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“We have come to earth to save humans from bad Conversion rates & Web sites that suck”
Traffic Outcomes
4
Your website is a leaky bucket
5
Conversion Rate Optimization is born
✓ Conversion Jam x 6 ✓ Conversion Manager x 200
+ 500 projects + 25 employees
A lot has happened since……
We are nr 1!
@conversionista Amsterdam Berlin Frankfurt London Munich Paris Stockholm Vancouver globaloptimizationgroup.com
>170 Conversion Experts in 10 countries
© Andre Morys, Web Arts AG www.web-arts.comFRANKFURT - HAMBURG - MÜNCHEN@morys Amsterdam Berlin Frankfurt London Munich Paris Stockholm Vancouver globaloptimizationgroup.com
W. Edwards Deming
„If you can't describe what you are doing as a process, you don't know
what you're doing.“
„A bad system will beat a good person every time.“
A process:whathow
sequence
The Optimization
wheel
12
What is the one thing Organizations never lack?
13
And what is believed to be the source of the BEST ideas?
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So here is your process
Creativity Ideas H.I.P.P.O Your BIG
launch
15
What are the problems with this “method”
Not data-driven
Unclear where ideas come from and what they are supposed to
achieve
Hit or miss
Not a repeatable process
BIG problems
4 Data- Driven? Outcome-focused? Hit or miss! Process?
17
Here’s our proposal
Creativity Ideas
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Here’s our proposal
Data Ideas
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Here’s our proposal
Data Hypotheses
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Ideas vs Hypotheses
An idea is a loosely formulated ambition
or direction with many possible
variations
An hypotheses is a structured idea that tells you: - Where it came from - How it’s supposed to work - What the intended result is
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The I.A.R. hypothesis formula
Insight
Action
Result
apps.conversionista.se
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So this is our process
Data Hypotheses
Our 4 BIG problems
We are data-driven
We focus on outcomes
There is something of a process
Still a “hit or miss” business
25
What do these writers say?
“Validated learnings” “Empirical validation”
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Or in plain english
“Fire bullets. Then Canonballs.”
27
The Optimization wheel
Data Hypotheses
Experiments
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Outputs
Data Hypotheses
Experiments
29
Compare to Build-Measure-Learn
Data Hypotheses
Experiments
Data Ideas
Code
Our 4 BIG problems
We are data-driven
We focus on outcomes
We have a process
We are agile
31
Hey, we’re not done yet
Data Hypotheses
Experiments
NEW problems
3 The Why? The right project? The unknown?
33
The Optimization wheel
Data Hypotheses
Experiments
34
The Optimization wheel
Data Hypotheses
Experiments
The insights phase -
Understanding the Why
HypothesesData
Psychology -
Bridging the gap
Data Hypotheses
37
How data and hypotheses interact
Data Hypotheses
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How data and hypotheses interact
Data Hypotheses
You come up with rough hypotheses built on best practices,
previous experience etc.
Then you qualify them with data
Start here
1.
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Add to wishlist. Great tool, but…….
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Add to wishlist. Great tool, but…….
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What does the data say?
Converts like crazy Aint nobody got time for that
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How data and hypotheses interact
Data Hypotheses
You dig down in your data to find patterns, anomalies, outliers.
Then you form hypotheses of WHY those patterns exist.
Start here
2.
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Case - IP telephony WTF!
44
Case - IP telephony
Flag
What you get
Check
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And then you repeat until you’re done
Data Hypotheses
Psychology -
Bridging the gap
Data Hypotheses
How do we know we have: - All the data we need - The right data?
Data
Experiments
How do we know we have: - All the data we need - The right data?
Data
Experiments
Tools & Research
49
Skistar (desktop) Eyetracking
Site catalystBig drop-off in the skipass purchase funnel
Eye trackingUser did not understand the pricing structure and decided to buy on arrival
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Form tracking
4,9 % Form conversion rate
63 % error rate
21 % refills on personnummer
1 ot of 3 is lost here
How do we know we are testing our best hypotheses?
Hypotheses
Experiments
How do we know we are testing our best hypotheses?
Hypotheses
Experiments
Prioritization
Our 3 NEW problems
We know the WHY!
We prioritise our best projects!
We have the right data!
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Build-Measure-Learn
Data Hypotheses
Experiments
PrioritizationTools & Research
Insights
Data Ideas
Code
BuildMeasure
Learn
55
Data Hypotheses
Experiments
PrioritizationTools & Research
Insights
HOW!!!?????
Double loop testing
Design
DevelopDeploy & Monitor
Follow-up
Data Hypotheses
Experiments
58
Do the maths
Estimated uplift
Testingtime
Tests per year
Compound Uplift
Beginner 5 %
Average Joe 10 %
Stellar 20 %
Based on 3000 daily visitors, 5% baseline conversion rate
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Do the maths
Estimated uplift
Testingtime
Tests per year
Compound Uplift
Beginner 5 % 138 days 3
Average Joe 10 % 35 days 10
Stellar 20 % 9 days 40
Based on 3000 daily visitors, 5% baseline conversion rate
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Do the maths
Estimated uplift
Testingtime
Tests per year
Compound Uplift
Beginner 5 % 138 days 3 16 %
Average Joe 10 % 35 days 10 250 %
Stellar 20 % 9 days 40 150 000 %
Based on 3000 daily visitors, 5% baseline conversion rate
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Shotgun testing
Alibi testing
One hit wonder testing
Double loop
testing
Testing velocity
Win rate
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Shotgun testing
Alibi testing
One hit wonder testing
Double loop
testing
Testing velocity
Win rate
Testing Blues
Testing Blues
Testing Blues
General persuasion techniques
Customer journey
Behavioural typology
OUR customers (personas)
64
The 6 questions a visitor will ask
Relevance Value
Trust Action
Ease Assurance
“Am I in the right place?”
“What can I do now?”
“Why should I do this, right here and right now?”
“Can I trust them?”
“How hard will this be?”
“If I do this now, what if….?”
@morys Amsterdam Berlin Frankfurt London Munich Paris Stockholm Vancouver globaloptimizationgroup.com
R E D U C E F R I C T I O N R A I S E M O T I V AT I O N
Average Uplifts of Experiments
67
The 6 questions a visitor will ask
Relevance Value
Trust Action
Ease Assurance
“Am I in the right place?”
“What can I do now?”
“Why should I do this, right here and right now?”
“Can I trust them?”
“How hard will this be?”
“If I do this now, what if….?”
Increase motivation
Reduce friction
Explore
Evaluate
Finish
Confirm
Visitor goals
Our classic funnel
Our classic funnel
The funnel became a shell
Explore
EvaluateFinish
Confirm
The funnel became a shell
Share
The acquisition gap
Relevance
Persuasion Techniques““Am I in the right place?””
Relevance
Keeping the scent
Implicit codes
Limiting cognitive load/Choice paralysis
Brand recognition
E
EF
C
4 Data- Driven! Outcome-focused! Hit or miss Process
3+
The Why! The right project! No unknowns!
@conversionista Amsterdam Berlin Frankfurt London Munich Paris Stockholm Vancouver globaloptimizationgroup.com
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