Shortcut the Sales Cycle: Machine Learning Product Recommendation Engine
To help an operational parts wholesaler improve sales efficiency and identify new opportunities, HBS custom-developed a machine learning product recommendation engine that speeds up the sales process by delivering precise, data-driven insights and prioritizing high-value opportunities.
The Challenge:
A distributor with thousands of products and thousands of clients needed a smarter way for its sales team to identify the right products for customers without relying on manual research.
The HBS Solution
An AI-powered Product Recommendation Engine that analyzes existing customer data and purchasing patterns to deliver personalized suggestions.
The Results
This streamlined sales workflows, improved efficiency, and deepened customer engagement. Sellers now spend less time searching and more time building relationships—driving both sales and customer trust.
The Challenge
Sales teams—especially those at small and medium-sized businesses—want to deepen client relationships, provide a better customer experience, and drive more sales. And one of the best ways to do that is pitching the right products to the right customers—but that’s easier said than done.
Too much time is spent juggling reports, intuition, and time-consuming research—only to risk missing opportunities. That time is wasted searching for answers instead of selling.
The Solution: AI Product Recommendations
To bridge this gap, an HBS client—one with a massive inventory full of thousands of different products—asked us to develop a product recommendation engine—built with machine learning—that turns their already existing sales data into actionable insights for their sales team.
Using principles like collaborative filtering and customer clustering, this tool provides targeted recommendations to account managers. The result? Time saved, precise sales, and smarter customer engagements that drive results.
How a Product Recommendation Engine Works—a Real World Scenario
This solution works much like Netflix or Amazon’s recommendation engine. Just as those platforms suggest shows or products based on what similar users enjoy, our product recommender system identifies high-probability sales opportunities by analyzing the behaviors and needs of similar customers.
Recently, HBS partnered with a client to implement this system with great results.
- Grouping Similar Accounts
We began by working with the client to identify key customer attributes—like purchasing history, business needs, and industry verticals. The system then clustered customer accounts based on those characteristics, creating a roadmap of relevant product recommendations tailored to each segment. - Learning from Other Accounts
Once accounts were grouped, the system analyzed which products and services similar customers were buying. If a product succeeded with one account, it was recommended to others in the cluster, giving account managers proactive opportunities. - Simplified Recommendations
Instead of spending hours researching what to sell, the client’s sales teams received a list of prioritized opportunities. This allowed them focus on what they did best—selling.
- Grouping Similar Accounts
This is not reactive sales; it's proactive and predictive. Instead of having someone spend a lot of time and effort doing research, the recommendations are already right there—it's a shortcut to selling.
Max Lacy, HBS AI Engineer
The Business Value of a Product Recommender
This recommender engine enables account managers to act strategically, sell faster, and deepen client relationships. Key benefits includes:
- Time Savings: Research that typically takes hours is now automated in seconds.
- Precision Targeting: Account managers can focus on high-probability opportunities instead of guesswork. Additionally, the model highlights areas that have been previously overlooked, providing visibility into account growth.
- Dynamic Intelligence: Recommendations are tailored to each customer based on multidimensional insights, ensuring the right products are prioritized for every customer.
- Stronger Outcomes: With cleaner data and predictive insights, deals are closed faster and customer trust is strengthened.
What’s Next
Because this system was—and is—designed to evolve as it updates with new purchasing patterns, business needs, and account data, its recommendations become even sharper and more effective. With each customer interaction, the model learns and adapts, providing sales teams with the most relevant intelligence.
Why This Especially Matters for SMBs
Small businesses often lack the extensive resources of larger competitors. This tool changes that by offering enterprise-grade capabilities in a scalable, cost-effective package. It provides SMBs with actionable, data-driven insights, allowing them to compete—and succeed—on a larger scale.
A Sales Shortcut You Can Count On
Ready to transform your sales strategy? This product recommender system is your shortcut to selling. It equips your team with data-driven insights that save time and deliver results.
Let us show you how this innovative approach can help your business identify high-value opportunities and close deals faster.
