A Guide to Customer Churn Prediction for Telecom Enterprises

Standing in the year 2023, it is safe to say that the information age is truly upon us. But alas, the telecom industry is in a never-ending turmoil owing to the sky-high competitive rivalry and the need for its enterprises to remain financially viable. As a matter of fact, that’s not even the end of the story.

With the emergence of this new age of information comes the ever-haunting phenomenon of customer churn, threatening telecommunication industries across the globe. It denotes the process by which a customer seizes their service with one provider and switches to another in the same industry. Sometimes, customers churn because of network quality issues and a lack of adequate features and consumable content. Other times, the high subscription costs, followed by discounts and special offers from competitors, resulting in the loss of buyers.

In this fight for survival, enterprises are now speeding toward customer retention over customer acquisition.

Let us add some context and assume a customer who pays $5 a month for a subscription. Now, if that price were to go down to $3, there is a good chance that the customer would consider paying the nominal fee in hopes of one day watching a show. Rescuing these small segments of subscriptions every month can help generate significant revenue over time.

But this, too, comes with a catch. One of the noteworthy yardsticks for ensuring customer retention is foreseeing which consumers are likely to leave and for what reason. This takes us to the million-dollar question…

How do telecom operators forecast customer churn?

The answer to this is fairly simple – a robust churn prediction model.

Enterprises that rely solely on customer feedback often tend to overlook related variables conditioning the churn. And that’s where a need for a specific methodology comes in, which can work on vast repositories of customer data and deliver insightful snippets into the behavioral patterns of consumers who left.

However, just like any other technique, getting a grip on the customer churn model can seem like an ordeal at first. This makes it the need of the hour for telecom companies to partner with innovative educational spaces pertaining to the applications of data analytics. Moreover, the step towards upskilling and cross-skilling talents can also help imbibe critical thinking and problem-solving skills across all organizational levels.

LEAPS, Analyttica’s experiential learning platform, delves deeper into a case study with simulated data that closely resembles real industry data. It provides an opportunity to upskill one’s team on the customer churn model for the telecom industry. To know more, visit us at https://leaps4enterprise.analyttica.com/cases/9540.