In D2C retail sales, metrics for success are relatively straightforward. Revenue is based on orders placed at a certain point in time, and the business can easily compare performance based on metrics such as customer spend, frequency of purchases, and recency of last purchase.

However, subscription-based models are more complex. Customers may sign up for a subscription at a certain point in time, but the associated revenue is not paid all at once. Payments are spread over a specific period, and there are uncertainties surrounding whether customers will make all payments, end their subscription early, or change their package. This complexity renders the standard D2C retail metrics less applicable.

To address these challenges, a separate reporting model is needed for subscription-based businesses. This new model considers different metrics for revenue, such as actual revenue received during a period (including existing subscription payments) and potential revenue based on the value of subscriptions created. The gap between expected and actual revenue is where the concept of 'good' and 'bad' customers plays a role.

Subscription/rental models may provide goods or services to customers before the total value is paid. In cases where a customer fails to make a payment, assessing the role of marketing efforts in attracting different quality customers, and factoring in the cost of following up on payments and potential defaults are additional elements to the reporting needed.

If we think of a common example, phone plans, the customer can subscribe to a plan that includes the latest smartphone and a set number of texts, minutes and data. The value of the goods has mostly been given to the customer already in the form of an expensive new phone, on the expectation that they will continue to pay their monthly bill until the end of the contract, at which point the phone is legally theirs.

What happens though when the customer fails to make a payment? They may be given a reminder and the payment tried again. The notion of ‘good’ customers and ‘bad’ customers comes into play. Has it been a successful sales period if we’ve doubled our customer base, but they all default on their payments? It could be possible that different marketing channels may bring in ‘better’ or ‘worse’ customers? Each of these require their own unique considerations.

It's important to understand that subscription models can vary, ranging from defined periods to open-ended contracts, which adds further complexity to revenue streams. In addition to the more standard magazine subscriptions, season ticket and hire purchase contracts, we’re seeing an increased use in subscription models in retail.  Retailers seeking to drive recurring revenue and build long-term customer relationships are embracing product subscriptions to create stable revenue streams.

An added complexity that a subscription payment model brings, is the concept of a customer applying for a subscription. In some cases this application is a simple pass through where the number of applications will always equal the number of subscriptions. In some cases where credit checks or application criteria must be met, those numbers will be different, which add another subscription reporting criteria – Submissions. 

Submissions are important, as any typical reporting metric then needs to consider this factor. Did a new marketing campaign prove to be effective? Or did that new campaign bring more applicants, but less total subscribers, meaning ROI (Return on Investment) or value is low.

Understanding the gap between expected revenue and actual revenue is key to the subscription analytics model, but we must also acknowledge the need for an immediate measure of success for marketing campaigns. Amending return on investment calculations to allow for an agreement value to give an immediate view on which marketing campaigns are working while adding a longer-term length “recognised revenue” ROI to understand the actual performance of campaigns and the revenue they delivered into the business is critical to helping decision makers understand performance and commercial viability of products, customer groups and activity.