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The Science Behind Customer Churn Customer-loyalty and retention is paramount in the online marketplace. Here's how to get the most out of Big Data and keep your customers coming back.

By Luc Burgelman Edited by Dan Bova

Opinions expressed by Entrepreneur contributors are their own.

Betsie Van Der Meer | Getty Images

Every business puts the customers first, which means today's consumers are unequivocally in the driver's seat. With fewer and fewer incentives to stay loyal, all it takes is one less-than-positive interaction and your customer can easily move on. The reality here is that it costs significantly more money to acquire new customers than retain existing ones, and it costs far more to re-acquire defected customers. That's why you need to place major focus on reducing churn in 2016, if you're not already.

The key issue: knowing your customer.

In order to identify early signs of potential churn you first need to start getting a holistic view of your customers and their interactions across multiple channels such as store/branch visits, product purchase history, calls to customer service, Web-based transactions, and social media interactions, to name a few. Let's take a look at the banking industry for example.

Related: Decoding Big Data for Business Growth

The ability to track customer sentiment gives banks early indicators into customer service issues and/or opportunities for greater engagement. It allows banks to be proactive in improving the customer experience and to continuously monitor engagement with the brand, which can highlight potential indicators of possible churn, such as:

  • Recent dramatic decrease in the assets in a customer's accounts.
  • Cancellation of automatic incoming credits or outgoing payments.
  • Negative customer interactions on customer calls.
  • Drop off in Web-based banking activities.
  • Drop off in mobile payments and the value of mobile transactions.

However, the information about a customer's sentiment and their experience across multiple channels lies in many structured and unstructured data sources. The information could be in the form of logs from a customer's bank visits or call center, website interactions, Tweets, Facebook interactions, community forums, customer emails, and customer surveys. Disparate and enormous fast moving data stored in silos makes it challenging for banks to get a holistic understanding of their customers, understand the shift in the sentiment or detect early warning signs and proactively engage them with retention or cross-sell marketing offers.

Related: Learn Nontraditional Ways to 'Know Your Customers'

How big data can help you predict potential churn.

The sheer volume of customer data available to companies has made it almost impossible to store, analyze and retrieve meaningful insights using traditional data management technologies. But now, big data can help you solve these challenges and allows you to leverage both structured and unstructured data from multiple channels.

Native big data technologies solve the data management challenges by storing, analyzing and retrieving the massive volume and variety of structured and unstructured data, and scale elastically as the data grows. Additionally, sophisticated data matching capabilities allow you to eliminate the data silos, connect the dots of a customer's interactions across multiple channels, and build a comprehensive holistic customer profile.

Related: 3 Ways to Monitor Customer Churn

Now what?

The question remains -- is this holistic view enough to predict potential churners in an efficient manner? It all comes down to changing the way we work. Introducing newer technologies can help us, but won't do much if we stick to the traditional ways of thinking. It's only doing more of the same -- with more data and more people.

Traditional approaches meant building models to arm you with the insights to identify the high-risk churn targets. But very often you will come to discover that after the damage is done. So you must go beyond accumulating insights and, instead, apply your customer analytics to your process. That means in real-time, on an individual level, and in the process of your customer interaction. Doing so will help you pinpoint exactly where and why they turn, and what their next move is and enables you to target them with relevant messages or offers at exactly the right time to prevent churn.

Ultimately, customer intelligence management and use of real-time big data and machine learning gives companies a distinct competitive advantage with the ability to prevent churn, drive cross-sell, and build customer loyalty. After all, smart businesses know that the first purchase is really just the beginning, and that the real business value lies in retaining that customer.

Luc Burgelman


Luc is a co-founder and the CEO of NGDATA, a bid data analytics and enterprise data management company. Prior to NGDATA, he co-founded Porthus, which delivered cloud-based solutions that IPO’d in 2006 and was acquired by Descartes Systems Group in 2010. 

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