Predictive Analytics: How Distributors Can Start Forecasting the Future

Create target customer segments that are more likely to take advantage of your unique product selection and services.
man holding tablet with pie chart in warehouse

For years, organizations have relied on institutional knowledge and market feedback to best manage expectations for customer acquisition and retention. Times have changed and numerous models have been developed that allow distributors to accurately predict customer behavior. 

To acquire customers, information made available from public records, data merchants and others can help to direct prospecting efforts to companies that are most likely to respond to your offering. Marrying your own transaction data with third-party data can expose new opportunities by uncovering what prospects are typically buying from other suppliers and by best matching your sales and marketing efforts to target the appropriate segments with the best sales and marketing plan.

Prospecting is only the start. Distributors can also now predict with high levels of accuracy which customers may be at risk of attrition and to what extent that risk exists. When married with customer satisfaction scoring, these models can be extremely valuable to reduce or eliminate customer defection. 

Sales teams will be well served to evaluate predictive modeling in coordination with their marketing and analytics teams and to develop programs that take advantage of the knowledge provided by these models.

Prospecting for New Customers

Plumbing distributor sales representative James had years of mild success building his portfolio through cold calling and it resulted in nearly 200,000 miles on his trusted Camry. Although his territory remained the same for years, he could always count on new businesses being opened to add to his list of prospects. Over the years, however, his success rate remained consistently low and he found himself struggling to justify the time and miles spent every week seeking new business.

This past year, things took a positive turn when James was asked to move into a sales leadership role. Not wanting to burden his team with the same challenges he had always faced, he began researching prospecting methods to create an advantage over his competition. He was pleased to uncover predictive analytics and immediately began building out a plan to first, uncover knowledge previously unavailable, and second, to use that knowledge to more effectively target prospects.

James worked with a team consisting of internal and external resources. Together, they developed a methodology that enabled them to bring together their own historical data with data available from data merchants. This new perspective allowed his team to create target segments that were more likely to take advantage of their product selection and services. They were also able to identify specific product categories of likely interest and the associated probable spend in those categories, opening up opportunities to modify their marketing approach with each prospect.

James and his team developed a plan to regularly review this new prospecting knowledge and created both segment and customer-specific sales and marketing plans to attract these customers. 

Gone are the days of blind cold calls without any information about the prospect; now they have specific knowledge of the company that can be used to attract prospects via email campaigns, social media, branch events, sales team interactions and more. Plus, James and his team now put fewer miles on their cars as they are targeting fewer customers, but with higher conversion rates.

Targeting At-Risk Customers

Getting new customers is great, but for many the biggest challenge is retaining existing customers. This was the case for a mid-sized tools distributor that found themselves losing volume with customers at an alarming rate. The entry of new online players resulted in additional competitive pressures they had not faced before. When sales representatives were creating their forecasts each month, they didn’t predict the decline that inevitably happened. Heck, they know their customers better than anyone, so how did they miss the shift in sales?

This scenario is played out all too often across many distributors in every industry. The simple answer to how forecasting goes wrong is the fact that there are too many nuances in purchasing trends for any sales representative to accurately forecast behavior, especially for customers that are rarely visited due to their limited size and/or amount of current business.

Often, incorrect assumptions have been made about the share of wallet with each customer, something that often masks a decline in volume as the customer can explain it away with stories about lost projects, etc., when the reality is that sales are being shifted to other suppliers.

In response to the added competitive pressures, this tools distributor took steps to engage others to develop a predictive analytics model that could identify the customers at risk and to assign a level of risk to each customer. The model relied on an analysis of transaction data and was able to predict the customers to be impacted, provide a score to reflect the likelihood of impact during the period and assign each customer a score that measured their loyalty, among other metrics. 

This was no easy task, as the model had to be modified over months of testing to refine it in a way that was most in line with their customers’ purchasing behaviors and accounting for anomalies like large projects, customer status and other key factors. 

Months into the program, the model was highly accurate and folks in the organization began to ask for more analysis, as it seemed the team had uncovered a crystal ball. The new model enabled the distributor to prioritize sales and marketing efforts to customers most at risk and resulted in reduced attrition rates as well as higher revenue per customer for those that continued to buy.

Read Part 2 here, where we explore six actionable steps for distributors to getting started with predictive analytics.

Mark Linder is CEO of Charleston, South Carolina-based MARV LANDERS, LLC. He has more than 20 years of experience leading multi-billion-dollar distributors through rapid changes on their digital journeys. Linder has achieved digital success by relying on the people and strategies required to effectively move organizations through significant strategic and operational changes. Reach him at mark.linder@marvlanders.com.

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