Telecommunication businesses have seen the adverse effects of customer churn on company revenue. With the challenging dynamics in the telecommunications industry, reducing churn in the customer base should be among any telecommunication company’s top priorities.
Companies should not only spend their valuable time and resources probing for new customers, they must keep their existing customers happy as it impacts their lifetime value and safeguards their revenue.
It costs 5 times as much to attract a new customer as it costs to keep an existing one.
According to various studies, acquiring a new customer is anywhere from 5 to 25 times more expensive than retaining an existing one. Today’s telecommunication companies are doing everything they can to retain their existing customers, but customer churn is still high.
Customers want great service, but surprisingly, companies aren’t delivering service that exceeds or even meets their expectations. This results in low CSAT and lower NPS scores, ultimately leading to churn.
In such scenarios, what companies require is an advanced way to predict churn risk as early as possible in the customer lifecycle stage. This is possible, especially with the latest advancement and adoption in AI technologies, and such solutions can help you understand your customers’ needs precisely.
Key business challenges for retaining customers
- Identifying root causes of customer dissatisfaction.
- Difficulty in identifying high-risk customers before it’s too late.
- Creating personalized offers to at-risk customers.
- Analyzing customer interactions will help in identifying the root causes of customer dissatisfaction.
- Tracking the customer journey in real time across the customer lifecycle will help in understanding customer behavior and predicting their risk propensity.
- Segmenting customers into various groups based on their behavior/needs will help in creating personalized offers to at-risk customers.
Though telecommunication companies have been putting in great efforts, they are still unable to conquer the churn battle because they aren’t leveraging the bulk of the unstructured data hidden in their CRM.
One of the reasons for this could be that most of the companies have been using traditional churn prediction models that have been heavily dependent only on the data gathered from transaction histories and demographics, but this method fails to integrate dynamic customer-generated input with real customer needs, wants, and wishes.
So, having a churn prediction model that can leverage customer interactions and is built on the latest technologies like machine learning and text analytics will help companies in analyzing the customer interactions at scale to understand customer behavior.
This helps companies gain key insights about the customer that when effectively utilized will drive a 2x faster reduction in customer churn and significantly boost company revenue.