In the telecommunications industry, addressing account closure, or churn, is a massive cost-saving effort. Using Path Finder and Modeler, analysts in this industry can target ways in which to improve retention by understanding customer behavior.
The initial step involves creating an event table to integrate interactions and transactions involving the customer. By capturing the events you can view the customer’s journey, which may have involved visiting a store, going to the website, calling the support line, upgrading service, and canceling service.
Using Path Analyzer, you can now click on the events to answer business questions, such as:
- What paths are my customers taking on the website?
- What paths are my customers taking before calling the support line?
- What paths are my customers before canceling their service?
You may, for example, uncover that a high percentage of customers who cancel have also had bill disputes. This may be a new pattern that was previously unknown and using Path Analyzer uncovered.
As a result, you may decide to take steps to retain customers who are visibly on this path and have not canceled their service. Using Path Analyzer, you can create a new pattern where:
- Event A = Bill Dispute
- Event B = Any Event.
The results expose that Bill Dispute events leads to Review Contract and then Cancel Contract. You can target that path and export the customer list, which can then be used in a customized marketing offer to retain at-risk customers.
You may want to take these findings further and share the results with the data science team for inclusion in their churn prediction model. The information you could share is that bill disputes have been shown to lead to churn, and it might be valuable to add bill disputes to the model.
Assume the data science team adds bill dispute to the customer profile table used by a model, such as Modeler. Running the Decision Forest model shows that the bill dispute field is important to the model.
These findings are then used to create a new target table using the top 20% of high-churn at-risk customers. These results are then added to the list of customers for the customized marketing campaign.
Using Path Analyzer and Modeler, you can also overlay customer value scores onto the list so that they can expedite reaching out to the high-value customers who are potentially at risk of canceling their service contracts.