See Customer Churn before It Actually Happens


Customer Loyalty Solutions Today

Customer satisfaction has long been monitored using different techniques and referred to using many terms. For Marketing Research, typically, surveys are provided to customers that ask either about their overall impressions of a company (called a Relationship Survey) or about a specific recent experience (Transactional Survey). In both cases, customers provide their opinions. The types of measurement metrics used vary a great deal: from as little as one question, which asks if a customer would recommend a company, to a complex survey that asks about an experience at every touch-point with a company.

New Technique to See Churn

These customer loyalty programs are tried-and-true and very important to the inner workings of an organization. However, one component still missing from these programs is the likelihood a customer is swayed to leave by specific competitive offerings.

By presenting key competitive products to a current customer base, and asking those customers if various products are attractive enough for them to consider leaving their current provider, an organization can gain a new level of extremely useful information.

C-ExIT – What It Is

C-ExIT (See customer churn before it happens) presents competitive market options to a set of current customers. It asks each specific customer which products are strong enough to tempt them to defect from their current provider, and which product features most affect their decision.

C-ExIT – What It Does

C-ExIT identifies what proportion of a customer base may be vulnerable to defection, and profiles “vulnerability groups” to help understand what might be driving vulnerability. In addition, C-ExIT provides individual index scores for each respondent who took the survey. These index scores can be cross-checked against demographics (or used in a formal segmentation model) to identify subgroups with varying degrees of vulnerability. In fact, an algorithm can be created and linked to a customer database to identify a C-ExIT vulnerability level for every customer.

As the competitive market changes often, C-EXIT can be re-fielded (after updating the competitive product sets) to get a realistic and up-to-date view of which customers are tempted to defect.

Integrating C-ExIT scores with traditional Customer Loyalty metrics (as well as newer Social Media Techniques) can provide a heretofore unknown level of specificity regarding engagement, loyalty, and satisfaction.

Adaptive Choice With A Twist:

  • Adaptive Choice-Based Conjoint (ACBC) is a key part of the C-ExIT methodology. It includes all the advantages typically associated with ACBC. However, by modifying one section of the choice exercise, gauging vulnerability more directly becomes possible.

Vulnerability Screening:

  • As traditional with ACBC, during the screening section, respondents are presented with real-world competitive packages.
  • However, rather than asking “Is this product a possibility”, respondents are directly asked if a competitive offering is a possibility that they would switch to from their current provider.


  • Asking customers directly if a competitive offering would possibly cause them to switch allows the beginning of the calculations for C-ExIT.

This modification takes no extra time for a respondent than what is typical for ACBC exercises.


What You See

Overall Vulnerability of Customer Base

The first deliverable our clients are given is a high-level understanding of the percent of their customer base that is likely vulnerable to defection.  In addition, our clients get insight into those customers who are “out-the-door” versus those that love their current product and won’t think of leaving.





Individual Churn Probability

Each respondent has an individual churn probability score. This score allows for two things:

  1. Customers’ probability scores can be profiled using other survey or database items, such as demographic variables, to understand the types of people who are most/least vulnerable.
  2. Customers’ probability scores can be used in a segmentation analysis, allowing classification algorithms to be created and fed back into a customer database.


Better Understanding Churn by Using Segmentation

Your customers are not all the same. They have different needs, different behaviors, different attitudes, different demographic profiles, and different vulnerabilities.  If you don’t understand these differences, you are left with a one size fits all approach to retain them. Feeding churn index scores and preference data attained through C-ExIT into a segmentation analysis allows our customers to see what drives these different customers and what specific efforts will be needed to retain them.









Classification Algorithms

Using segmentation analysis creates groups that are not only applicable to the respondents studied in a survey, but can be applied to the rest of a client’s customer database. Created algorithms help understand churn vulnerability across the entire customer base. By using the reverse segmentation method, segments can be well understood both in terms of their vulnerability and their demographic/targeting profiles.

For more information about how C-ExIT might help you, contact us at