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How retail banks can prevent churn with a deeper understanding of the connected consumer

 
July 10, 2017

The digital world, woven together by connected networks, has created a culture of constant connections. Consumers have access just about anything they needanytime, anywhere. The result is that connected customers are more empowered than ever before. They have infinite choices and channels. They expect every touch point to be consistent, personalized and proactive. They expect any product or service at a moments notice, answers to questions anytime, and seamless experiences across their physical-digital journeys.

While all industries are or will be affected by digital disruption, one of the industries most impacted is retail banking. As Brett King, CEO and founder of Moven, fintech influencer and futurist, states on his site, Breaking Banks, technology and customer behavior will bring about more changes in banking in the next 10 years, than in the last 200 years.

Customer loyalty in this time of constant change is difficult to maintain. Digital technologies that spurred the development of the connected customer have made it easier for customers to switch brands at a moments notice. One poor experience and customers are very likely to take their business elsewhere.

Traditional churn prevention

To prevent customers from taking their business to a competitor, retail banks often use churn-predicting models to identify risk patterns in customer behavior and take corrective actions. They use a traditional approach that analyzes customer activity looking for indicators such as a significant decrease in assets in a customers accounts, a drop-off in payments and transactions and a decrease in engagement indicated by low email and newsletter open rates. Based on the customer activity, predictive models are used to detect high-risk customers and target them with a new offering or promotion.

A smarter way to reduce churn

What is missing in the traditional approach to churn prevention is a deep understanding of the connected consumerbehavior, motivations and overall profitability.

A more effective and profitable way to manage churn is to combine churn-predicting analysis with a 360 degree view of the customer that includes enriched profiling, persona models and customer scores such as: experience, sentiment, Lifetime Total Value (LTV) and profitability, churn, risk and Recency, Frequency, Monetary Value (RFM) scores.

Customer analytics software that takes both churn probability and customer LTV into account and delivers recommendations for personalized Next Best Offers and Next Best Actions based on deep customer insights can help banks retain valuable customers and realize maximum profit from their retention programs.

It is a well-established fact that acquiring a customer is far more costly than keeping a customer.

In this world of constant change and endless connections, it is critically important for retail banks to use smarter retention programs to keep their valuable customers happy, engaged and loyal.

More information: Learn more about how to retain high value customers and respond quickly to competitive threats –Customer Intelligence & Insights Solution for Banking and Financial Services

Kathleen Holm is Marketing Director of the TCS Digital Software & Solutions (DS&S) Group. She has more than 25 years of experience marketing technology software and services to enterprises worldwide. She leverages her extensive background in enterprise software technology to help organizations develop effective marketing strategies, create targeted messaging and positioning, and implement effective go-to-market plans to improve corporate performance. Prior to joining TCS, Kathleen was a Senior Principal of technical product marketing for Oracle Fusion Middleware where she was responsible for defining the marketing strategy based on industry maturity and customer trends. She also held positions at IBM including Market Manager for WebSphere Developer Programs, Market Manager for Tivoli Integrated Service Management and Tivoli Brand Specialist. Prior to joining IBM, Kathleen worked with four high-tech startups.