Avoid failure by thinking critically about your data. Avoid these 5 data traps and create products and services customers want.
Based on Value Proposition Design by Alexander Osterwalder, Yves Pigneur, Greg Bernarda & Alan Smith. More info: http://bit.ly/1tbBCH6. #vpdesign
5 Data Traps to Avoid When Testing Your Value Proposition Design
1. 5Data Traps to Avoid
when testing your Value
Proposition Design
2. Overview of the Testing Process
Extract Hypotheses Prioritize Hypotheses Design Test Prioritize Test
Run Tests Capture Learnings Make Progress
3. Avoid failure by thinking
critically about your data.
Experiments can’t predict future
successes with 100% accuracy.
4. trap #1
False Positive Trap
risk: Occurs when your testing data misleads
you to conclude, for example, that your customer
has a pain, when in fact that is not true.
5. trap #1
False Positive Trap
tips: Test “the circle” before you test “the
square”. Understand what’s relevant to
customers and avoid being misled by positive
signals for irrelevant value propositions.
Design different experiments for the same
hypothesis before making important decisions.
6. trap #2
False Negative Trap
risk: Not seeing things that are there.
Occurs when your experiment fails to detect,
for example, a customer job it was designed
to unearth.
7. trap #2
False Negative Trap
tips: Make sure your test is adequate.
Dropbox initially tested customer interest
with Google Adwords. They invalidated their
hypotheses because the ads didn’t perform.
Yet, people didn’t search because it was a
new market, not for a lack of interest.
8. trap #3
The “Local Maximum” Trap
risk: Missing out on the real potential.
Occurs when you conduct experiments that
optimize around a local maximum, while
ignoring the larger opportunity. Positive
testing feedback might get you stuck with a
much less profitable model while there is a
more profitable one.
9. trap #3
The “Local Maximum” Trap
tips: Focus on learning rather than optimizing.
Don’t hesitate to go back to designing better
alternatives if the testing data is positive, but
the numbers feel like they should be better.
10. trap #4
The “Exhausted Maximum” Trap
risk: Overlooking limitations (e.g. of a market).
Occurs when you think an opportunity is larger
than it is in reality.
11. trap #4
The “Exhausted Maximum” Trap
tips: Design tests that prove the potential
beyond the immediately addressed test subjects.
12. trap #5
The Wrong Data Trap
risk: Searching in the wrong place.
Occurs when you abandon an opportunity
because you are looking at the wrong data.
13. trap #5
The Wrong Data Trap
tip: Go back to designing other alternatives
before you give up.
14. trap #5
Create products and services
customer want. Start with your
best value proposition.
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