An Overview of Customer Retention Analytics
Martech Outlook | Friday, September 22, 2023
Customer Retention Analytics increases profits and reduces costs, saving time and effort by making informed decisions, engaging customers, and delighting them.
FREMONT, CA: A business's actions and strategies to keep existing customers refer to customer retention. In order to achieve these goals, customer retention analytics provide predictive metrics about which customers are likely to churn - enabling them to stay ahead of the curve.
Analytical benefits for improving customer retention are noted below:
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Contributes to sustainable growth: Maintaining existing customers allows for more sustainable growth. Retaining existing customers makes the most business sense, but it's not quite that straightforward. Analyzing customer retention is one way many companies gain a competitive edge.
Acquires customers at a lower cost: Keeping the present customer is cheaper than acquiring a new one. Keeping an existing customer is five times more economical than attracting a new one.
Keeping in touch with loyal customers is crucial, as they are extremely valuable. If organizations know why some customers leave and why others stay, they can take the appropriate measures to retain them.
Upsell/cross-sell opportunities are easier: Existing customers are much easier to market to and sell to. The cost of acquiring a new customer is usually low when selling a new product or service to an existing customer base.
The types of customer retention analytics are as follows:
Predictive Analytics: The most common method is this one. In predictive analytics, models are used to predict what might happen in a specific situation in the future. Analyzing next best offers, churn risk, and renewal risk could be included in this analysis.
Prescriptive Analytics: It can help determine the best future solution among a variety of options, suggest ways to take advantage of future opportunities, or illustrate the consequences of each decision. The next best action and next best offer analysis are examples of prescriptive analytics for customer retention.
Descriptive Analytics: Although not always the best value, it can be useful for uncovering patterns within a certain segment of customers. Businesses can gain insight into what has happened historically and uncover patterns and trends to investigate further using this technique. The analysis of market baskets uses descriptive analytics such as summary statistics, clustering, and association rules.
Outcome Analytics: Outcome analytics, also known as consumption analytics, provides insight into customer behavior. Consumption patterns and associated business outcomes are the focus of this approach. It is suggested to learn how the customers use various products and services to better understand them.
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