This document discusses Lean startup methodology, including building products based on assumptions, minimum viable products (MVPs), measuring data from MVPs, learning from customer feedback, and pivoting based on data. It provides examples of startups that pivoted after learning their initial assumptions were incorrect, such as a task marketplace startup that pivoted from user-generated tasks to profile-based task finding after measuring low user engagement with the initial MVP. The document emphasizes the importance of testing assumptions through quick iteration and using real data to make product decisions rather than sticking to initial visions.
2. Not Lean
I think the problem is X
The solution looks like this:
A website with social logon
Location based
HTML 5 mobile ready front end
Cloud based, multi continent hosting for resilience
Ability for users to like each others profiles
Monetization via adverts
We can launch in 6-9 months
4. In the beginning
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Lean manufacturing
- Toyota
2011
5. How to drive a startup
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Product Optimization
Strategy
Vision
& Iteration
Pivot
True North
6. STRATEGY IS BASED
ON ASSUMPTIONS
Customers will come back to the app daily
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Customers can be acquired for $1
Customers will use a mobile app
Each customer will invite 0.5 other customers
Customers wants to do X We can build it in 3-months
Advertisers will pay $3 per customer
There are 100,000 early adopters
We will attract customers via social media
7. LEAP of FAITHs
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Some assumptions are Leaps of Faith
iPod
-People want to listen to music on the move
-People will pay for music
11. MEASURE
Must be real data
What to measure – everything
AARRR – Pirate metrics
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12. LEARN
Test data with customers
Must impact business results
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13. CYCLE FAST
IDEAS
LEARN BUILD
DATA PRODUCT
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MEASURE
14. PIVOT or
PERSEVERE
Get enough data then make
Data based decisions
Customer based decisions
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15. PIVOTS
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- Zoom in pivot –more niche
- Zoom out pivot – less niche
- Customer segment pivot
- Customer need pivot
- Technology pivot
- Business model pivot
- Channel pivot
16. PIVOT
sortedlocal.com
@oxygenaccel
www.oxygenaccelerator.com
An unskilled tasks market place
Assumptions
-People wanted unskilled tasks doing (customers)
-People wanted unskilled per hour work (sorters)
-Attracting sorters was going to be difficult
-Customers will want to use mobile
17. PIVOT
sortedlocal.com
@oxygenaccel
www.oxygenaccelerator.com
An unskilled tasks market place
MVP
-Customer task led mobile app
- Customer creates profile & then task “I need a cleaner on
Friday at 4pm to clean my flat”
- Sorters respond via messaging system saying they will do it
& their price per hour
- Customer selects a Sorter & pays
18. PIVOT
Data 1st Month
-1st task posted was an advert not a task
-600 Sorters signed up
-20 Customers signed up
-60% bounce rate on website
-Zero tasks completed
-Customers saying to many responses to task, takes to long to
message each sorter, trust issues with sorters
sortedlocal.com
@oxygenaccel
www.oxygenaccelerator.com
An unskilled tasks market place
19. PIVOT
sortedlocal.com
Pivot – Live for 2 weeks
-Sorter led task market place
-Each sorter has a profile
-Customer searches by task & location (i.e. cleaning)
-Customer selects Sorter from a list of profiles
@oxygenaccel
www.oxygenaccelerator.com
An unskilled tasks market place
20. PIVOT
sortedlocal.com
@oxygenaccel
www.oxygenaccelerator.com
An unskilled tasks market place
Data – 2 weeks old
-2000 customers signed up
-31 tasks posted
-5% bounce rate on website
-Zero tasks completed (via site)
-Transactions are being taken off-line
-150+ clicks on Book now button on Sorter profile
LEAN startup reduces the input required. It allows you to fail faster having spent less money and wasted less time
There are two key assumptions you will have made. One is around the value proposition – your product is wanted by the customer.
You must collect hard data (downloads, unique visitors, dwell time, signup rate etc).
Use the data to ask intelligent questions of customers for a subjective view on your data – this may answer the why question
All data must be put in context of business results – does this data help us prove we can make money out of this
You must collect hard data (downloads, unique visitors, dwell time, signup rate etc).
Use the data to ask intelligent questions of customers for a subjective view on your data – this may answer the why question
All data must be put in context of business results – does this data help us prove we can make money out of this
You must collect hard data (downloads, unique visitors, dwell time, signup rate etc).
Use the data to ask intelligent questions of customers for a subjective view on your data – this may answer the why question
All data must be put in context of business results – does this data help us prove we can make money out of this
You must collect hard data (downloads, unique visitors, dwell time, signup rate etc).
Use the data to ask intelligent questions of customers for a subjective view on your data – this may answer the why question
All data must be put in context of business results – does this data help us prove we can make money out of this
You must collect hard data (downloads, unique visitors, dwell time, signup rate etc).
Use the data to ask intelligent questions of customers for a subjective view on your data – this may answer the why question
All data must be put in context of business results – does this data help us prove we can make money out of this
You must collect hard data (downloads, unique visitors, dwell time, signup rate etc).
Use the data to ask intelligent questions of customers for a subjective view on your data – this may answer the why question
All data must be put in context of business results – does this data help us prove we can make money out of this
You must collect hard data (downloads, unique visitors, dwell time, signup rate etc).
Use the data to ask intelligent questions of customers for a subjective view on your data – this may answer the why question
All data must be put in context of business results – does this data help us prove we can make money out of this
You must collect hard data (downloads, unique visitors, dwell time, signup rate etc).
Use the data to ask intelligent questions of customers for a subjective view on your data – this may answer the why question
All data must be put in context of business results – does this data help us prove we can make money out of this