Increasing sales effectiveness with repurchase predictions

Use Case

Upsell and Repurchase Prediction

A U.S. wellness services company saw ROI in just over 2 months by integrating Pecan’s predictions into its call center campaigns.

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Industry: Wellness services

Company Size: Hundreds of providers in over 100 U.S. locations; $250M+ revenue

Solution: Prioritize and personalize call center campaigns with predictions

Platform Use Case: Upsell/cross-sell prediction

+ 12%

increase in repurchases

+ 35%

uplift in sales conversion rate

+ $400K

of incremental revenue captured within just 2 months

Challenge

This wellness services company helps its customers optimize how they look and feel, fueling a fresh outlook and greater confidence. 

But the company needed help in optimizing its customer outreach to suggest another visit or recommend new products and services.

The company relies largely on call center campaigns, which are relatively expensive. Before Pecan, the company used rule-based logic and descriptive analytics to optimize repurchase campaigns. 

However, this process generated broad customer segments, not individual-level recommendations, which the company found didn’t personalize their sales effort enough. Using only simple data points — like whether a customer made a purchase in the last 30 days — was insufficient to accurately anticipate their future needs and interests.

In a world where customers expect a highly individualized experience, this approach resulted in missed sales opportunities, plus inefficient use of the call center and staff resources. 

Solution

The company partnered with Pecan for a predictive modeling solution to power better personalization and maximize the impact of their call center campaigns. 

Pecan’s predictive analytics offered a better way to foresee customers’ interests. Using all of the company’s historical Salesforce data, not just a few selected data points, Pecan’s models could find more nuanced patterns in customer behavior. Those patterns can be used to accurately predict customers’ future needs and preferences.

Specifically, Pecan’s models identify the customers most likely to repurchase within 30 days, allowing the call center to prioritize which customers receive contacts. Additionally, the models generate personalized recommendations for each customer instead of creating simplistic, overly broad customer segments. 

The first models were ready to test in just two weeks, demonstrating the speed of implementing predictive analytics with Pecan.

Predictions are fed from the Pecan platform back into Salesforce for the company’s sales teams to use in prioritizing and personalizing customer outreach. 

Results

Using the models’ predictions, the company saw results rapidly, with ROI generated in just two months. 

Within two months, the company was able to capture $400K of incremental sales by automatically targeting and prioritizing outbound sales campaigns to customers with the highest likelihood to repurchase. Refining their outreach with predictions makes the sales process more efficient, effective, and relevant to customers’ interests.

With Pecan’s accurate predictive solution, this company maximized its sales potential and shaped a better customer experience. Adopting predictive analytics made it possible to confidently anticipate customers’ wellness needs and desires, creating growth opportunities and efficiencies that descriptive analytics failed to provide.

Contents

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