The document discusses strategies for increasing user retention in mobile apps. It recommends analyzing user engagement and retention data to identify drivers of retention and natural usage habits. The data should be used to segment users and define triggers for contextual messages. The analysis can inform a cross-channel retention strategy involving product, messaging, and CRM improvements. Case studies show how adaptive onboarding and tailored push notifications increased retention metrics for various apps. Scoring users based on engagement can further personalize retention campaign frequency and content.

1. AppFollow Meetup
How to use data to increase
your user retention
Kevin Bravo - Innovation Lead at Phiture

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Quick about me
Kevin Bravo
Growth Consultant & Innovation Lead
Auditing and helping well established companies (>1M MAU) to
understand and improve their users retention through data-driven
CRM activities (emails / push / inapps).
Background in Data Analysis, Tech & Product growth, iOS
Development.

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App Store Optimization | Retention | Growth Consulting

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● Acquisition costs keep increasing
● Competition is fierce
● User expectations are higher than ever
● Very low barrier to uninstalling apps
○ Low retention rates are the norm
● Many users don’t return after first session in the app
● Winning apps grow MAU and MRR through healthy retention
● Losing apps will burn through acquisition budgets with
slower (or negative) growth
The Mobile App Market
is Saturated

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Retention Metrics
Consider core action that users should build a
habit around
(Intent-based app vs social apps)
At SoundCloud: Monthly Active Listeners
(metric defines both frequency and activity)
Other metrics to look at:
MAU, WAU, DAU - generic active user metrics
DayN, WeekN, MonthN Retention
Reactivation Rate
N-day rolling retention

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Everything affects Retention
● Changes to acquisition methods
● Changes to the product
● Changes to the way the product is marketed + positioned
● CRM Activities
● Interactions with Customer Support or a broader user Community

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Assess the current situation
before diving into tactics
● Identify gaps + opportunities
● Develop insight into drivers of
retention
● Inform strategy through data

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Identify Tech Gaps

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Identify Data Gaps & Tools Discrepancies
Goal: Ensure data is being collected at the right
level of detail
Event Tracking should facilitate:
● Analysis + Investigation
● Impact Measurement + Conversion Tracking
● Targeting capabilities
● Personalization capabilities
● Trigger points for message delivery

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Document your event + attribute taxonomy
It’s not exciting to do, but rewards are:
● Identification of gaps + bad data
● Consistency of event naming across 3rd party tools
● Improved clarity + trust in the data
● Onboarding of new team members faster

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Key levers for influencing Engagement + Retention

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Understanding impact of current CRM efforts

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App Engagement Analysis
● Make sure you know your app inside
and out
● What are the common paths users are
taking?
● What features are most popular?
● What are common usage patterns?
● Are there emergent clusters in user
behaviour?

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Engagement + Retention Data Analysis
A major part of the audit involves active data exploration:
● Building a quantitative picture of the current state of retention
● Identifying useful behavioural segmentation approaches
● Uncovering the actions that are highly correlated with higher
retention
● Understanding the current usage frequency and establishing
a realistic target usage frequency

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Identifying Drivers of Retention

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Natural Usage Frequency
Method available here: https://mobilegrowthstack.com/natural-usage-habits-choosing-your-engagement-metric-a158438962c3

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Segment retention data with different approaches

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Defining user triggers (TCALP Framework)
- Time
- What is the different time of day / week when the user is the most likely to use your
product?
E.g: Uber: It's Saturday night and I want to go out
- Change
- What are the changes in the environment that can affect your user to use your product?
E.g: Uber: City transportation are on strike
- Action
- What actions your users are taking that can tie to another usage of your product?
E.g: Netflix: User just finished watching a show
- Location
- What change of location can impact the usage of your product?
E.g: Headspace: user just arrived home from work
- People
- What information / behavior of other people can affect the usage of your product
E.g: Tinder: There are a lot of people swiping right now in the app for your city

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The outputs of the different analysis should
help you build a strategy for improving
retention
The Retention Strategy may involve
changes to approach in:
● Positioning + User Acquisition
● Product
● CRM / Marketing Automation
Tying it all together...

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Messaging strategies to increase retention
Suggested Channels + Tactics
Frequency: activity notifications (push + email)
Feature exposure: In-App Messages for Adaptive
Onboarding
Session Length: In-App Content Feed

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Multiply impact with a cross-channel approach

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Building a CRM Lifecycle Plan

24. Case Study: Vody
Vody is a TV show + Movie discovery and tracking app,
soft-launched in South-East Asia.
What we built:
● Adaptive onboarding within the CRM to target users based
on retention analysis (which features tied to better
short-term retention)
● Increased onboarding completion rate by 15%

25. Case Study: HS
Headspace is a meditation app available worldwide.
What we built:
● Push notifications tailored for each user:
○ Delivery time based on their meditation time
○ Copy based on their onboarding choice (why they
are meditating) & packs they didn't try yet (or didn't
finish)
○ Next push based on interaction with the previous
one
● Increased Activation rate (people who meditated at least
once) by 6%

26. Case Study: ONX
OnX is a toolbox for hunters in the US.
From our correlation analysis, we defined that the compound
impact of using 4 different features within M1 tied to long-term
retention.
What we built:
● Adaptive onboarding within the CRM to educate users on
our main retention metric (Feature 1)
● Feature-based onboardings for W2-W4 based on previous
behavior (Features 2-4)
Each campaign had variants that were triggered until the
user used and understood the feature (Occurrences of
Feature X > 1)

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To go further: User level scoring
You can focus on improving CRM
frequency based on the user level.
By understanding how users interact with
your messages (push/email/inapps) you
can define an "engagement score" for
each user, and tailor your campaign
frequency (and value prop) for each user,
instead of running bucketed campaigns.

28. Thanks! - Any Questions?
kevin.bravo@phiture.com
@phituregrowth
mobilegrowthstack.com | phiture.com
Check out the Mobile Growth Nightmares
Podcast! www.mgnpodcast.com
Thanks!
Any Questions?