If you are running a subscription service, recurring revenue is without a doubt the most important metric to keep track of. What are the key contributing factors?
A decomposed recurring revenue, as illustrated below, provides a holistic view over the direct leavers of its growth. It allows you to:
In this article, we will use a monthly subscription service as an example and peel Monthly Recurring Revenue (MRR) like an onion into detailed and directly manageable metrics. We will also discuss various data projects that help optimise at each point.
|Component||Goal||Data Projects||Responsible Team|
|# Trials Started||Acquire new users.||Marketing ROI optimisation
|% Trial Conversion||Maximise conversion from trial to subscribers.||Conversion dashboard
|Subscription Retention||Keep subscribers happy and retained.
|Retention and feature dashboards
Churn propensity model
Natural Language Processing (NLP)
|Pricing Strategy||Determine the optimum price vs. conversion point.||A/B tests
Multi-armed bandit (MAB) algorithm
The first layer is simple. MRR is the direct product of the number of active subscriptions and their monthly price.
Number of active subscriptions can be first broken down into new and existing (excluding cancelled) subscriptions, each with its own tuning factors.
This metric sits on the acquisition side and is usually managed by the marketing team for consumer products and services. Below are some subcomponents to consider:
During the trial period, make sure all users get to experience what makes your product unique so they reach the Aha! Moment, when they realise the value it provides hence convert to paid subscribers.
One way to pinpoint the Aha! Moment is to conduct historical analyses over the behavioral patterns between trialists who do vs. do not convert to subscribers during the trial period. List out all your unique features and track their usage. Key metrics to consider per feature:
Once users convert to full paying subscribers, all efforts should go into retaining them and prevent churn. We discussed retention metrics in detail in a previous post here. Key things here are to:
Most services have a standard and fixed pricing structure. However, if you have a degree of flexibility to adjust your pricing dynamically (e.g. by market segments and / or time-based), explore different data strategies to optimise your results. If you have a good volume of traffic, conduct A/B tests for optimum price / conversion ratio. You can also use this approach during the pre-launch of a new country to determine the best fixed-price. Make sure your test traffic is significant in size and representative of your user base.
Multi-armed bandit (MAB) algorithm is another solution to maximise rewards systematically. Pre-define a list of pricing options and let the model find you the best outcome. We introduced it in a previous post here.
We decomposed recurring revenue to a list of components to help you understand the drivers of its growth and how data can help. We would love to hear your thoughts and suggestions, please get in touch. We are always happy for a chat!
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