Tackle customer churn with SPSS Modeler
It's well-established that customer retention is much less expensive than customer acquisition. According to Harvard Business Review (HBR), landing a new customer costs five to 25 times more than keeping one. And, says HBR, if you can reduce customer churn by five percent, depending on your industry or sector, you can increase profits by as much as 95 percent. Preventing churn should be a priority for nearly every company.
A vital part of a viable strategy for preventing churn is the effective use of data. Let’s look at the role predictive analytics can play in that strategy.
Customer retention and the effective use of data
Once a customer leaves your business, there’s little you can do about it. Analyzing customer data and acting on insights enables you to build strategies for keeping your customers. If you carefully study the data of customers you lost and satisfied customers, it’s possible to ascertain what causes customer dissatisfaction as well as customer happiness. You start by finding, analyzing and sharing the information you need to improve decision-making with query, reporting, analysis, scorecards and dashboards.
Then, it’s time for predictive analytics.
Predictive analytics: One antidote for customer churn
Half the battle of reducing churn is discovering what might be causing it so that your business can adjust its strategy and create offers or methods that prevent it. Predictive analytics can help you to discover hidden patterns and relationships in data so you can forecast behavior and significantly improve decision-making.
In the case of customer churn, you can use predictive modeling to explore the reasons why customers chose to move to one of your competitors. Once key indicators are identified, the customers may be sorted into a ranked list of high probability to churn down to the least likely to churn—these are the loyal customers. Such information can be used to actively monitor customer behavior and usage patterns and can highlight those who may be thinking of migrating to another provider.
You might think that building predictive models is difficult and time-consuming. But that is not necessarily so. An intuitive, visual data flow process of analysis can make it easier to create models that tackle the question of customer churn. Enter IBM SPSS Modeler, a leading visual data science and machine-learning solution.
Time for a tour of IBM SPSS Modeler
Visual predictive modeling with SPSS Modeler starts with GUI-based data science and machine learning algorithms. SPSS Modeler is a drag-and-drop data science tool that empowers business users and data scientists alike to prepare and explore data and build predictive models. You can also build machine learning models. Machine learning can help you train on data sets before being deployed on your data. Some machine learning models are online and continuous. This iterative process of online models leads to an improvement in the types of associations made between data elements.
You can see how to build a machine-learning model to predict customer churn by taking a product tour of the SPSS Modeler 18.2 interface. In a few simple steps, you’ll inspect and prepare a customer churn data set, train a machine-learning model, and evaluate model performance. At the end of the tour, you’ll have a gains chart that displays your model, showing you how you could identify patterns that reduce churn at your business.
Hop on the churn reduction tour
If you’re ready to see how to address customer churn with a machine learning model, you can get started on your SPSS Modeler tour here.
IBM SPSS Modeler is available as a stand-alone offering or as part of IBM Watson Studio. For more information about how predictive analytics can help your business, the SPSS Modeler page is a great place to start.