Imagine that you had more insight into the underlying drivers of churning customers. In other words, that you would be able to identify why a customer is ending its relationship with the company. Think of yourself as the head of a banking branch for instance. Picture that your bank office is affected by long waiting times due to a shortfall in capacity of your employees and that this inconvenience has made customers reconsider their bank choice. This means that a strong relationship exists between your bank office’s waiting time and customer churn. Without any churn analysis, such correlation remains undiscovered and only pops up once customer base decline starts hurting revenue. By linking operational data to customer behavior on the other hand, you are able to discover such detrimental patterns proactively. This is the true strength of advanced customer analytics: exposing areas of concern in customer behavior before it starts hurting the numbers.
But that is not where it ends. Besides operational data, many other data sources such as demographical, macro-economic and web data can be integrated in a churn analysis. Moreover, many other customers across other branches may show a churn behavior pattern that is very much alike. The more data points you possess on customers and the touchpoints in their journey, the higher your chances are of identifying such trends. With the help of a machine-learning algorithm, all this data can be processed so that you are no longer only capable of analyzing churn patterns of the past, but also of predicting customer churn behavior for the future!
The question that remains however is how such prediction can create tangible value for your company. A specific case might make it crystal-clear for you. Since 2005, Coyote, the French leader in real-time road information, has been highly dependent on the amount of customers that engage with their device. In particular, by increasing their number of users, and thus collecting more data, the company succeeds in delivering a service that is more accurate and performs better. This is why customer retention and churn rates have proven to be crucial for the company’s business model. As a result, Coyote decided to team up with an external partner in order to boost its retention figures through user behavior modeling. After integrating customer devices data as well as user data into a predictive model, the tandem managed to accurately map out churn behavior on a massive scale. Based upon the resulting insights, Coyote rolled out an outbound call campaign for about-to-be churning users of their product and got an 11% increase of retention rate in return.
Despite the proven effectiveness of customer churn reduction, most present-day companies tend to spend the major part of their marketing budget on the customer input side by focusing exclusively on acquisition rates. Perhaps, marketers feel most comfortable in presenting graphs that show the number of new customers going ‘up and to the right’, as this is generally perceived by managers as the epitome of excellent marketing work. Yet too often, when churn rates are overlooked, this leads to a flawed reporting. It may very well be that for every new client, three are leaving through the back.
Even more so, from a financial point of view, extensive research shows that the acquisition of new customers costs 5 to 10 times more than selling to a current customer. In addition, current customers spend 67% more on average than those who are new to a business. This shows that in terms of customer analysis, it is important for your business to consider the overall picture through a combination of both acquisition efforts as well as retention optimization.
That is where The Reference comes in. We are a digital agency that is focused on driving true business value through a hands-on yet purposeful approach. Our dedicated data team aims to guide businesses from scratch to hatch in the process of converting raw data into tangible business results. Whereas others in the market may get disproportionally excited by a negligible increase in predictive accuracy for the sake of personal achievement, we are driven by the enablement of marketing teams to make well-informed decisions based upon data-driven insights. Hereby, the ultimate aim is to make efficient use of data in order to take your business to the next level while leaving competitors in the dust.
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