Mega Telco Data Science
Devise a step-by-step plan for how the data science team should use Mega Telco’s data resources to decide which customers should be offered the special retention deal prior to the expiration of their contracts. In particular, please, explain carefully what data should be used and how. Also, how should the firm choose a set of customers to receive their offer in order to best reduce churn for a particular incentive budget.
In order for Mega Telco’s data science team to extract the correct answers to which customers should be offered the special retention deal prior to the expiration of their contracts, they must first start by segmenting the current customer base, using the data resources at hand. A proper collection of data must first be initiated and organized. Next, and in order for predictive modeling to occur, that is, be pro-active, instead of reactive, the scientists should break down the data by key demographic and social groups and identify which subset of the customers has the highest churn rate. After identifying the particular group(s), insights about each group should be obtained in order to create specific marketing messages for each of the groups. These insights may include complaints, communication, billing issues, eyc. Additionally, outside data that can be connected to company data, and that sheds additional light on the purchasing behavior and value of each subset should be sought to formulate final groups. These groups should then be prioritized based on their overall value of loss to the company, and marketed differently with a particular offer.
In order for Mega Telco’s data science team to extract the correct answers to which customers should be offered the special retention deal prior to the expiration of their contracts, they must first start by segmenting the current customer base, using the data resources at hand. A proper collection of data must first be initiated and organized. Next, and in order for predictive modeling to occur, that is, be pro-active, instead of reactive, the scientists should break down the data by key demographic and social groups and identify which subset of the customers has the highest churn rate. After identifying the particular group(s), insights about each group should be obtained in order to create specific marketing messages for each of the groups. These insights may include complaints, communication, billing issues, eyc. Additionally, outside data that can be connected to company data, and that sheds additional light on the purchasing behavior and value of each subset should be sought to formulate final groups. These groups should then be prioritized based on their overall value of loss to the company, and marketed differently with a particular offer.