Too Much Customer Churn? Don’t Panic, Use Analytics

Customer turnover, or churn, is a fact of life for many businesses. But predicting customer behavior can help mitigate losses due to churn, and may even help turn customer loyalty around.

It’s an eye-opening experience when a business we consult with sees how this process works. And even if they discover a huge churn problem using analytics, the main message is this: Don’t panic.

What do I mean? For starters, some of our customers began their analytics journey by testing data from their own enterprises. After drilling through reports, dashboards and scorecards, they assume that the first step in the journey is complete. They are happy when they see the “what happened” knowledge, as at least one big question has been answered.

But, as often happens, several new questions appear after the “what happened” answer is unveiled: Why did this happen? What made it happen? Will it happen again? Is there a trend? How do my actions affect customer churn?

One customer we worked with assumed he knew all about his business challenges. This was shown in the numbers and the charts, and everything pointed to the same pain: their valuable customers were fleeing, slowly but steadily, although the lead generators were going full throttle.

Churn-Venn-Diagram

At this point, the knowledge about customers and their behaviors was critical. But usually that means working with a lot of data and several heterogeneous sources: demographic data, old transactions, customer service interactions, browsing data from the company’s website, and responses to marketing campaigns. In many cases, in the era of the Internet of Things (IoT), the list is even longer.

Once this information is integrated and running in a fast columnar database, identifying churners is easy. The real challenge is determining if a customer is not going to come back. With this set of customers, it is possible to find the attributes that define a churner. OpenText Big Data Analytics uses a profile algorithm based on Z-Score statistical analysis and compares all the desired attributes against the set of customers.

Churn-Profile

Classifying and re-classifying data are some of the most common tasks that data scientists and business analysts do. It’s very important for companies make classifications of their customers.

OpenText Big Data Analytics offers several algorithms to classify objects, including Naïve-Bayes (recently added to the 5.1 release), Decision Tree and Logistic Regression. It is always a good idea to compare all of the available models with your data to determine which data mining technique performs better.

Churn-DecisionTreeChart

The next step after training and tuning the classification model to identify churners is to apply the model to your loyal customers – ones that have not yet fled. The classification algorithm searches for those patterns that define potential churners among the loyal customers and marks them as “probably future churners.”

Now it’s time to take care of those customers. Admittedly, taking care of customers requires time and resources, and probably the company will need to prioritize some customers above others. So why not start with the group of customers that generate a higher profit? Yes, I’m talking again about classifying customers. You will never be finished with classifying customers.

If you want to see a step-by-step simple guide on how to look for churners, try our OpenText Big Data Analytics Free Trial and find the Customer Analytics Samples that focus on analyzing and predicting churn.

After that, try the techniques with your own customer data. If you find too many churners, please, don’t panic. Just do analytics. And let us know what you find.

Isidre Royo

Isidre Royo is a Product Manager of OpenText Analytics, specializing in Predictive Analytics and Big Data, based in Barcelona.

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