manual tracking processes with structured digital workflows
- Improved visibility into sales performance and KPIs
- Integration with future ERP modernization initiatives
This roadmap reflects a broader shift toward end-to-end digital intelligence, enabling scalable growth, operational efficiency, and data-driven decision-making.