The “Use It or Lose It” dynamic in the Subscription Economy means that if your customers don’t use your service, you’ll lose the customer, the renewal and the revenue. The relationship between subscriptions and usage illustrates this dynamic well. If a customer’s usage meets specific thresholds, the subscription revenues are retained at renewal. If usage climbs enough, subscription revenue growth can be generated by a new subscription tier or more seats. Similarly, customers with the most usage tend to expand their subscriptions to more complementary products.
On the other hand, if a customer’s usage drops below certain thresholds, the first thing that occurs is tier or seat churn, which reduces subscription revenue. Ultimately, continued drops in usage lead to product churn as the customer decides to use less of the service. Finally, customer churn occurs when usage falls so low that regardless of price the subscription is not justified.
So how much usage is enough to retain subscription revenue? To grow subscription revenue? To cross-sell other products? And at what point does usage decline create tier or seat churn? Product churn? Customer churn? And are the usage thresholds the same for every product and every rate plan? And finally, how do you measure usage: by active user, by artifacts created, by transactions, by report views? The only way to know the answers to these questions is to collect and then analyze usage data in the context of subscription data.
Usage data is simply the data recorded about usage. Its volume, variety, and velocity make it difficult to harness. The volume of usage data includes billions of user events to be analyzed for correlations and insights. The variety of usage data ranges from web events, mobile application events, call center activity, and other customer interaction points. And the velocity requires millions of new usage events being recorded and responded to every day. To complicate things more, usage data is generated and stored in systems that are separated from the subscription data, so attributing (i.e., linking) usage to the right rate plan, product, subscription, and customer can be tricky.
As part of Z-Finance Open, Scout Analytics automates that integration of usage and subscription data. Scout Link for Zuora links usage data managed by Scout Analytics with subscription data managed by Zuora. The result is predictive analytics utilized to drive revenue growth. The integration of the two sets of data allow correlations between usage and subscription retention, growth, and churn to be identified and quantified. Consequently, as usage data accumulates:
- Trial prospects can be segmented by those most likely to close
- At-risk customers can be identified early to prevent churn and retain revenue
- Renewals can be classified as cross-sells and up-sells to grow revenue
- Rate plan prices and packaging can be modified to optimize revenue
In other words, predictive analytics made possible by Z-Finance Open and Scout Analytics can be used to optimize subscription lifecycles and customer lifetime value. By linking usage data to subscription data, you can quickly and easily identify the correlations on which trials are most likely to close, which customers are at risk, where the most up-sell potential exists, and what are the best cross-sell opportunities – i.e., you can optimize the subscription lifecycle. Predictive analytics derived from the combination of usage and subscription data allows a company to know what actionable pricing and customer management opportunities exist and align their resources to optimize revenue growth in the Subscription Economy.
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