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June 2016
solving problems with
personalization
an introduction to problem solution mapping
2
problem / solution mapping
A unifying framework for continuously optimizing around a common set of goals,
problems & sol...
3
it’s still an 

experiment
everything is an
experiment
promotions + campaigns
merchandising changes
copy changes
UX + IA...
4
what is
personalization?
The customization, targeting or adaptation of
content and/or experiences for end users
based on...
5
sizing the prize
large
segment
effort
opportunity
small
segment
algorithmic manual
target smart phone users
with ability...
6
increase mobile conversion rate
by 10% by the end of 2016.
ask yourself:
• target set?
• clearly understood?
• time base...
7
increase mobile conversion rate
by 10% by the end of 2016.
problems
ask yourself:
• is it a root problem?
• who does it ...
8
solution hypotheses
ask yourself:
• I believe that…
• If I am right then…
• could a designer/
developer/analyst
reasonab...
9
segments: let’s get real
where should we start?
how do you get from qualitative
personas to definable 

data segments?
i...
10
When you are personalizing, you are still
experimenting.
Map personalization hypotheses back to clearly
defined goals a...
thanks!
sales@clearhead.me | 844.425.7432
clearhead.me
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Solving Problems with Personalization

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Personalization! The word that digital marketers and vendors cannot stop saying. "Personalization is the future." "We need personalization!" Well, at Clearhead, while we are champions of better digital experiences through data, we reverse engineer every solution to the formative question -- "What problem are you solving?".

Recently, we've been working with data-driven marketers to think of personalization, not as the answer or the future, but as a means for solving segment specific problems through the leveraging of explicit and implicit end user data. We've tried to demystify the term, explain it in relation to terms like "AB/MV Testing" and provide some ways to approach segmentation.

Most importantly, though, we've discussed personalization through the lens of Problem Solution Mapping (PSM). PSM is a unifying method for continuously optimizing around a common set of goals, problems & solutions, researched & validated with data every step of the way. Problems are mapped to clearly defined goals and are then rigorously researched, via usability research, analytics & customer feedback, to distinguish noise from signals.

Learn more: www.clearhead.me

Published in: Data & Analytics

Solving Problems with Personalization

  1. 1. June 2016 solving problems with personalization an introduction to problem solution mapping
  2. 2. 2 problem / solution mapping A unifying framework for continuously optimizing around a common set of goals, problems & solutions, researched & validated with data. 
 1 
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 3 goals PS Ps 
pS ps problems 
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 4 hypotheses prioritized by data 
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 learnings validated by data investments UX product trading marketing
  3. 3. 3 it’s still an 
 experiment everything is an experiment promotions + campaigns merchandising changes copy changes 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? 
1 How will you know if the 
 change was successful? 
2
  4. 4. 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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a 
b
  5. 5. 5 sizing the prize large segment effort opportunity small segment algorithmic manual target smart phone users with ability to send cart to their desktop people who bought this long tail item also bought this target un-authenticated people who come from pinterest with an email opt-in modal
  6. 6. 6 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
  7. 7. 7 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
  8. 8. 8 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
  9. 9. 9 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?
  10. 10. 10 When you are personalizing, you are still experimenting. Map personalization hypotheses back to clearly defined goals and validated problems. 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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  11. 11. thanks! sales@clearhead.me | 844.425.7432 clearhead.me

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