How AI Helps Address Customer and Employee Churn

Even though churn is recognized as one of the most persistent business problems, most organizations haven’t yet developed mitigation approaches or tried AI-driven solutions. In today’s data-driven world, traditional approaches to churn mitigation don’t work: consumer and employee behavioral patterns change too rapidly and apply to smaller and smaller cohorts. AI can help businesses deliver granular consumer and employee insights and drive highly targeted churn intervention tactics. 

Successful churn prevention methods can have a significant impact on the bottom line, as well as the cost of doing business. The cost of acquiring new customers can be up to 6-7x higher than retaining existing ones.¹ As for employees, with the Great Resignation seemingly continuing, employee retention is still an important operational imperative, as costs to replace seasoned and well-adjusted employees might be too high for some businesses.²   

In this environment, it’s important that businesses address churn in two ways. First, gain both a true understanding of the churn rate and its causal patterns. Second, implement AI to discover both the insights and techniques that will help create a solution to lower customer and employee churn.

How AI Can Deliver Granular Churn Insights

Churn analysis typically involves using a set of statistical approaches to identify customers or employees likely to churn and applying appropriate interventions to mitigate this risk. However, because interventions are traditionally applied at a high-level to entire groups, they are often not specific enough for individuals in those groups to be effective. 

These interventions can also be expensive (or simply inappropriate) when delivered in large quantities. For example, blanket discount offers wouldn’t always work for customers about to cancel their subscription. Some of them might be interested in more specific offers, like bundles or additional features or maybe even specific content.

Voluntary employee turnover alone costs the U.S. economy a trillion dollars a year.³

This lack of detail and visibility is why many organizations are turning to AI, as it helps organizations move away from general approaches and create granular intervention tactics, appropriate for smaller groups or even individuals. Machine learning and AI enable organizations to work through incredibly large datasets at high speed, delivering deep analysis of data, in all of its various forms, to find the factors that predict churn and highlight people at risk

A good churn prevention solution isn’t just built on predictive models, though. You also need to have clear prevention plans for when an individual is determined to be at-risk – and it’s incredibly important to get feedback from business stakeholders on the features and patterns your model can act on, and the mitigations it can realistically offer. For example, if commute time is identified as a risk factor for employees, can you offer remote working to any employee or only those in specific locations?

Improve Churn Mitigation with the DataRobot AI Platform

Сhurn prevention is a  popular use case among DataRobot customers across industries. For example, D&G, one of the leading insurance providers in the UK, uses DataRobot for pricing optimization to determine the price point where customers are most likely to be happy with the warranty coverage they receive and renew their policies. There are many other churn-focused use cases, like media subscription renewal forecasting or clinical trial churn predictions.

Whether you choose expert advice around specific churn use cases or develop your own models from scratch, you benefit from the DataRobot platform:

Enterprises address churn with the DataRobot AI Platform and see multiple benefits. 

• Achieve higher machine learning model accuracy. The only way to judge the performance of a predictive model is to assess the cumulative lift – the improvement in the precision of your interventions. To do this, you need to a) have established a clear baseline, and b) be able to clearly understand the improvement you’re seeing. And while it sounds obvious, not all tools make this easy. With DataRobot, you have access to out-of-the-box evaluation techniques on each model, like  Lift Chart, and ROC curve graphs, which enable you to validate the model’s effectiveness and how it is performed.
• Improve engagement from business stakeholders. Involving business stakeholders or domain experts is critical to developing a resilient and reliable churn prevention solution. DataRobot AI Platform offers a  highly intuitive, graphic-led way to engage the teams that will make your churn prevention strategy a success. 
• Understand the impact of your data with feature impact graphs, which rank all the churn features appearing in the model, and make it easy for you and your experts to identify if they are valid, or if they are artificially influencing the predictive capability of the model. Tweaking this enables greater accuracy.
• Achieve granularity of insights with prediction explanations, which show you the reasons why the model has suggested someone is at-risk, enabling you to compare it to the information you have from outside the model. For instance, if an employee’s job role has a high prediction rating, does HR already know of issues within that team?

Start Developing Churn Predictions with AI

Although churn is an inevitable part of running a business, DataRobot helps organizations create strategies that can quickly and effectively transform churn mitigation.

DataRobot provides you with the tools necessary to create a deeper understanding of churn factors that can lead to a robust plan for combating it. You’ll be able to validate predictive models before you deploy them, and use DataRobot features to keep stakeholders in the loop.

Learn more about how DataRobot is helping organizations.

 ¹ American Express, Retaining Customers vs. Acquiring Customers

 ² Computer World, The Great Resignation isn’t over yet

 ³ Gallup, This Fixable Problem Costs U.S. Businesses $1 Trillion

About the author

Atalia Horenshtien

AI/ML Lead - Americas Channels, DataRobot

Atalia Horenshtien is a Global Technical Product Advocacy Lead at DataRobot. She plays a vital role as the lead developer of the DataRobot technical market story and works closely with product, marketing, and sales. As a former Customer Facing Data Scientist at DataRobot, Atalia worked with customers in different industries as a trusted advisor on AI, solved complex data science problems, and helped them unlock business value across the organization.

Whether speaking to customers and partners or presenting at industry events, she helps with advocating the DataRobot story and how to adopt AI/ML across the organization using the DataRobot platform. Some of her speaking sessions on different topics like MLOps, Time Series Forecasting, Sports projects, and use cases from various verticals in industry events like AI Summit NY, AI Summit Silicon Valley, Marketing AI Conference (MAICON), and partners events such as Snowflake Summit, Google Next, masterclasses, joint webinars and more.

Atalia holds a Bachelor of Science in industrial engineering and management and two Masters—MBA and Business Analytics.

Meet Atalia Horenshtien