Customer churn costs U.S. companies an estimated $168 billion annually. This is because small percentages add up to real money. A two percent reduction in customer attrition for a business doing $5 million in annual sales translates into $100,000 in lost revenue. Then there’s the cost of attracting new customers to replace those who left on top of growing the business.
Companies that get better at using all their data to reduce customer churn can compete more profitably. They just need some help to learn with a high degree of certainty and precision not just why customers churn, but who is churning in order to create effective retention strategies.
Whether you’re competing in B2C, B2B or B2G, here’s how AI-driven data analysis can help reduce churn.
Challenges and Limitations with Traditional Methods to Understand Customer Churn
Traditional methods for figuring out the causes or what to do about churn have made it hard to reach firm conclusions. For example, surveys completed by customers that ditch are often limited by a non-representative customer sample. At best, you end up with a best-guess hunch and start down that path.
Traditional analysis of customer data with today’s BI tools is also challenging because they limit your research to just a few possible drivers. You may miss something when what’s really needed is to find patterns across dozens of possible variables.
Custom AI models can be more reliable for handling this complexity and identifying trends, but they take a lot of time and resources to build and are often a black box. This makes it hard for your analysts to effectively explain to your customer success team what they uncover so they can do something about it.
Starting with out-of-the-box AI analysis is a way to explore customer churn data in a way that can help improve existing methods in time, investment and accuracy.
Seven Steps on How To Use AI-Driven Data Exploration To Identify And Reduce Churn
Here are seven steps for how to use AI-driven data exploration to zero in on customer churn quickly.
Step #1: Start with as much data as you can get on customers who have churned. AI-driven exploration can take on as much data as you can throw at it: pricing, sales rep, location, key event dates, promotions, customer service activity, purchase/renewal history, customer service contacts, etc.
Step #2: Select the initial target: customers that didn’t renew a contract, haven’t purchased in the last six months or whatever your definition of a churned customer is.
Step #3: Run an analysis looking at the obvious drivers (price, location or length of time as a customer) and generate a visualization of them.
Step #4: Run the analysis again looking at all drivers except the obvious ones. The computing power of AI can help you to identify and surface what’s really going on. Obvious drivers aren’t always at the root of the problem or within your control. Taking them out allows for a more actionable analysis.
For many organizations, these four steps are enough to get an unbiased full picture of the causes of churn, avoid hunch work and form a strategy to counter customer attrition. But if you want to get proactive and really understand your high-churn customers, here are a few additional steps to keep going.
Step #5: Generate a network graph of your customers. Network graph analysis takes data and plots it to reveal communities of customers. It provides visualizations that show the commonalities that exist, which groups are the biggest and what relationships exist between them.
Step # 6: Look at AI-generated profiles of the communities discovered in the network. Zooming in on the personas of high-risk customers helps you to figure out what your strategy should be and if you need more than one.
Step #7: Identify and address at-risk clients proactively with defined strategies to head off churn. That might look like a monthly report to surface clients who match the profile you’ve worked up or an AI model that pulls them forward—ideal for a high-volume organization.
Staying Ahead of Customer Churn
Most organizations already have the data they need to understand customer attrition. The missing piece might be the means to sift through it and then explain the insights with customer success teams.
For those interested in using AI-driven data exploration for this sort of job, the steps above are effective methods to start using AI-driven data exploration to get out in front of churn—and stay there.
About the Author: Michael Amori is CEO and co-founder of Virtualitics, an artificial intelligence and data exploration company. He is a data scientist and entrepreneur with a background in finance and physics, and believes AI applied to data analytics can help solve some of the world’s toughest challenges.
Edited by Erik Linask