When Redemption Plus went in search of predictive analytics experts, it wanted to work with a company that could help it understand and energize its own unique client base.

As a leader in providing redemption and incentive programs, Redemption Plus understands how important it is for businesses to motivate customers. Yet its data analysis faced a challenge. Given the characteristics of diverse clientele, seasonal cycles, and changing trends, how could Redemption Plus more accurately assess and predict profitability? This meant taking the predictive modeling game to a whole new level.

The Ask

Redemption Plus started with a few simple questions for DEG, a full-service digital agency and expert in marketing analytics. First, could DEG help it identify not just its top customers, but also the most profitable ones? Second, could DEG identify groups of top customers who shared similar profitability characteristics, thus predicting future top prospects? Last, could DEG recommend customer-service strategies for nurturing these customer groups?

The Answer

The DEG advanced analytics team assessed the data’s potential. A basic RFM score focused on past purchases was obviously far too limited to do justice to the rich material. The pool covered thousands of customers, accounts, and orders over a three-year period, including demographics, purchases, products, costs, and other information.

Instead, DEG used SPSS Modeler, a specialized statistical modeling tool, to build its own completely custom predictive model – the Profitability Predictor Score (PPS) – which is a measure of customer profitability rather than revenue.

Here’s how it works. The PPS creates a score that measures each customer’s potential future profitability. DEG built the PPS with custom variables unique to Redemption Plus customers, such as purchasing behavior, order cadence, etc.

Since the PPS is a more definitive value, with differing weights assigned to each input variable, these advanced customer scores can be easily ranked and interpreted independently.

Using the PPS, DEG identified top customers based on future predicted profitability. This elite customer group was then separated into six clusters, based on shared characteristics, using advanced data clustering methods to identify natural groupings. Since the PPS is a custom score, DEG also has the ability to update the scoring algorithm as Redemption Plus’ business evolves, or refresh the same algorithm with up-to-date data.

The Result

From account growth to maintenance, Redemption Plus now knew which accounts to focus its attention. Next, DEG helped the company figure out exactly how to do it.

Based on the PPS analysis, DEG’s top strategists recommended different sales and customer-service strategies for each of the six customer clusters. This included better aligning accounts (particularly top accounts) to sales teams with a focus on customer needs. It also took into account the strengths and focus of sales teams and individual representatives, as well as their currently established relationships.

DEG gave Redemption Plus the ability to predict profitability and align efforts accordingly, which is a measure every business wants to know, as well as the opportunity to better understand, and connect with, its customers.