Subscriptions are still growing and innovating, with the subscription box industry projected to create $40 billion in revenue this year, according to Subscription Summit organizers.
Of course, that means a fair amount has been written about subscriptions, even more perhaps specifically about the value of data and analytics in the world of subscriptions. But most of that information targets the ways data and analytics can fine-tune customer acquisition and inform decisions around price or products.
Acquisition is only half the battle. The understanding of customer behavior and retention data are key for establishing a profitable business. Churn — when customers leave a subscription program — is to be expected. But plenty of newcomers to the space are shocked when they see real churn rates, and they don’t know what data can help them make decisions to successfully battle it.
Without getting overly technical, “churn” is a mathematical depiction of an attrition curve, typically starting out steep and flattening after a few shipments. The tail of successful subscription programs can extend for years beyond a brands’ acquisition programs. The attrition curve should be reasonable; when it drops steeply without flattening out, it becomes a symptom of potentially larger issues.
As big brands such as Adidas “Avenue A,” Starbucks’ “Reserve Roastery” and Proctor & Gamble’s “Gillette on Demand” continue to enter the new membership economy, they will be looking more closely at how to retain customers — and data will be the key.
Dissecting the causes of churn.
Before a business can fight churn, it’s important to know why churn happens. The most basic cause is that the product isn’t attractive, becomes less attractive over time or loses value for the consumer. When consumers open their boxes and are underwhelmed by the product selection, are faced with lackluster quality or discover the price isn’t worth the experience, logical subscribers will leave.
Another potential issue involves trial offers. We’ve all seen one-time trial subscription enticements offered for free or a steeply reduced price. The first order is a no-brainer for the customer and gets people to take the plunge. But is the actual offer exciting enough to persuade customers to keep paying for future shipments at full price? About 80 percent of consumers should stay past the initial shipment — if the number dips significantly lower, the trial isn’t inspiring members to remain for the next shipment.
An often-overlooked cause of churn is a company’s inability to keep subscribers active when something goes wrong with their payment sources. A longstanding customer’s credit card can expire or be declined for multiple reasons, but that issue alone should not automatically make them miss a shipment or indicate that they don’t want to continue the relationship. Sometimes bad things happen to good credit cards.
Many subscription businesses see increased churn because they don’t employ enough data, technology or effort to resolve payment issues, but it’s worth the battle. A tough but worthwhile solution is “credit extension,” in which companies with enough data can determine whether shipments should continue even if the card on file doesn’t clear immediately.
Ultimately, churn is costly. An unusually high churn rate will kill ROI, making it tough for an entrepreneur to recover losses from potentially higher acquisition costs. At the same time, when you experience a reasonable churn rate and the membership base stabilizes, you can expect significant cash flow from long-term subscribers. There will be recurring revenue and no acquisition cost against these cohorts.
Winning the fight against churn starts with data, analytics and intelligence. Data should be a key source of all decision-making, and actions based on deep analytics will always make the difference between profit and loss. Sophisticated subscription models are teeming with data from all the touchpoints and behavior information available. From the moment of acquisition to the final transaction, multiple layers of detailed information are just waiting to be collected, organized and analyzed to beat churn.
Which sets of data are most useful as is relates to combating churn? Standard recurring revenue metrics such as cost per order, average order value, and lifetime value are helpful for a business overall, but they don’t really help predict or give tools to combat churn.
Businesses that want to drive positive actions going forward should consider collecting data points such as acquisition information, customer demographic information, how often consumers return a product or request refunds, how often a subscriber interacts with customer service, and how regularly consumers engage with social media and open marketing emails.
Armed with that data, businesses can start to see which groups of people might be more likely to cancel and can take actions to keep churn rates manageable. The data will also give insight into which acquisition channels and media businesses need to avoid and which they should invest in more heavily.
After you collect your data, there are a few strategies you can implement to combat high levels of churn: