This document provides an agenda and overview for a personalization strategy workshop hosted by Optimizely in Los Angeles on August 18, 2016. The agenda includes: registration and refreshments at 2:30 PM, an opening by Travis Bryant at 3:00 PM, a personalization capabilities overview and demo by Ian Thiel at 3:15 PM, a break at 3:45 PM, a discussion of personalization best practices by Matty Wishnow at 4:00 PM, and group discussion and networking from 5:00 PM. The workshop will focus on personalization strategies and capabilities using Optimizely's platform. Presenters will discuss personalization demos, case studies, and best practices to help attendees develop effective personalization approaches
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Personalization Strategy Workshop - Los Angeles
1. Personalization Strategy Workshop
Los Angeles
August 18, 2016
Travis Bryant, Vice President of Sales, Optimizely
Ian Thiel, Head of New Products, Optimizely
Matty Wishnow, Founder & CEO, Clearhead
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2. Agenda
2:30 PM Registration & Refreshments
3:00 PM Open/Welcome
Travis Bryant, Vice President of Sales, Optimizely
3:15 PM Personalization Capabilities Overview & Demo
Ian Thiel, Head of New Products, Optimizely
3:45 PM Break & Networking
4:00 PM Personalization Strategy Best Practices
Matty Wishnow, Founder & CEO, Clearhead
5:00 PM Group Discussion, Networking & Happy Hour
3. Welcome and Opening Remarks
Travis Bryant, Vice President of Sales, Optimizely
Travis Bryant
Vice President of Sales
Optimizely
4. Personalization Strategy & Demo
Ian Thiel, Head of New Products, Optimizely
Ian Thiel,
Head of New Products,
Optimizely
10. Behavioral retargeting produces 4x ROI
Personalized campaign
Used behavioral targeting to show buy-one-get-one tire
promotion to customers who had viewed tires.
Results
• 21% increase in overall site sales
• Sold out of tire stock 2 months before expected
• 4x return on investment in Optimizely and solutions
partner services
23. 2
problem / solution mapping
A unifying framework for continuously optimizing around a common set of goals,
problems & solutions, researched & validated with data.
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goals
PS
Ps
pS
ps
problems
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hypotheses
prioritized by data
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experiments
validated by data
outcomes
UX
product
merchandising
marketing
24. 3
it’s still an
experiment
everything is an
experiment
promotions + campaigns
merchandising changes
new features & functions
UX + IA changes
new SaaS additions
personalized experiences
everything
if everything is an
experiment,
then these are
questions to live by
What problems are the
changes solving?
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How will you know if the
change was successful?
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25. 4
what is
personalization?
The customization, targeting or adaptation of
content and/or experiences for end users
based on implicit or explicit attributes of more
refined segments.
what is a/b testing?
A method of comparing a variation to a control
to determine if the differences observed in the
sample are statistically likely to survive in a
larger, general population or data set.
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a
b
ab testing to segmentation segmentation to a/b testing
a
b
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b
27. 6
“A good archer is known not by
his arrows but by his aim.”
-Thomas Fuller
28. 7
increase mobile conversion rate
by 10% by the end of 2016.
ask yourself:
• target set?
• clearly understood?
• time based?
• realistic?reduce mobile bounce rate by 15% by the end
of 2016.
increase mobile revenue per visit by 5% by the
end of q3 2016.
improve mobile net promoter score by 10% by
the end of q1 2017.
goals
29. 8
“If I had one hour to save the
world, I would spend fifty-five
minutes defining the problem…
and only five minutes finding
the solution.”
-Albert Einstein
30. 9
increase mobile conversion rate
by 10% by the end of 2016.
problems
ask yourself:
• is it a root problem?
• who does it impact?
• where and when
does it impact them?
• how do you know it
is a problem?
users find it hard to click on our filter & facet
functionality on their smart phone.
it is challenging for users to look at alternative
product shots on our mobile PDP because the
thumbnails are so tiny.
we frustrate mobile phone users with two extra
steps — interstitial cart and account options —
before getting them to checkout.
goal
31. 10
“Determine the thing that can
and shall be done, and then we
shall find the way.”
-Abraham Lincoln
32. 11
solution hypotheses
ask yourself:
• I believe that…
• If I am right then…
• could a designer/
developer/analyst
reasonably begin
work based on the
hypothesis?
I believe that if we skip the interstitial cart page
for smart phone users and redirect them to
checkout once they add something to their cart,
they will be less likely to waver in their journey
and bounce. If I am right, then, mobile
conversion rates for for smart phone users will
increase by 5%.
I believe that if we eliminate the “sign up”
option at the beginning of check-out for all
unauthenticated users on smart phones, they
will be less intimidated by the prospects of
filling out extra form fields and will be more
likely to purchase. If I am right, then mobile
conversion rates for for smart phone users will
increase by 10%.
we frustrate mobile phone
users with two extra steps —
interstitial cart and account
options — before getting
them to checkout.
problem
33. Consistently require all changes
to be mapped to problems &
hypotheses.
Determine if additional problem
research is needed.
hypotheses experiments outcomesproblemsgoals
Determine what should be
researched or tested vs “just do it”
Review & iterate regularly.
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Agree to the “Problems Worth
Solving.”
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what do you do once
you’ve got your map?
34. goals problems hypotheses outcomes
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define clear success metrics, that
include targets, time basis &
exceptions reporting.
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capture business & end user problems.2
organize all problems into clusters.3
research & prioritize problems.4
agree how much to invest in “problems
we want to solve.”
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develop solution hypotheses for
“problems we want to solve.”
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prioritize solution hypotheses.7
ux, product & stakeholders plan for
feedback & experiments.
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solutions are tested at various stages.9
experiments
analysis & data stories around specific
solution hypotheses.
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ongoing measurement of KPIs related to
goals.
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qualitative tracking of “problems worth
solving”
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expand investment in those
solutions that worked!
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move on to new hypotheses if
old ones proved invalid.
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use data to regularly re-align
around problems & solution
hypotheses.
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mapping details
35. 14
segments: let’s get real
where should we start?
how do you get from qualitative
personas to definable
data segments?
is predictive segmentation
a real thing?
anybody doing amazing
omni-channel personalization?
for problem
research
for hypothesis
development
should we just randomly
explore segments?
36. 15
When you are personalizing, you are still
experimenting.
Don’t personalize just because you can. Endeavor to
solve problems that relate to critical goals.
Key considerations for personalization
• Segment size
• Segment value
• Manual v algorithmic
key takeaways
Segment definition and exploration takes time.
There’s no magic button (yet).
Experiments come with risk and investment.
Multi-channel customer data layers are increasingly a
practical reality!
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