Select a large retail business, and in a 2-3 page, APA 7th edition-formatted paper, address how an analytical customer relationship management (CRM) tool may help with customer churn prediction models as described in the reading and in the linked videos. How would business leaders utilize data gathered about their customers to customize and target advertising? How would prediction models help in decision-making regarding promotions to offer or other methods to help retain customers and reduce churn?
Sample Answer
Analytical CRM and Customer Churn Prediction: The Case of Walmart
The implementation of an Analytical Customer Relationship Management (CRM) tool is essential for large retail businesses like Walmart to move beyond simply managing transactions toward predictive customer strategies. Walmart, with its massive customer base and diverse channels (in-store, online, mobile), generates astronomical amounts of data that, when processed by an analytical CRM, can be used to predict customer churn, customize advertising, and optimize retention strategies.
Analytical CRM and Churn Prediction Models
Analytical CRM focuses on using data mining, pattern recognition, and predictive modeling to gain insights into customer behavior. For Walmart, this involves processing data from sources such as purchase history, online browsing patterns, loyalty card usage, returns, customer service interactions, and app activity.
How Analytical CRM Supports Churn Prediction
Analytical CRM tools provide the infrastructure and algorithms necessary to build and run Customer Churn Prediction Models. These models generally utilize machine learning techniques (e.g., logistic regression, decision trees, or random forests) to assign a "churn risk score" to each customer.