Executive Summary

A global chemical leader needed a modern, scalable solution to uncover cross-sell and up-sell opportunities buried within fragmented systems and decades of data. Sales teams lacked clear guidance on what to recommend, to whom, and when, leading to missed revenue and inconsistent customer engagement. The initiative aimed to deliver a unified, intelligence-driven approach that empowers sellers with actionable insights and drives stronger customer relationships and growth.

The Challenge

• Critical customer, product, and transactional data was scattered across multiple systems, making it difficult to surface meaningful insights.
• Sellers relied on manual processes to identify cross-sell and up-sell opportunities, leading to inconsistent results and missed revenue.
  
  
• No unified, intelligent system existed to guide sellers with timely, personalized recommendations.
  
  
• The business lacked a scalable way to continuously refine recommendations based on real-world seller feedback.

Our Approach

1. Built a Unified Data Foundation in Snowflake
   
   We established a Single Source of Truth combining operational, transactional, customer, and product data to power high-accuracy recommendations.
   
   
2. Enabled Zero-Copy Data Federation for Efficiency
   
   Through Snowflake–Salesforce/Data Cloud federation, we ensured data consistency while avoiding duplication and reducing operational cost.
   
   
3. Delivered AI-Driven Next Best Product Recommendations
   
   A deep-learning Next Best Product (NBP) model identifies and ranks cross-sell and up-sell opportunities, made available directly inside Salesforce for seamless seller adoption.
   
   
4. Established a Continuous Feedback Loop
   
   Sellers act on recommendations, provide feedback, and help retrain the model. A simple Analyze → Review → Refine cadence ensures fresh, relevant insights over time.

How It Works for Sellers

Inside Salesforce, sellers view NBP recommendation cards tailored to each customer. They can instantly create opportunities or submit feedback that enhances future recommendations. This integrated, in-flow experience eliminates guesswork and accelerates decision-making.

Measured Impact

The AI-powered solution delivered significant commercial value:

• $544M in potential revenue identified by machine learning.

• 73% recommendation success rate.

• 670 recommendations generated across 128 high-value customers.

• $30M+ in quick-win opportunities surfaced.

• Strong uplift in customer engagement and average order value.

• Cross-sell and up-sell insights embedded directly into the sales workflow.

Conclusion

By unifying data and embedding AI-driven recommendations directly into the seller workflow, the client transformed its revenue generation engine. What was once manual and fragmented is now intelligent, automated, and measurable. The result is a scalable solution that continually improves through feedback, empowers sellers with precision insights, and unlocks hundreds of millions in potential growth, positioning the organization for long-term competitive advantage in an evolving market.