InsightSquared cites an ideal benchmark of around four for rapidly expanding products. The reason is that it helps you determine the ‘acceptable’ level of churn that your new growth can sustain without flipping revenue upside down.
But that’s just a rough estimate depending highly on your own unique business model and product. So start by raising your own historical trends, first, before striving to match this external benchmark.
Average Revenue Per User (ARPU) is what it sounds like: Total MRR divided by active customers.
Once again, higher is better. But it’s not always realistic.
ARPU helps you determine which levels of investment are acceptable in places like customer service or acquisition.
Earlier, we looked at the three different kinds of product tutorial onboarding. If your ARPU is low, one-on-one customer calls are out of the question. They’re too expensive, and don’t scale well enough to support the high volume of customers needed to grow. Same goes for acquisition costs.
AdWords might convert well. However, the Cost Per Lead on a single customer is probably too expensive at scale to support a lower ARPU.
Instead, you need an acquisition strategy that lowers the cost to acquire each lead, like through content and SEO. It might take longer to ramp up than AdWords, but the difference is that it will be sustainable growth your business model can support.
Unsurprisingly, churn also affects this equation.
The types of customers you lose (big vs. small) has a direct impact on ARPU, raising or lowering it exponentially in some cases if a lot of your revenue is tied up in a few big customers.
Think about a B2B company with subscription revenue.
It takes a lot of little $10,000 annual contracts to make up the revenue of a few $100,000/year ones. Losing a whale or two can drastically change ARPU overnight.
Next, Annual Run Rate is your MRR multiplied by twelve months. It gives you a future projection based on your most current revenues, assuming everything stays the same.
Annual Run Rates provide a fast, easy benchmark. However, it can also neglect to show you the full picture of how profitable your product is (or how efficiently it’s growing).
The other big problem is that it doesn’t take into account big fluctuations. This is especially an issue in the early days, where large customer accounts can often distort reality.
The last piece to the puzzle is engagement.
Once you have your data for MRR, you can start to look for areas where users are consistently dropping off to determine engagement. This is often the missing piece of retention reporting.
Take a look at what behaviors are causing drop-offs and why.