Predictive Analytics

Avoiding churn with customer analytics

By August 16, 2016 No Comments

One of the most important aspects of working in any business is keeping strong relationships with customers. Many companies have some level of churn that occurs at any given time. There's no way to completely eliminate turnover, but it's in an enterprise's best interest to at least minimize it. By having strong retention rates, it's possible to mitigate costs overall. Shreesha Ramdas, in an article for CMSwire, found that the cost of keeping customers was about 12 to 18 cents per dollar of revenue. In comparison, getting new customers costs $1.07 per dollar of revenue. Custom BI solutions can help businesses retain customers by focusing on predictive analytics.

Utilizing statistics to understand exits
Using modeling to analyze churn is nothing new to businesses. Car companies, for example, have trajectories on when parts are likely to fail so that they can tell vehicle owners when they should visit a repair shop to make adjustments. What makes predictive analytics so useful is the rise of a variety of data points in conjunction with modeling to answer a variety of questions at once. Customer turnover has these in various different forms: support ticket levels, number of returns, quality of client communications and more. All of these lead to creating general ideas of when and where an individual or business may lose interest.

Analytics allows businesses to think beyond the question of "what if."

Moreover, analytics helps businesses think beyond the multitude of scenarios that can occur in any situation. Just as much as it can answer the "what if," it can figure out how to answer for any cause of churn as it appeared {wording is a little confusing}. In this way, a company can adapt quickly to customer relationships turning sour without losing control of the situation. It can then think outside the usual tactics of providing discounts to better serve a customer and help them stay longer.

Many analytics platforms help deal with churn by creating their own formula and mechanisms to better serve the interests of businesses. Consider the Watson Analytics platform. IBM noted in its Watson blog of a sample data set companies can use to build their own customer turnover analysis. It allows for data points from customers who left, the products and services they signed up for, account information and demographics. From there, a company can assess key factors that drive a customer away, from early problems in a contract to issues that appear in semantics filters of emails. This helps identify individuals or firms that are at risk of leaving and presents opportunities to help them stay.

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