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Congrats - you've released your app in the app store! You might've even already hit your first milestone of 1,000 - maybe even 10,000 downloads. But your MAU is a tiny fraction of that. And embarrassingly you don't even know what a MAU is. Time to learn how to use mobile analytics to create engaged users through steps like better on-boarding, lifecycle emails, a/b testing and more.
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JEFF STEINKE
co-founder: Less Meeting | BlueFletch Mobile Consulting
1
jeffsteinke.com
@jeff_steinke
The Tao of MAU
turning app downloads into engaged users
A STARTUP STORY
chapter I
3
our startup’s monthly active users (MAUs)
Name
This
Chart
with essentially 0 MAUs a few months prior,
by mid July our startup has reached 420,000 MAUs
Guess Who
- Growth explosion during the summer of
2011…
- In the music industry…
- Lets users stream music with friends as
virtual DJs…
7
VANITY METRICS BAD,
ACTIONABLE METRICS GOOD
chapter II
8
Who still wants to give me $1,000,000?
How about $10?
from The Verge:
“[Turntable.fm] proved to be the kind of thing
that many people used exactly once”
3 Characteristics of GOOD
Actionable Metrics…(there are more, but this will get you started)
1. Actionable metrics drive decisions(aka they’re actionable)
2. Actionable metrics complement design(designers, listen up!)
Where’d the fun go?
3. Actionable metrics are
Quantitative AND Qualitative
CLIMBING THE MOBILE
ANALYTICS MOUNTAIN
chapter III
19
1. novice
5. expert
4. proficient
3. competent
2. advanced beginner
DREYFUS MODELof mobile analytics skill acquisition
1. novice 5. expert4. proficient3. competent2. advanced beginner
Data for data’s sake
Vanity metrics (e.g. how
many app downloads?)
It’s better than nothing…
NOVICE
1. novice 5. expert4. proficient3. competent2. advanced beginner
Descriptive data
Demographics (e.g. who is downloading my
app, by country, keyword, etc?)
The “Who”
ADVANCED BEGINNER
1. novice 5. expert4. proficient3. competent2. advanced beginner
Diagnostic data
Event metrics (e.g. what did the user do after
downloading my app?)
The “What”
COMPETENT
1. novice 5. expert4. proficient3. competent2. advanced beginner
Predictive data
Data Analysis (e.g. which button color led to higher
engagement?)
The “Why”
PROFICIENT
1. novice 5. expert4. proficient3. competent2. advanced beginner
Prescriptive data
Drives behavior (e.g. how can I proactively get users
to be more engaged?)
The “How”
EXPERT
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