Customer retention is one of the major challenges in any industry. Many firms realize that their existing customers are their most valuable asset, and it is more beneficial to keep and satisfy existing customers than to acquire new customers. Several studies such as the research done by Frederick Reichheld of Bain & Company (the inventor of the net promoter score) suggests that increasing customer retention rates by 5% increases profits by 25% to 95%.

Due to saturated markets and stiff competition, it is very essential for Communication Service Providers (CSPs) to identify customers who are prone to switch to other networks and to develop an effective and accurate customer churn model to efficiently manage the customer relationships.

Earlier efforts on analytics to identify potential churners

With the early advancement of analytical capabilities, CSPs tried to group their customers into different segments based on a handful of parameters. The identified ‘potential churners’ were often pampered with the lucrative package and discounted rate in an effort to retain them.

However, these initial approaches were not without their negatives;

• Lack of capacity to process all possible data sources
• Analytics at individual customer level was not available
• Lack of flexibility; the analytics models were often not adaptable or easily changeable to behavioral, regional or organization level trends

An automated approach to predict customer churn

Flytxt’s Automated Machine Learning (auto-ML) is the process of automating the time consuming, iterative tasks of model development and deployment. It allows data scientists, data analysts, and business users to build inactive churn model with high scale, efficiency, and productivity all while sustaining model quality.

Flytxt’s unique Feature Engineering and Feature Selection technique enables the extraction of key variables. It is now possible to identify the potential inactive customers that are likely to churn and take measurable steps to retain them quickly with the advancement of data-driven auto-ML framework. This auto-ML framework has been validated by major telecom operators across the globe.

#2020 may tutorials #overviews #churn #customer analytics #predictive analytics #data analytic