Metrics in early stage startups - Leancamp Berlin
Post on 21-Oct-2014
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@fmssnr + @andreasklinger > #leancamp
Metrics
Lessons Learned
Photo: Dstarg
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
Who here
- wants to run
- runs
- works in
- help
startups?
Photo: Dstarg
Who here
is before
Product/Market Fit?
Photo: kenjinakazawa
Startup phases…
Problem/Solution Product/MarketAcquisition
Company building
Discovery Validation Efficiency Scale
Source: Steve Blank’s
Customer Development
log(time)
tra
cti
on
Goal of this session:
Talk about metrics
in early stage
because feels kinda
different…
Photo: mecca dawn
What does it
tell me about
my product?
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.
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
Source: Custdev.com
Discovery Validation Efficiency Scale
QualitativeValidation
QuantitativeValidation
Metrics are applied differently in
early stages
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
Photo: Pascal
Framework:
AARRR
Example Photoapp
Aquisition - User registered
Activation - User took a photo Retention - Opened the app again <= 2pm
Referral - Share a photo publicly Revenue - haha
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.
Cohort - registration date
WK acquisition activation retention referral revenue
Photoapp registration first phototwice a month
share …
1 4000 62,5% 25% 10%
2 8750 65% 23% 9%
3 3500 64% 26% 4%
… … … … …
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/
Retention= people come again= people “do” again= people “buy” again
read: http://danhilltech.tumblr.com/post/12509218078/startups-hacking-a-cohort-analysis-with-google
Retention
Photo: Marie
You focus on retention
because...
Retention = f(user_happiness)
Find your Happiness metric!
e.g. crashpadder (exit to airbnb)
cohorts hosts-happiness by city&time
to create an health/happyness dashboard
Photo: Axel Hala!sz
Dataschmutz
A layer of dirt
that obfoscutates
your
insightful/useable/real data.
Dataschmutz
e.g. created by traffic spikes
Dataschmutz - eg spike traffic
WK visitors acquisition activation retention referral revenue
EyeEm downloads registration first phototwice a month
share …
1 6000 66% / 4000 62,5% 25% 10%
2 25000 35% / 8750 65% 23% 9%
3 5000 70% / 3500 64% 26% 4%
Example 2: MySugrDataschmutz
MySugr
is praised as
“beautiful app”
example.…
=> Downloads
=> Problem:
Not all are diabetic
They focus on
people who
activated.
Dataschmutz KPIs not drilled down enough
ExampleGarmz/LOOKK
had90% activation (votes)
but they only voted for friendsinstead of actually using their platform.
Dataschmutz Competitions
Competitions createadditional ValueProposition.
The process of user
activation
Photo: きなこ
The process of user activation
Your users that are happy and retentive:
What action differed them from your lost users?
Example: Twitter signup processHow 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
Photo: @fmssr
KPIs
&
Dashboards
Good KPIs- Eliminates "Dataschmutz"- Is simple to explain- Focus on retention- To optimize retention focus on activtion
- Drill down -> Metrics need to hurt.
Team
Photo: fisheyedreams
“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
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
Read on!Startup metrics for Pirtates by Dace McClurehttp://www.slideshare.net/dmc500hats/startup-metrics-for-pirates-long-version
Actionable Metrics by Ash Mauyrahttp://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 - @stueccleshttp://www.slideshare.net/stueccles/lean-startup-metrics
Cohorts in Google Analytics - @serenestudioshttp://danhilltech.tumblr.com/post/12509218078/startups-hacking-a-cohort-analysis-with-google
Slides: http://www.slideshare.net/andreasklinger
Thanks
@andreasklinger
@fmssnr
Photo: Yayoi Yaguchi
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