In this presentation, you'll find examples of insights that you can get if you look at your acquisition and retention data on a cohort basis. There are also some tips on how to approach cohort analysis if you are not doing it yet.
The Best Mobile Retargeting Strategies for the Future
Unlocking Cohorts to Assess your App's Past and Further its Future
1. PRESENTED BY
YOUR PICTURE
Unlocking Cohorts to Assess your
App’s Past and Further its Future
June 6, 2019
Ekaterina
Shpadareva
User Acquisition
and ASO manager
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2. ABOUT RUNTASTIC
At Runtastic, we develop and promote a comprehensive
portfolio of easy-to-use health and fitness apps, services and
content.
A HOLISTIC APPROACH TO HEALTH & FITNESS
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4. MONETIZATION SETUP AND THE MOMENT WHEN THE TRIAL_STARTs
Some standalone exercises and workouts are free
Premium offer = all workouts unlocked + customizable
training plan
7-day trial: access to all premium features
Not cancelled till Day 7 => billed on Day 8
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5. Install-to-Purchase time lag (days)
AFTER “7-day trial”BEFORE “7-day trial”
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WHAT HAS CHANGED
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6. BEFORE “trial” AFTER “trial”
Purchased Premium Subscriptions per duration
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WHAT HAS CHANGED
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7. ● Initial and recurring revenue drivers
● Lifetime Value, Average Revenue per install / user / paying user
● Average lifetimes
● Trial to Purchase conversion rate
● Install to Trial conversion rate
● Churn and renewal %
● # refunds
● # negative store reviews => store ratings
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WHAT WE HAD TO PREDICT
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9. What we did:
➔ Switched internal reporting and restructured internal data (with the help of our amazing BI team) to
cohorts based on date of install
When you should consider it:
● When there’s a delay between marketing touch point (e.g. serving an ad) and meaningful conversion
event (e.g. purchase)
Are there any drawbacks?
● Visualization
● Trends are retrospective BUT you can build powerful projections based on them
HOW WE TACKLED THE UNCERTAINTY CHALLENGE
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12. ● Check your targeting and
traffic quality per channel,
including organic
● Double check if there’ve been
changes to the login
component
● Exclude users who are
already in your app
ecosystem via custom
audiences #gdpr
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INSIGHT #2: TRAFFIC QUALITY AND USER INTENT OR TRACKING ISSUES
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13. Step 1. Define your “ideal” ROAS
development pace AND “ideal”
distribution of subscription durations
Step 2. Impact the distribution by:
● Offering discounts
● Experimenting with paywall design
● Experimenting with pricing
Note: no “one fits all” solution, look at it
on a platform/country level
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INSIGHT #3: HOW RETURN ON AD SPEND DEVELOPS AND WAYS TO IMPACT IT
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14. % of purchases made by users acquired through “paid”
Try scale it with:
● relevant copies and videos in your ads
● updated “more seasonal” app store descriptions
● customized CRM messages
● discounts and promo campaigns
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INSIGHT #4: SEASONAL TRENDS AND WAYS TO LEVERAGE THEM
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15. Premium
Churn, %
Extra tip: Additionally, monitor
fluctuations in rates like:
● Install to Trial %, Trial to Purchase
% , Install to Purchase %
● Renewal rate
● LTV
● # Recurring purchases and
revenue
● ARPU, ARPPU
● and other metrics
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INSIGHT #5: PREDICTIONS AND INSIGHTS INTO CHURN % AND OTHER METRICS
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17. BONUS INSIGHTS: HOW CHANGES YOU MAKE TO APP IMPACT USER ENGAGEMENT
Daily
Active
Users, non-
cohort
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18. Built on a cohort level, the trend can
show if the product is getting more
engaging over time and help you
assess product launches and
performance of other feature
changes.
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BONUS INSIGHTS: HOW CHANGES YOU MAKE TO APP IMPACT USER ENGAGEMENT
19. Plotting retention graphs for
weekly and daily cohorts can give
you even more insights, e.g. Is better
retention on Day n caused by new
features / added content or
seasonality?
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BONUS INSIGHTS: HOW CHANGES YOU MAKE TO APP IMPACT USER ENGAGEMENT
20. 20
BONUS INSIGHTS: BEHAVIORAL COHORTS AND MAPPING USER BEHAVIOUR THAT MATTERS
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22. Tip #1. Don’t stop at “cohorts”. Break users it into “segments” or “components”. Mix and match various
pre- and post-install metrics, events and dimensions to discover and grow your “power” users.
=> Adjust bids accordingly to reflect ROAS differences
Tip #2: Invest time into cost data and S2S (server to server) integration
Tip #3: Build your “minimum viable metrics” dashboards reflecting revenue and engagement metrics
(retention, DAU/WAU/MAU, revenue and activity-related conversion events and rates)
Tip #4: Look at granular details but don’t miss out on the big picture
CONCLUSION. TIPS ON GOING AN EXTRA MILE
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Focus of the presentation - on Runtastic Results
Disclaimer: Exact numbers are hidden for confidentiality reasons but the data are representative.
Subscription model
- free version where some features/workouts are locked.
- starting mid-2017 - 7-day trial for 12-month subscription which unlocks all premium features for 7 days and a user is billed on Day 8 after install, if he hasn’t cancelled the trial
Before trial: most purchases happening on Day 1, and almost 100% of them within 7 days => fine for reporting on a weekly non-cohort basis.
After introducing the trial:
=> 1-month subscriptions still happening on Day 1, but 12-month subscriptions (from where most revenue comes) arrive on Day 8 after the trial expires (because that’s when users are billed)
Different distribution of subscription types: Most purchases are becoming 12-month subscriptions instead of 1-month as we had before
After the change we had to figure out the ratios and costs for all of these metrics per platform and country in order to know what users to acquire from where and what users are converting “the best”
When most important conversion events are delayed in time, find another relevant metric, event that happens sooner, and even if it’s a “vanity”metric, e.g. # of trial starts you can soon calculate the conversion from trial to purchase per country/platform/channel, and you’ll know what amount of revenue and what ROAS to expect as well as what’s your Cost per Trial should be so that you can cut non-performing channels at an early stage
Most uncertain was the revenue flow for “recurring” subscriptions because it takes a year to see how many users from every cohort churn or renew
Understand attributes and values of every segment and treat them differently
Quickly identify insights critical to your main goal. Let these findings drive your decisions on bidding, targeting, budget attribution, messaging, and creative
Key benefits of switching to cohort analysis:
Helps you accurately measure the impact of channels, devices, and touchpoints on overall business growth, especially revenue and profit.
Helps build better projections
Removes the guesswork from planning and allows you to be smarter with every dollar you spend
Empowers the UA team and lets them turn data into meaningful insights
The cost of acquiring a new user can increase significantly while exploiting one channel. Reason: users who were most responsive to ads and interested in the product, have already been acquired long time ago. As time passes, each new person exposed to ads is more immune and harder to recruit than the last, hence the acquisition costs increase as the app ages. As a result, the cost of acquiring a new user has almost doubled compared to 1.5 years ago due to channel and market saturation and advanced competition.
Scaling and testing new targeting options and new countries / channels can also result in CPI / CPR increase if the test is not successful, but if you plan to grow the user base and want to reach new users interested in your apps, it’s hard to avoid.
When diversifying - think of the strategic aspect, apart from the potential monetary benefits that come together with reaching new audiences. The best value can be sometimes found by being first to discover and optimize for a new audience (e.g. Instagram stories 1.5 years ago, Snapchat, etc.)
Extra tip: Build similar graphs to see how your acquisition costs develop for not only per every paid channel in your set, but also “blended” costs, i.e. paid and organic installs if you invest time, effort and money into ASO.
When managing a multi-app portfolio, some users might not have your app installed but may already be in your app ecosystem.
=> make sure you’re not cannibalizing your own users.
Even though the total % of new and existing users signing up/in is more or less the same for us over time, Sign in % was increasing during winter months => we were acquiring more users who already had accounts in our multiple apps.
Do you see the same trends in organic acquisition? If not - check your targeting and the quality of traffic per channel.
It is cheaper to cross-sell to existing users via CRM so be careful with not spending too much % of your budget on showing ads to existing users, unless you’re running specific retargeting campaigns.
Blue lines - monthly cohorts where the % of 1-month and 12-month subscriptions is more or less equal.
Red lines - monthly cohorts with the majority (80% of subscriptions) being 12-month
=> Try to push for the renewal of the right subscriptions at the right moments in order to create a sustainable revenue flow. Then you will get some influxes of revenue in between, instead of waiting for 6 or 12 months to pass before you get some.
=> figure out the best subscriptions split for your product and make sure it reflects differences in LTVs, impact this split (some ideas above)
Users coming through paid channels were converting faster than organic users due to seasonal messaging on paid channels (summer-body-related). Complement it with other means (some ideas above)
Extra tip: A/B test and figure out the real profitability of your promotional and discount campaigns to make sure you’re not offering discounts to users who would purchase the subscription anyways. Monitor how the Install / Registration - to - Purchase rate develops (hopefully it’ll be growing) and make sure the Average Revenue per Paying User is not dropping too much.
Churn predictions can be very insightful and these rates wouldn’t be possible to see on a non-cohort basis (e.g. Google Play) when on a given day the # of cancellation can be higher than the # of purchases
On non-cohort graphs it’s tricky to separate new and returning users. What’s the spike about? The CRM campaigns (push and emails) or more investment on the UA side?
The retention trend over time can show if the product is getting more engaging. If you unlock a bunch of features / implement a new one you can see a gradual increase in retention, which can help you assess product launches and performance of other feature changes.
Higher retention % on Day 1 for April and May cohorts - is it caused by unlocked premium content or seasonality?
To answer this, you might need to build retention graphs for daily and weekly cohorts of users. In our case, it was clearly unlocked content, which happened during the week of April 22-28.
Retention graphs can also be based on any meaningful in-app event beyond the app install, e.g. try map the retention rate to monetization or another event/rate that best represents whether users are getting value from your product.
Users who purchased the premium plan have the retention rate 3 times higher compared to those who didn’t. This retention rate is comparable to super engaged “power” users who have done 10+ workouts a month => figure out which is easier to push towards (monetization or engagement or both) and what is your bigger focus at the moment, in order to turn regular or less active users into your “power” users and contribute to positive changes in the retention rates.
Luckily, in mobile we have a chance to analyze users from all angles, including pre- and post-install behavior.
Look at different quality of users and their intent by:
acquisition channel (e.g. Facebook is driving users with a different retention/monetization profile than Google, Twitter users are most engaged but monetize worse, etc.)
Demographics (age, gender)
Geo (country, city)
Device
Acquisition time (are there differences in user behaviour / monetization / retention caused by seasonality, featurings in the app stores, promo campaigns, etc.?)
+ Be consistent in using the same source of retention data and way of measuring it (Google Analytics, Firebase, Adjust, internal data on user sessions, etc.)
