Metrics in early stage startups - Leancamp Berlin

1. Metrics
Lessons Learned
Photo: Dstarg @fmssnr + @andreasklinger > #leancamp

2. Florian Meissner,
CEO of EyeEm
@fmssnr
Andreas Klinger
Co-Founder a.D. of LOOKK
@andreasklinger
Hello Berlin.
All slides are on
www.slideshare.net/andreasklinger
Photo: TTL

3. Who here
- wants to run
- runs
- works in
- help
startups?
Photo: Dstarg

4. Who here
is before
Product/Market Fit?
Photo: kenjinakazawa

5. Startup phases…
traction
log(time)
Discovery Validation Efficiency Scale
Acquisition
Problem/Solution Product/Market Company building
Source: Steve Blank’s
Customer Development

6. Goal of this session:
Talk about metrics
in early stage
because feels kinda
different…
Photo: mecca dawn

7. What does it
tell me about
my product?

8. Vanity VS Actionable
Usually:
“We have 5000
(Total Registered) Users…”
But also numbers that relate
stronger to your PR bumps than
to your product core.
“We have 5000 Visitors / Month”
is in early stage usually not
actionable.

9. problems in early stage:
1) external traffic messes up your insights
2) product is not ready for market
communication, vp, market seg, channels,
product - all is yet wrong. So how much does
“10% improve really tell you”
3) small data pool of actually useable data
Photo: Dstarg

10. Metrics are applied differently in
early stages
Discovery Validation Efficiency Scale
Qualitative Quantitative
Validation Validation
Source: Custdev.com

11. Early stage metrics are useable for:
Validation of customer feedback
- saying vs doing
- did they really use the app?
Validation of internal opinions
- believing vs knowing
- “Our users need/are/do/try…”
Photo: dstarg

12. Framework:
AARRR
Photo: Pascal

13. Example Photoapp
Aquisition - User registered
Activation - User took a photo
Retention - Opened the app again <= 2pm
Referral - Share a photo publicly
Revenue - haha

14. Example Photoapp
Aquisition - User registered
Activation - User took a photo
Retention - Opened the app again <= 2pm
Referral - Share a photo publicly
Revenue - haha
To see progress over time we create groups of users
(cohorts) and compare them.

15. Cohort - registration date
WK acquisition activation retention referral revenue
twice a
Photoapp registration first photo share …
month
1 4000 62,5% 25% 10%
2 8750 65% 23% 9%
3 3500 64% 26% 4%
… … … … …

16. In early stage focus on retention +
activation
Aquisition - User registered
Activation - User took a photo
Retention - Opened the app again <= 2pm
Referral - Share a photo publicly
Revenue - haha
read: http://www.ashmaurya.com/2010/07/3-rules-to-actionable-metrics/

17. Retention
= people come again
= people “do” again
= people “buy” again
read: http://danhilltech.tumblr.com/post/
12509218078/startups-hacking-a-cohort-
analysis-with-google

18. Retention

19. You focus on retention
because...
Retention = f(user_happiness)
Photo: Marie

20. Find your Happiness metric!
e.g. crashpadder (exit to airbnb)
cohorts hosts-happiness by city&time
to create an health/happyness dashboard

21. Dataschmutz
A layer of dirt
that obfoscutates
your
insightful/useable/real data.
Photo: Axel Hala!sz

22. Dataschmutz
e.g. created
by traffic
spikes

23. Dataschmutz - eg spike traffic
WK visitors acquisition activation retention referral revenue
twice a
EyeEm downloads registration first photo share …
month
1 6000 66% / 4000 62,5% 25% 10%
2 25000 35% / 8750 65% 23% 9%
3 5000 70% / 3500 64% 26% 4%

24. Dataschmutz
Exam
MySugr
is praised as
“beautiful app”
example.…
=> Downloads
=> Problem:
Not all are diabetic
They focus on
people who
activated.

25. Dataschmutz
KPIs not drilled down enough
Example
Garmz/LOOKK
had
90% activation (votes)
but they only voted for friends
instead of actually using their platform.

26. Dataschmutz
Competitions
Competitions create
additional Value
Proposition.

27. The process
of user
activation
Photo: きなこ

28. The process of user activation
Your users that are happy and retentive:
What action differed them from your lost users?

29. Example: Twitter
signup process
How many times do people
need to use Twitter to come
back next month?
(7)
What did they do?
Magic number 30
(Follow 20 people, followed
back by 10)
How do we get people to 30?
Make assumptions, create
features and run tests!
Watch: http://www.youtube.com/watch?
v=L2snRPbhsF0

30. KPIs
&
Dashboards
Photo: @fmssr

31. Good KPIs
- Eliminates "Dataschmutz"
- Is simple to explain
- Focus on retention
- To optimize retention focus on activtion
- Drill down -> Metrics need to hurt.

32. Team
Photo: fisheyedreams

33. “If we have data, let’s look at data.
If all we have are opinions, let’s go with mine.”
-Jim Barksdale, former CEO of Netscape

34. Team
Clear decision making hierarchy
Broken code -> data not trustworthy -> trust lost -> data
useless
Implement data thinking (especially in core dev team)
New features need to have a goal. And this goal needs to
be represented by a KPI
Focus on a simple and small set of KPIs, dont go crazy
(example google analytics)
in early stage not data driven but data validation

35. Read on!
Startup metrics for Pirtates by Dace McClure
http://www.slideshare.net/dmc500hats/startup-metrics-for-pirates-long-version
Actionable Metrics by Ash Mauyra
http://www.ashmaurya.com/2010/07/3-rules-to-actionable-metrics/
Data Science Secrets by DJ Patil - LeWeb London 2012
http://www.youtube.com/watch?v=L2snRPbhsF0
Twitter sign up process
http://www.lukew.com/ff/entry.asp?1128
Lean startup metrics - @stueccles
http://www.slideshare.net/stueccles/lean-startup-metrics
Cohorts in Google Analytics - @serenestudios
http://danhilltech.tumblr.com/post/12509218078/startups-hacking-a-cohort-analysis-
with-google

36. Thanks
@andreasklinger
@fmssnr
Slides: http://www.slideshare.net/andreasklinger
Photo: Yayoi Yaguchi