Wondering which key SaaS metrics you should focus on?
To find out how your SaaS business is performing, collecting and measuring some key metrics early on is essential. The reason why is because this delivers a historical overview instead of a mere snapshot of your performance.
Nevertheless, you’ll want to avoid “Analysis Paralysis” and focus on the essential metrics which provide you with actionable insights.
We’re keeping things simple here and listed a few vital SaaS metrics that will enable you to make informed decisions on topics such as marketing and product development.
SaaS Metrics For Digital Marketers
1. Monthly Recurring Revenue
The most defining metric for every SaaS startup: Monthly recurring revenue or MRR for short. As this measures how much predictable income you’re expected to make each month.
For subscription models, knowing how much revenue you’re able to generate each month is crucial so you can keep a handle on your on your monthly costs. Of course, you’ll have both monthly and annual subscriptions to include in your MRR.
However, don’t mix the two up. For example, you can have six monthly subscriptions of EUR 200 and two annual at EUR 2400.
Six monthly subscriptions x EUR 200
2 annual subscriptions x EUR 2.400 (= EUR 4.800 / 12 months)
= EUR 1.600 MRR
To get the monthly value from the annual subscriptions, divide the total amount by 12 to get your MRR.
Engagement can be one of the most useful SaaS metrics to look at, as it shows you how your product is being used, how often and by how many.
Active daily users are something you’ll want to track and to determine if you’re numbers are increasing every month.
Here’s the data you may want to keep an eye on:
- How often users log in
- Duration of app usage
- Intervals between logins
Look for any pattern concerning usage to analyse what users are doing compared to your assumptions.
In Lean Analytics, authors Alistair Croll and Benjamin Yoskovitz have a neat suggestion of segmenting your users into two groups:
- Group A: Users who do what you want
- Group B: Users who don’t do what you want
This way you’re able to identify common traits between them. From segmenting your users into the two groups above, you may be able to see that loyal users come from a specific referral source (e.g. Reddit or HackerNews), are the same age, or are located in the same country/city.
A/B testing also makes things more interesting and put your assumptions to the test. One thing to try is, adding a new feature and see whether it increases usage. Here you want to find out what makes your product be used more.
A classic example is when Twitter found that people who followed more than ten people, were more likely to return and use the platform. This led to Twitter introducing a Suggested Follower box to encourage users to follow more profiles.
3. Churn Rate
Your users and customers will not stay with you forever, and will eventually churn.
Your churn rate shows you how often they churn and is the percentage of people who abandon your service over a period – which can be measured on a weekly, monthly and quarterly basis.
When is user considered to be churned? This is up to you decide. But it’s usually when a user has been inactive for 90 days.
To calculate your churn rate, you’ll need to know:
- The number of users
- The number of cancellations
Once you know those two figures, you can use this formula to calculate your churn rate:
[(number of churns during a period)÷(# customers at the beginning of period)]
You’ll also want the churn rate for paid and free users. Paid users will typically churn by cancelling their subscription and going back to the free model – whereas free users will just cancel their accounts.
One thing to bear in mind comes from Steven H. Noble, who points out that using the formula above does not necessarily give you the entire picture. Noble suggests, however, to measure churn on a daily basis for the most accurate numbers.
In case you want to learn more, take a look at Noble’s post: Defining Churn Rate
4. Cohort Analysis
To further analyse your churn and spot any relevant trends to address, you’ll to break it down into cohorts.
Cohorts represent a group who share a common trait. One example could be all users who signed up to become customers on June 1st are a cohort.
During a cohort analysis, you will want to analyse your users’ behaviour over a period of time.
Not only can you analyse your churn rate to see which group of users left – but you can see whether the introduction of a new feature led to more engagement or not.
5. User Acquisition Cost
Acquiring users has a cost to it. Even though you may not have spent any money on advertising and all your user acquisition has been organic – your employees still spent time executing marketing and sales activities.
Here is where user acquisition cost comes in handy as it measures how much it cost you to acquire a user. To get your costs, you can use this simple formula:
[Marketing and sales expenditure ÷ Number of users]
The reason why you will want to know your UAC is to determine whether making you’re a profit from the users you’re signing up. Here you’ll need to know your average revenue per user and the lifetime value, which I’m covering below.
6. Average Revenue per User
How much revenue are you making per user? This is where average revenue per user comes in and can be calculated with the following formula:
[Revenue ÷ Number of active users]
The next step after finding out how much money each user is making you is to find a way to increase it.
The most used ways to increase average revenue per user is by up-selling and cross-selling.
The most obvious would be offering different plans where you’re offering more features for a higher monthly fee. The next would be to incentivise users to subscribe to the annual plan with a 10 percent discount.
7. Lifetime Value of User
The lifetime value of a user is a projection of revenue you can expect to receive from the entire relationship between them and your startup.
What’s important to point out here is that this isn’t money you have in the bank and this SaaS metric is mostly used for forecasting.
As you’ll see from Kissmetrics’ infographic, there are different ways to calculate LTV which they cover in their infographic. Depending on the data you’re collecting and already have available, it might be best to keep things simple with this formula:
Lifetime value = [Average revenue per user] x [1 ÷ percentage of annual churn]
For a more accurate picture of your LTV, you’ll want to use cohorts to identify which segment has the highest LTV.
With your LTV you’ll know how much you can spend on marketing and on which segment to deliver the highest return.
It’s up to you decide which SaaS metrics to tracks and how deep you want to go. Nevertheless, you’ll want to make reporting a habit so you can accurately see how your startup is performing and which areas to optimise.
A handy resource to keep your data and Saas metrics organised is this KPI dashboard from Venture Capitalist, Christoph Janz, check it out here: A KPI dashboard for early-stage SaaS startups
Did I miss something? Let me know by commenting below.