App based businesses and SaaS products are plenty and come with their respective business challenges. And among these, figuring out actionable metrics is an important one.
While many might be relying on their analytic dashboards to ascertain the numbers, nevertheless, it is always useful to understand the math behind the entire exercise to derive custom or some specific metric during certain events. There are not more than 4-5 frequently used metrics that we can look deeper into and derive something that is actionable.
So here are the most commonly used analytic parameters –
Daily Active Users (DAU) and Monthly Active Users (MAU)-
Daily active users are nothing but the number of users who have used your app or SaaS on a particular day at least once. Daily active users can be observed on a daily basis from your analytics.
Monthly active users, on the other hand, are users who are engaged or do some type of activity on your app or SaaS at least once over a period of 30 days.
However, remember that these terminologies are likely to have relative implications and usage (especially MAU). Hence, DAUs and MAUs must be used to obtain actionable metrics on a monthly or weekly basis. One can consider a specific time period to calculate DAU or MAU on those days. It may be the end of a week or month. Successive weeks and months can give an idea on how much users are staying back. This is why one of the best metric you can derive from DAU and MAU is engagement
The problem with this entire exercise is the repetition of user count. If your app has high retention rate some users may carry to more than 2 months of usage. Now, if active users acquired from previous months are counted along with newly acquired active users to derive MAU, it does not serve much of purpose. The reason is- it cannot tell you if your app engagement is steady, gotten better or gotten worse over months.
Only when acquired users are segregated with respect to a time frame (mostly considered from the start to the end of a month) and then studied in clusters over months; only then actionable metrics can be derived.
This is when Cohort Analysis becomes important and applicable. Cohort charts can segregate the users on the basis of months and provide their respective engagement over time or successive months. Users are not intermixed.
Retention rate is simply defined as the percentage of users who were acquired in a specific time period in the past and still use your app at least once a month.
Hence retention rate is often calculated with respect to cohorts where each cohort is nothing but the total number of acquired user in a specific month and their retention rate in successive months.
Simply put, Churn rate is the number of users who leave or unsubscribe from a service within a specified time period. In other words, users that are lost over a period of time constitute your churn rate.
For apps and SaaS businesses, Churn rate is often calculated on a monthly basis, usually starting from the start to the end of a month.
Churn rate can be effectively fetched out from cohort charts. But like we said that we are going to look deeper and see how to derive actionable metrics from these terminologies.
Similar to MAU and DAU, Churn rate as a metric might provide crazy numbers that might not be helpful at all.
By definition, churn rate should be a ratio that is derived dividing the number of churned user divided by the total number of users who joined the service. However, this is simple said than done.
Consider a two month period with usage statistics for an app.
Month- November December
Start 10000 14357
New Users 5000 5000
Users lost 500 719
New users lost 125 125
Total 14357 (EON) 18513(EOD)
Now going by the formula we have churn rates as 6.25% and 5.87% for November and December respectively. We used the formula as given below to calculate the Churn Rate.
However, if November had fewer new users, the churn rate would be lesser while for December it will increase. This does not really help us, as the sole purpose of churn rate is to derive a normal metric that is proportionate to the size of the business and its current growth rate as well.
An approach to solve this can be re-engineering the above formula to-
With the above formula, the churn rates come out to be 5.9% and 5.1%. This looks better than the previous formula. However, if the number of churned users fluctuate by a great degree with each month, this formula would give radical shifts in the percentage. Try it out for 100 new users instead of 5000 for November. This formula comes with an underlying assumption that the churn rate is evenly distributed with respect to time which is not true.
The average of users in the beginning of the period and end period may seem like it can fix the problem with new customers coming. But this is not the case here. Churn rate may not be evenly distributed in successive months which will eventually result in uneven results.
One of the best way to determine a credible churn rate is to find aggregate rates over a period of 30 days and then derive the metric using the number of initial users. An expression derived by Shopify engineers sums it up as-
This equation helps in finding the churn rate at any point in a specified time frame. Also, the churn rate obtained by this equation for the given time frame is proportional to any date range you may consider.
Duration is the reciprocal of churn rate. It is a quick metric that signifies the duration of retention on a monthly basis.
Example- If your churn rate is 20% then 1/20 = 0.05. Or you can say that the average user from a stayed 5 months on an average.
The five parameters derived here are commonly used across all app businesses and SaaS entities. Depending on what action(s) lead to retention or engagement in your business, your parameters will be derived. However, your observation can go beyond them to derive more meaningful and actionable metrics to evaluate your SaaS or app businesses.