Artificial intelligence and predictive sales analytics are paving the way for industrial B2B companies to unlock the value in their data and statistically predict what and when their customers will buy. You don’t have to be a data scientist to know that data is playing a critical role in transforming businesses across industries, but it helps to know a few that can get you there.

picture of two men looking over order before driver leaves for a delivery.

Predictive analytics deals with extracting information from ongoing and historical data and using it to predict trends and behavior patterns. Predictive algorithms work by profiling and grouping companies based on customer data such as size, location and type of company, past purchase history and ongoing transaction data. 

It can use various techniques including data mining, statistical modeling and machine learning to find correlations in the data, identify patterns and produce outcomes. Predictive algorithms benefit from time, usage and feedback, getting smarter as they consume more data, and producing more accurate and better insights.

Predictive sales analytics has grown in prominence as enterprises amass larger and larger data sets. This has created increased opportunity for large sales organizations to mine their data for predictive customer insights. Meanwhile, software companies have developed solutions that apply these techniques to create customer-specific insights that are delivered to sales to drive scalable and repeatable results.

For B2B sales enablement, predictive customer insights are instrumental in helping sales reps align their time and energy to the opportunities with the highest likelihood of closing and those that produce the best financial outcomes for the business.

It answers the questions that sales reps inevitably have on a regular basis – Where should I focus my time? Which customers do I call first? What should I talk to them about? Or, to say it in simpler terms, how am I going to hit my numbers?

Industrial manufacturers and distributors can use predictive sales analytics for true sales enablement that drives growth by leveraging intelligent customer insights for upsell, cross-sell and retention opportunities.

This was the case of FleetPride, a U.S.-based, industrial distributor of heavy-duty truck and trailer parts. The company wanted to stimulate organic growth and improve customer retention rates, but faced a competitive market with high customer defection. FleetPride looked for a data-driven solution to help identify and deliver hidden opportunities within customer accounts to their 400-person sales team at scale. 

The executive team partnered with Zilliant to integrate insights that helped their sales team to maximize customer lifetime. The customer-specific insights enriched their CRM instance with actionable guidance for its sales teams that drove greater efficiency and effectiveness. And when paired with a strategic, change management plan to drive adoption, FleetPride captured an additional $1 million in revenue per month, and met their goals for same-customer revenue and customer retention.

Read the full case study to learn how the company implemented an AI-enriched, predictive sales analytics solution that resulted in a 20 percent increase in same-customer revenue and exceeded their sales forecast four months earlier than expected.