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How to transform customer retention with analytics


Analytics can transform your business. Last time I shared with you how to transform your marketing with analytics. This time I’d like to share with you the power of analytics to optimize customer retention and how to use a Decision Management approach to give you a competitive edge.

How to transform customer retention with analytics

Conventional wisdom is that the cost of retaining an existing customer is far less than acquiring a new one. As a result, customer attrition rates are a key business performance metric in many industries from telecommunications to financial services, online retailing, Pay-TV and more. But a focus on attrition or retention rates is only half the story, you also need to focus on the cost of retention. Optimal customer retention is about retaining the right customers at the right price. And this means knowing who the right customers are as well as what will retain them most cost-effectively.

There are three steps to transforming customer retention with analytics:

Step One: Build customer understanding

Most companies have two main barriers to building customer understanding—siloed data and competing business units. Data is not organized around customers, and different channels and business units keep their data separate making analysis impossible. Business units and product lines compete for customer attention resulting in product-centric rather than customer-centric offers. Each business unit makes its own offers with little or no knowledge of the overall impact on the company.

Decision Management Edge: Companies that created and used a single view of the customer across channels and business units were able to quickly develop fine–grained segmentation and to monitor customer behavior so they could rapidly respond to developing trends.

Step Two: Establish differentiated treatments

Most companies segment their customers only by product type, by region or by original channel. When retention offers are made they are largely undifferentiated–the same for everyone. More is needed to enable true differentiation. Data mining techniques can find groups of customers who have a desired characteristic—such as a likely positive response to an offer—as well as groups who are generally similar. These descriptive analytic techniques deliver cluster or segment definitions that divide customers into meaningful groups. Combined with an infrastructure for delivering different offers based on these segments, these techniques can enable truly differentiated treatments.

Decision Management Edge:  While analytic segmentation adds to a company’s understanding of its customers, it does not automatically deliver differentiation. Companies need to ensure that the analytics drive differentiated decisions at every customer contact. The effectiveness of these decisions—what offer to make to a customer threatening to leave or which customers to proactively target—drives customer retention.

Step Three: Focus on predictive decisions

Step two used historical data to understand customers. Companies can take this one step further and use this historical data to look into the future. Predictive analytic techniques take uncertainty about the future and turn it into a usable probability. Instead of being uncertain which customers are churn risks, companies can assess the probability that each customer is such a risk. Historical data about the behavior of customers, the profitability of products over time, service cancellations and more can be used to calculate the probability of a behavior for a given customer. These techniques can estimate the likelihood that a specific customer will churn in the next 30 days, for example, or predict which offer they will find most compelling. With these probabilities in hand, customer treatment and retention decisions can be made more accurately, creating offers that are more likely to be accepted and targeting those customers at greatest risk.

Decision Management Edge:  Companies who developed predictive models to find their most desirable and at-risk customers and used those models to improve decisions at every touch point were able to focus retention efforts and uncover new paths to increased revenue.

By building customer understanding, establishing differentiated treatments, and focusing on predictive decisions, companies like yours can make better customer retention decisions. Every decision is differentiated, based on what has worked in the past as well as what is likely to work in the future. Every decision is based on the customer and on what worked with similar customers. Every decision focuses on retaining the right customers at the right price.

Each step builds value – using integrated customer data improves programs at the macro level, differentiating treatment makes your offers more compelling, adopting predictive analytics target based on future value and future risk. Companies are already using these approaches to transform their retention strategies and drive value to their bottom line. This is not a future vision but a practical way to make improvement today. And Decision Management gives you the edge in quicker realized value of analytics implementations.

These insights come from my research and are based on the collective wisdom of over 50 companies that have transformed their business using analytics. These companies tackled core business issues like customer retention, marketing, service delivery, operations and customer centricity, in industries as varied as Telecommunications, Retail, Healthcare, Education, Government and Banking.


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