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Download our content ready marketing performance measurement PowerPoint presentation slides and represent your business marketing performance report to the clients and management. This marketing performance management presentation PPT covers a slide on various essential aspects such as ideal acquisition cost model, customer acquisition cost, the cost estimate to acquire a customer, and CAC by channels. Using these marketing operations management presentation slides, you can showcase the organized management of marketing resources. Represent the procedures to accomplish the measurable profit in return on investment and efficiency while maintaining quality in client experience using this business performance management PowerPoint presentation. With the application of these template slides in your presentation, you will illustrate the concept of marketing operations management, performance management, media intelligence, marketing effectiveness, marketing automation, business performance management, marketing performance report and many more. Our motive is to provide a presentation that helps the customer to achieve the end targets. Our Marketing Performance Measurement PowerPoint Presentation Slides are emphatic. They add force to your gestures.
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On Wednesday, October 14th, Michael Aagaard (Sr. Conversion Optimizer, Unbounce) and Inbar Yagur (Sr. Content Strategist, Taboola) led a webinar discussion on landing page optimization. Topics included:psychological principles that help you design effective pages, how to conduct solid, data-driven user research, and more.
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The research presented in this talk represents the collaborative efforts of a team of research scientists and engineers at Netflix on our journey to create best in class user experiences.
Optimize your webpages for more conversions with professional online testing tools and processes. Eworks WSI Cyprus optimizes your conversion rate using an online testing methodology that is outlined in the attached presentation.
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Asko Relas: Machine Learning for conversion optimization – How to be relevant at scale
1. How to be relevant at
scale
Machine Learning for conversion optimization
31.10.2018Etunimi Sukunimi1Asko Relas
2. How do we do
conversion optimization
of personalized offers and content
across channels
in an automated manner?
3. Picking the best converting content
X% Y% Z%
Dreams to fit
any lifestyle
An immersive environment to enhance your
imagination about coziness
Daydreaming
is a state of
mind
An immersive environment to enhance your
imagination about coziness
Feel at home
on the go
An immersive environment to enhance your
imagination about coziness
Which variant of a teaser should we show to which customer?
4. Multi-armed bandit
Which bandit should I play?
Start with random experimentation, and as experience is gained, focus more on the one that
gives the most wins.
X% Y% Z%
5. Sounds a bit like A/B-testing...
B
A
Testing
(Learning)
C
Traffic
A/B/C test
6. Sounds a bit like A/B-testing...
B
A
Testing
(Learning)
C
Using the best
variant
(Earning)
C
Traffic
A/B/C test
7. Sounds a bit like A/B-testing...
B
A
Testing
(Learning)
C
Using the best
variant
(Earning)
C
Traffic
A/B/C test
Earning while learning
A
C
B
Traffic
Multi-armed bandit
8. Sounds a bit like A/B-testing...
B
A
Testing
(Learning)
C
Using the best
variant
(Earning)
C
Exploit the best converting variant increasingly
Traffic
A/B/C test
Earning while learning
A
C
B
Traffic
Multi-armed bandit
9. When to use each approach
A/B testing:
• Experiments where the intent is to find the best variant, and to stick with it for some time
• Eg. making decisions on user interface design
Bandit:
• Short tests, where we don’t have enough time to run an A/B test
• Think about news headlines: By the time an A/B test finishes, the news may be old
• Continuous optimization, where focus is on maintaining high conversion rate
• Reacting to for instance changes in behavior over the course of a year
11. A conversions B conversions C conversions p %
0 0 0 100
Exploration vs. exploitation
How to balance between learning and earning:
• When deciding which variant to show, with probability p pick a random variant,
otherwise pick the best variant so far
• Start with p = 100% and decrease it as we gain experience
12. A conversions B conversions C conversions p %
0 0 0 100
A conversions B conversions C conversions p %
1 0 0 99
Exploration vs. exploitation
How to balance between learning and earning:
• When deciding which variant to show, with probability p pick a random variant,
otherwise pick the best variant so far
• Start with p = 100% and decrease it as we gain experience
13. A conversions B conversions C conversions p %
0 0 0 100
A conversions B conversions C conversions p %
1 0 0 99
A conversions B conversions C conversions p %
1 2 3 95
Exploration vs. exploitation
How to balance between learning and earning:
• When deciding which variant to show, with probability p pick a random variant,
otherwise pick the best variant so far
• Start with p = 100% and decrease it as we gain experience
14. A conversions B conversions C conversions p %
0 0 0 100
A conversions B conversions C conversions p %
1 0 0 99
A conversions B conversions C conversions p %
1 2 3 95
A conversions B conversions C conversions p %
2 3 7 90
Exploration vs. exploitation
How to balance between learning and earning:
• When deciding which variant to show, with probability p pick a random variant,
otherwise pick the best variant so far
• Start with p = 100% and decrease it as we gain experience
15. A conversions B conversions C conversions p %
0 0 0 100
A conversions B conversions C conversions p %
1 0 0 99
A conversions B conversions C conversions p %
1 2 3 95
A conversions B conversions C conversions p %
2 3 7 90
A conversions B conversions C conversions p %
4 5 14 80
Exploration vs. exploitation
How to balance between learning and earning:
• When deciding which variant to show, with probability p pick a random variant,
otherwise pick the best variant so far
• Start with p = 100% and decrease it as we gain experience
16. A conversions B conversions C conversions p %
0 0 0 100
A conversions B conversions C conversions p %
1 0 0 99
A conversions B conversions C conversions p %
1 2 3 95
A conversions B conversions C conversions p %
2 3 7 90
A conversions B conversions C conversions p %
4 5 14 80
A conversions B conversions C conversions p %
7 12 65 40
Exploration vs. exploitation
How to balance between learning and earning:
• When deciding which variant to show, with probability p pick a random variant,
otherwise pick the best variant so far
• Start with p = 100% and decrease it as we gain experience
17. A conversions B conversions C conversions p %
0 0 0 100
A conversions B conversions C conversions p %
1 0 0 99
A conversions B conversions C conversions p %
1 2 3 95
A conversions B conversions C conversions p %
2 3 7 90
A conversions B conversions C conversions p %
4 5 14 80
A conversions B conversions C conversions p %
7 12 65 40
Exploration vs. exploitation
How to balance between learning and earning:
• When deciding which variant to show, with probability p pick a random variant,
otherwise pick the best variant so far
• Start with p = 100% and decrease it as we gain experience
A conversions B conversions C conversions p %
12 23 162 10
18. X1%
Dreams to fit
any lifestyle
An immersive environment to enhance your
imagination about coziness
Z1%
Feel at home
on the go
An immersive environment to enhance your
imagination about coziness
Y1%
Daydreaming
is a state of
mind
An immersive environment to enhance your
imagination about coziness
But different users behave differently...
19. X1%
Dreams to fit
any lifestyle
An immersive environment to enhance your
imagination about coziness
Z1%
Feel at home
on the go
An immersive environment to enhance your
imagination about coziness
Y1%
Daydreaming
is a state of
mind
An immersive environment to enhance your
imagination about coziness
But different users behave differently...
X2%
Dreams to fit
any lifestyle
An immersive environment to enhance your
imagination about coziness
Z2%
Feel at home
on the go
An immersive environment to enhance your
imagination about coziness
Y2%
Daydreaming
is a state of
mind
An immersive environment to enhance your
imagination about coziness
20. X1%
Dreams to fit
any lifestyle
An immersive environment to enhance your
imagination about coziness
Z1%
Feel at home
on the go
An immersive environment to enhance your
imagination about coziness
Y1%
Daydreaming
is a state of
mind
An immersive environment to enhance your
imagination about coziness
But different users behave differently...
X2%
Dreams to fit
any lifestyle
An immersive environment to enhance your
imagination about coziness
Z2%
Feel at home
on the go
An immersive environment to enhance your
imagination about coziness
Y2%
Daydreaming
is a state of
mind
An immersive environment to enhance your
imagination about coziness
X3%
Dreams to fit
any lifestyle
An immersive environment to enhance your
imagination about coziness
Z3%
Feel at home
on the go
An immersive environment to enhance your
imagination about coziness
Y3%
Daydreaming
is a state of
mind
An immersive environment to enhance your
imagination about coziness
21. Use contextual data as input
• Look at the context in which the past conversions occurred
• Depends on the business domain
• Demographics (Age group, Geography, Family, ...)
• Temporal (Time of day, Day of week, Time of year)
• Products / services bought
• Channel, Device
• Once we have collected data, we can identify the attibutes that actually do
correlate
22. Mapping user context to best content
User context
1
0
0
1
0
Age: 18-25
Age: 26-35
Age: 36-45
Channel: web
Channel: mobile
Other attributes...
23. Variants to pick from
Content A
Content B
Content C
Dreams to fit
any lifestyle
An immersive environment to enhance your imagination
about coziness
Daydreaming
is a state of
mind
An immersive environment to enhance your imagination
about coziness
Feel at home
on the go
An immersive environment to enhance your
imagination about coziness
Mapping user context to best content
User context
1
0
0
1
0
Age: 18-25
Age: 26-35
Age: 36-45
Channel: web
Channel: mobile
Other attributes...
24. Variants to pick from
Content A
Content B
Content C
Dreams to fit
any lifestyle
An immersive environment to enhance your imagination
about coziness
Daydreaming
is a state of
mind
An immersive environment to enhance your imagination
about coziness
Feel at home
on the go
An immersive environment to enhance your
imagination about coziness
Mapping user context to best content
User context
1
0
0
1
0
Age: 18-25
Age: 26-35
Age: 36-45
Channel: web
Channel: mobile
Neural net for mapping a user context
to a piece of content
0.45
0.56
0.82
Other attributes...
26. The whole setup
Feedback
Product promotion(s)
Recomms
Calculate product
recomms for each user
Behavioral
data
CRM user
profile
Choose best content for
each recommendation
27. vs.
The whole setup
Feedback
Product promotion(s)
Recomms
Calculate product
recomms for each user
Behavioral
data
CRM user
profile
Choose best content for
each recommendation
28. vs.
The whole setup
Feedback
Product promotion(s)
Recomms
Calculate product
recomms for each user
Behavioral
data
CRM user
profile
Choose best content for
each recommendation
Dreams to fit
any lifestyle
An immersive environment to enhance your imagination
about coziness
Daydreaming
is a state of
mind
An immersive environment to enhance your imagination
about coziness
Feel at home
on the go
An immersive environment to enhance your
imagination about coziness
29. How do we do
conversion optimization
of personalized offers and content
across channels
in an automated manner?
30. Summary
• A/B testing everything at scale is time consuming, costly, and slow
• Pick your battles and let a machine do the bulk work
• Earn While You Learn
• Data collection is a cost, which bandits attempt to minimize
• Some DXPs already provide bandits out of the box
• We used a neural net for the contextual bandit, but that is not the only possible approach
• Any gradient-descent based algorithm will allow incremental (read: fast) learning
• Random forests etc. require less feature engineering, but are slower learners due to
their batch orientation