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Metrics
                   Lessons Learned




Photo: Dstarg   @fmssnr + @andreasklinger > #leancamp
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…
traction




                                                               log(time)


           Discovery           Validation      Efficiency                Scale



                                              Acquisition
Problem/Solution       Product/Market       Company building



                                                           Source: Steve Blank’s
                                                          Customer Development
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
Metrics are applied differently in
              early stages




  Discovery       Validation      Efficiency           Scale




Qualitative                                       Quantitative
Validation                                         Validation


                                                                 Source: Custdev.com
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
Framework:
                  AARRR




Photo: Pascal
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

                                        twice a
Photoapp   registration   first photo                share        …
                                        month


   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
You focus on retention

                         because...
               Retention = f(user_happiness)




Photo: Marie
Find your Happiness metric!

e.g. crashpadder (exit to airbnb)
cohorts hosts-happiness by city&time
to create an health/happyness dashboard
Dataschmutz

                             A layer of dirt
                           that obfoscutates
                                  your
                      insightful/useable/real data.




Photo: Axel Hala!sz
Dataschmutz


e.g. created
by traffic
spikes
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%
Dataschmutz
                                Exam
              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

Example
Garmz/LOOKK

had
90% activation (votes)

but they only voted for friends
instead of actually using their platform.
Dataschmutz
Competitions




Competitions create
additional Value
Proposition.
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 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
KPIs
           &
       Dashboards




Photo: @fmssr
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 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
Thanks

                                 @andreasklinger
                                    @fmssnr



                       Slides: http://www.slideshare.net/andreasklinger




Photo: Yayoi Yaguchi

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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
  • 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
  • 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.
  • 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.
  • 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