Why Subscription Businesses Will Turn to AI to Manage Churn in the Coming Year
Arm yourself with data to help fight off subscription fatigue
With the economy shifting further and further towards e-commerce and digital services, it’s no surprise that 2021 saw a major increase in companies deploying subscription-based pricing. In particular, the subscription model has come to define the way consumers enjoy popular entertainment, from using services like Apple Music and Spotify for streaming music to an ever-expanding range of new video services like Disney Plus, Peacock and Netflix.
According to an April 2021 report from Deloitte, the average consumer maintains nine entertainment subscriptions across video, music and gaming. With popular content fragmented across competing platforms, consumers must choose the services that meet their needs within a limited monthly budget. This market saturation is leading to frustration among consumers, with more than half of those surveyed by Deloitte reporting that they find it difficult to access content across so many services.
As subscription businesses turn the corner into 2022, they’ll be forced to fight against a trend that is gaining momentum among consumers: subscription fatigue. And a trend that is gaining momentum among service providers: churn. Rather than pay nine or more subscription fees each month, many consumers are choosing to prioritize the services they use the most or those that offer the best value for money. This shift in consumer behavior is, in turn, giving rise to new technologies like artificial intelligence (AI) to meet the increasingly daunting challenges posed by customer flight and the increasingly critical need to focus on customer retention.
The Churn Challenge
As early as 2000, business experts found that customer retention would play a vital role in e-commerce success. According to a frequently cited piece from Bain & Co., a 5% increase in a company’s retention rate can drive profit increases ranging from 25 to 95%. On the other hand, the cost of bringing in a single new customer is significantly more expensive than holding onto an existing shopper.
More recently, the growing importance of customer data has provided more reason for companies to focus on retaining their best customers. With new insights into the behaviors and preferences of their shoppers, retailers and subscription service providers have made customer lifetime value (CLV) a guiding metric for their sales and marketing strategies. By maximizing the length of each customer relationship, the company enjoys the double benefit of increased revenue and decreased marketing expenses to acquire new customers.
So with retention — and AI — both becoming the name of the game, how can a subscription business optimize their operations in the coming year? Here are three key steps to applying AI to minimize churn:
1. Collecting and processing customer data: Every company will have a unique set of customer data based on the information they’ve collected throughout the sales process. Tackling churn with AI begins by cataloging and understanding each available data source, ensuring that the information is organized and accounted for. The data must then be preprocessed to clean and filter the information, separating out anomalies and arranging the data in a format conducive to analysis. For an AI solution to provide actionable insights, organizations must resist the temptation to cut corners during the tedious early stages of data collection: an algorithm is only as good as the data underpinning it.
2. Modeling and analyzing the processed data: Different data has different characteristics and requires a different approach for analysis. Whether a company is analyzing the total spend of a group of customers (numerical) or combining results based on shared characteristics (categorical), the data must be carefully inspected and modeled to produce useful insights. Of course, all personally identifiable information must be stripped from the data to achieve anonymity and maintain compliance with customer privacy regulations. Dividing the data into audiences and segments will allow the company to apply targeted marketing and sales strategies without compromising user privacy.
3. Interpreting the results and applying new strategies: To gain the insights needed to predict and reduce customer churn, the company’s data analysts must interpret the collected data. A strong customer churn model will be able to generalize the results of different data segments and apply those generalizations to potential future outcomes. Business leaders can ask questions of the model, for example, what will happen to customer churn if the subscription cost is reduced by a certain amount. The model will then determine the future results of that strategy, providing the end user with data-backed information to help drive future decision-making. As companies explore new products or business strategies, the AI-backed model makes it easier to predict the outcomes and move forward with confidence.
Entertainment and media companies shifted towards subscription models because of the predictability it brought to their operations — guaranteeing a set monthly fee from each subscriber. However, as customer behavior shifts as a result of subscription fatigue, the same companies are now dealing with new levels of uncertainty. With thousands of users in a subscription renewal cycle each month or year, companies must develop new strategies to ensure that their valuable subscribers don’t cut ties. And they can often benefit again from the enhanced predictability that can be surfaced from AI-generated insights. In 2022, we’ll see an increasing number of providers fighting over a finite number of consumers, and those companies with the strongest retention algorithms will be best positioned to survive. ■
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Vijay Sajja is the founder and CEO of Evergent, a Sunnyvale, Calif.-based provider of lifecycle management services for cloud video providers.
By Jens Koerner