Ask anyone building mobile apps and they’ll tell you retention is the biggest issue they’re facing. Retention for apps sucks. Period.
With the different metrics to measure retention — D1, D7, D30, Week1, Week5, Month 2 — it’s really important to decide on the one metric we should pay attention to. Before we proceed further let’s define retention.
Retention
What percentage of your users are coming back week after week, month after month?
What is retention? The simplest way to define to measure ‘retention’ is an app open. We can use plays(for a music app), bookings(for a diner app) to define retention too.
How many of the users you acquired on 1st March opened the app again on 3rd March?
How many of the users who first played a song on 1st March, played a song again on 3rd March?
When we group a cohort, we use the acquisition date to define the users acquired. If you’re just getting started with cohorts, please read basic post.
D1 retention: The number of unique users who came back to your app on the next day of installing/opening the app.
D1 % = no. of users who opened app on d1/no. of users who opened on day 0
Where D0= users who opened the app on the first date. D1= users who opened the app on the next day.
Definition: By default Google Analytics & Apsalar consider the ‘first app open’ as the acquisition date for building a cohort. So all the users who opened the app for the first time(NOT installed) on 11th would be considered as D0 users.
D7 retention:
D7 % = no. of users who opened your app on D7/no. of users who opened your app on D0.
Please note that this is a day metric. Only the users who opened your app on a particular day after opening it up on the first day are counted.
D30: No. of users who open your app 30 days after it was installed.
While d1-d7 are good metrics since the 70% of the apps are uninstalled/become dormant in the first 7 days and % change in D1 has a direct impact on the overall number of retained users, D30 doesn’t enjoy the same benefits. Changes in D30 may not give you an accurate picture of your app performance.(Is it the re-engagement campaign? Is it the push strategy? Is it the app performance(lesser crashes)?)
Now that we have considered how to measure daily retention, let’s look at weekly retention. In weekly retention, we club users into buckets of weeks during with they were acquired and measure the retention over a period of weeks. Looking at data cumulatively has the advantage of averaging out the highs and lows to give you a more accurate picture.
Week1 retention: All the users who opened the app in week0 (suppose 24th-30th Jan, who also opened the app in the next week1 (17th-23rd Jan)
week1 retention =
No. of users who opened the app in week1/No. of users who opened the app on week0
Similarly week5 is defined as
week5 retention: No. of users who opened the app in week5/No. of users who opened the app in week0
Ex. If you acquired 100 users from 13th-19th dec, if 12 users opened the app again in the 5th week(17th-23rd Jan), week5 retention would be 12/100= 12%
So what conclusions can we draw from the chart above:
- Our week1 retention has varied with a big dip in 27th-2nd Jan with only 34% users coming back.
- Week2 retention has greatly increased in the recent weeks. Please note that we cannot currently look at week2 retention for 7th-13th Feb since the entire week isn’t complete. (if today’s date was 19th feb :))
- We started re-engagement campaigns around 10th targeting inactive users, our Week 4 retention has improved greatly. 12–17 is a huge jump for Week4!
quality of users obtained from different channels varies across channels, it’s very important to look at channel-specific retention rather than overall retention.
Month2 retention: Commonly known as M2 retention, it’s the number of users who were acquired in M1 who came back to the app in the next month. Example All the users who opened the app in Jan who had installed the app in Dec.
Does your app have a daily use-case, a weekly use or a monthly-use case?Based on this the retention metric you’re looking at would vary substantially.
How to model Growth?
Having a successful referral program to add virality is great, since it’s the only way to sustainably grow an app. However considering it’s hard to achieve true virality, let’s extend on the model built by Rahul Vohra from Rapportive & model user growth considering a different retention rate for each channel.
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Sample chart for week-on-week retention for apps:
A sample chart for week-on-week retention looks something like this.
What’s week-on-week retention? Week-on-week retention is the number of users who are retained from the last week to this one.
So week2-to-week3 retention % here is week3 retention/week2 retention * 100. We lose the most number of users in week0-week1, then the drop off decreases a bit till it stabilizes or goes down to a certain extent. So by week6-week7 our most active users continue to use the app with a drop-off rate of 20% odd.
Let’s consider that Organic, Facebook, Google & affiliates are your main sources of acquisition. Further let’s assume that week1 retention for Facebook is 30% and week5 retention is 7%(Yes! I’m taking numbers on the lower side).
The activation rate: % of installs which become a ‘qualified’ user. So the simplest definition of activation is a signup.
Just like the retention rate varies per user, the activation rate also differs per channel. Ex your organic users might have a better activation rate compared to Facebook, while incent traffic might have better activation rate(since they are paid for taking actions :)) though a poor retention rate.
We try to answer a simple question: Assuming you get 5000 downloads per day, how many users would you left have in a month with a retention rate of 10%?
So without further ado, let’s look at the numbers.
Acquisition:
Users lost!
Each channel loses users at a different rate based on its weekly retention rates. Here’s a sample model of the number of users you would lose.
Loss equation:
Users lost in week0 = Users lost in current week from week0(current week =week0 so no users lost!)+ users acquired during week-1 that were lost in current week + Users from week-2 that were lost in this week + Users in week-3 that were lost in this week + Users from week-4 that were lost + Users from week-5 that were lost in the current week
Further clarifying this equation:
Users at start of week0*(1-week0_retention rate) + Users at the start of week(-1)*(week1-week0 retention) + Users at the start of week(-2)(week2-week1 retention) + Users at the start of week(-3)*(week3-week2 retention) + Users at the start of week(-4)*(week4-week3 retention) + Users at the start of week(-5)(1-week5 retention)
Therefore growth = Sum(growth in each channel)
Why is the number of users lost after week5 0?
Here we’re making the assumption that loss becomes constant after week5. Ideally the app continues to lose users but at a lower rate. But it’s an ok assumption for now.
At week5, you were left with 8790 from the 73,600 users you acquired from the 105,000 downloads you got in a week! Assuming you have a respectable 38% week1 retention rate. Holy Moly!
let’s see how a 10% increase in week1 retention affect our overall number of users.
So if you’re week1 retention goes up but the retention for the remaining weeks is still low, it doesn’t make much of a difference.
On the other hand let’s check what happens if we have a 10% jump in week5 retention.
9169, that’s a 4% jump in overall users remaining.
So we can see to make a meaningful difference to your growth story you’ll have to work on retention across weeks.
In conclusion, one should monitor D1-D7 daily to track changes due to a version update and keep track of the initial experience. week1 and week5 help us get an idea of immediate and long term retention. It’s a good idea to dig into the details on M3, M4 retention too.
Here’s a link to the growth model if you’re looking to model the overall growth for your app. Hope you retain more users!
In summary:
- D1-D7 tell you about day-to-day workings of the app
- week1 & week5 give a more complete picture. M2 gives complete long term outlook of your app retention
- Look at channel specific retention
- Model Growth for app — to understand how retention affects your app growth
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