How to Leverage AI to Predict (and Prevent) Customer Churn
By: Dan Schoenbaum
Tags: Growth Hacking, AI, Conversion Optimization, SaaS, Machine Learning

The problem is, most managers have traditionally taken a retroactive approach to addressing customer churn. They’ll make tweaks, changes, and adjustments, then look back retroactively and conduct a post-mortem as to whether or not those changes were effective. However, with recent advances in Artificial Intelligence (AI) applications, product managers are now capable of better predicting customer churn and take proactive steps to prevent it.

The Problem with the Retroactive Approach

You’ve created what looks to be a well-functioning product or app with a slick user experience, and have some initial success with user acquisition. But after a while, users start to drop off, customers churn, and you’re not quite sure why. In an effort to reduce churn, designers, developers, and product managers will try a myriad of tactics to solve the problem. They might change colors, tweak fonts, move a pay wall, or alter the User Interface (UI), and then wait 2–3 weeks to gauge whether or not turnover has improved. Based on previous retention benchmarks from weeks prior, they’ll try to figure out which change or changes made the difference. Was it one change, a few, or the sum of changes put together?

The disruption of this loop kills workflow, productivity, and overall efficiency in all departments related to improving UX. This A/B approach to testing one or two changes at a time, measuring success, selecting the best option, and moving to the next is slow, cumbersome, and inefficient. Worse, to even consider doing this right, you need to implement one change at a time, and that can take much more time than you have runway for your business.

In short, it’s a backward approach that fragments the UX improvement process, and often addresses the root cause of customer churn too little, too late.

How AI Can Solve Churn Proactively

Our team of data scientists has cráted a better, faster, more efficient approach to solving for customer churn that leverages new advances in machine learning. The real secret sauce to our approach is the way AI is leveraged in a predictive manner. The more you can forecast churn, the better you can prevent it. With machine learning models, you can understand what’s specifically causing churn. Product managers, developers, designers, and executives are spared the guessing games.

Prediction

The first step is the exploratory phase, where you take a deep dive into the data. Instead of click-stream or purchase data, utilizing Machine Learning allows you to sift through large amounts of data instead of only small sections. If you had a list of 100 users ranked top to bottom in terms of likelihood to churn, for instance, you could analyze clusters to see what kinds of people seem to be represented in the “highly likely to churn” bucket. By uncovering profile attributes such as age, gender, income, campaigns the customers came from and source of the customer, you’ll better be able to predict what kinds of customers are likely to churn (and which won’t).

Diagnosis

Machine learning can take you most of the way through analyzing data so that an analyst will be able to help the business team understand who’s likely to churn and propose preventative changes in the UI. Through Behavioral Analytics tools, you can segment users by any attribute — behavior, spending levels, age, or cohort and take appropriate action. The diagnostic step is also vital because you can quantify the risk, correct course, and put measures in place to prevent turnover from happening in the future.

Action Steps to Leverage AI

Now that you’ve effectively leveraged AI to develop predictive models for what kinds of customers are highly likely to churn, here are some specific actions you can take to prevent churn over the lifetime of your business or product:

Intervention — One of the best ways to prevent churn is to intervene in the customer lifecycle of profiles that are likely to churn. By triggering an alert to both the user and your internal team, you can focus on taking steps to retain key accounts or even specific individuals.

— One of the best ways to prevent churn is to intervene in the customer lifecycle of profiles that are likely to churn. By triggering an alert to both the user and your internal team, you can focus on taking steps to retain key accounts or even specific individuals. Acquisition — Churn isn’t always predicted just based on profile elements. It’s also predicated on acquisition channel (Google Adwords, Social Media, Content Marketing, partner referrals, etc.). Based on your predictive analysis, you can target only the most lucrative users with the best retention and LTV, and, of course, fine-tune your products for these specific customers.

— Churn isn’t always predicted just based on profile elements. It’s also predicated on acquisition channel (Google Adwords, Social Media, Content Marketing, partner referrals, etc.). Based on your predictive analysis, you can target only the most lucrative users with the best retention and LTV, and, of course, fine-tune your products for these specific customers. Experience — Color, font, user flow, and other parts of the experience are all things that ultimately impact churn. With AI and behavioral analytics, you now have the tools to know where to focus your efforts on tweaking the user experience.

The bottom line is that customer churn can’t be solved retroactively anymore. Companies, brands, and product managers need to take a proactive approach if they want to meaningfully reduce churn rates. By leveraging AI to generate a data-driven, predictive strategy, companies can take the guesswork out of where to focus their efforts for a healthier SaaS business and even gain a competitive advantage.