Maintaining the relationship with a customer can be a costly exercise, and not all customers provide the same value to an organization. There are some customers who buy little and the value of their purchase is low - and this is unlikely to change regardless of the type of stimuli. You may still want to keep them as customers, but you don't want to spend a lot of your marketing budget on them if you know they are unlikely to respond to new offers. Essentially, the organization can lose money by investing in this type of relationship.

Then, there are other customers with whom a stronger relationship and better customer interaction would result in more profitable relationship and higher value to the organization. These two groups of customers differ in their buying potential.

Recently, I was tasked to provide assistance to a financial company with a goal to estimate the purchasing potential of about a million customers who have rarely been contacted in the past. Due to the prohibitive cost of the acquisition programs, and the fact that it is cheaper to sell to an existing customer than to a newly acquired customer, this South African financial organization had decided to re-acquire a segment of their dormant base that was rarely contacted in the last five years. The intention was to use advanced analytics to separate customers whose buying potential was spent from the customers who would still respond given appropriate marketing stimuli.

So, this was two-step process. On a first step we needed to quantify customer buying potential through building a simple “look-a-like” model, which would assign probability to purchase. For customers with potential above a specific threshold we produced a cross-selling model that would give us a probability of purchase for a specific product. After that was done, we were not only able to isolate customers who still had buying potential, but moreover – we were able to tell which product should be offered to which customer so that this potential is realized.

After modelling was done, we conducted some pre-implementation testing which was successful, and that opened the door for a full implementation. About 12 months later, we collected numbers that indicate 24 percent off the dormant segment was re-activated.

Have you worked on similar projects? What positive results have you achieved with customer segmentation? What lessons have you learned about re-activating dormant customers?