metrics in early stage startups - leancamp berlin

@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