Increasing sales effectiveness with repurchase predictions
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.
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 predictionA prediction is the ultimate goal of a predictive model. In Pecan, a prediction is often tied to a specific customer. After learning from data… More
increase in repurchases
uplift in sales conversion rate
of incremental revenue captured within just 2 months
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.
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 analyticsPredictive analytics uses data, statistics, and machine learning techniques to build mathematical models that can generate predictions about things likely to happen in the future…. More 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 analyticsAnalytics is a business practice that uses descriptive and visualization techniques to gain insight into data; those insights can then be used to guide business… More 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.
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 analyticsDescriptive analytics is the analysis of data from the past in order to “describe” what has been happening in a business. It typically includes looking… More failed to provide.