Workshop on personalization

1. Personalization,
Beyond
Recommenders
Edward Chenard

2. About Me
Data, Digital and Personalization Transformation
A Few Highlights
• Worked with personalization and recommenders for 20
years
• Led data science and engineering at numerous Fortune
500’s
• Built and led the creation of over 50 data driven and
digital products
• Grew up in Bermuda, now lives in Minnesota

3. Where the Story Begins
It’s 2011

4. Vision of Data – Continual Customer Relevant Interactions
Using data to learn about our customers.
CONTEXT
AGGREGATE
GENERATE
INSIGHTS
SERVE
MEASURE
CAPTURE
• Services that capture and combine real-
time signals from the physical world with
location data, online activities, social
media, and many other types of contextual
input.
• Technology that enables rapid aggregation
of data from multiple sources and delivers
insights that can give users an immersive
and valuable experience.
• The power of context will enrich not only
online experiences but real-world ones as
well.
• The emergence of context-based services
will have a compounding effect— those
new services themselves provide additional
sources of context.

5. Metadata
How Data Flows
Context Capturing Pipeline
Context Mapping
Customer
Actions
Web-enabled
Access Point
Resource
Online Context Capturing Pipeline
Real-time
Repository
MapReduce
Processing
Real-time
Processing
Customer
Actions
Internal Context Capturing Pipeline
Internal
Access Point
Data
Algorithms
Data Aggregation Insights
Repository
Insights Generation Pipeline
Servicing API
Servicing
Views
Experience
Experience Feedback
Business Rules
Scheduling/Queue Mgmt
Customer
Reactions
Archive
Repository

6. Recommended Product Algorithm
Calculation - Matrix Decomposition
1 0 0 1 0 0 0 …
0 0 1 0 0 0 1 …
1 0 0 0 0 0 1 …
0 0 1 0 1 0 0 …
0 0 0 0 0 1 0 …
0 0 0 1 0 1 0 …
Input Matrix
User X Item
(~ 6 mill X ~20k)
Tuples:
(user, item, count)
Filtering:
• viral items
• users with too few
items
• Items with too few
users
Row
Index
User
1 a1745690-96fb-11e2-9e96-
0800200c9a66
2 c86312f1-96fb-00e2-9e96-
0800200c9a66
3 e41a1b11-96fb-11e2-9e96-
06c03c85fb6e
Col Index Item
1 1945531
2 2953816
3 8264042
Clickstream
(web, e-mail)
Purchases Social/Search/etc
DataSources

7. Event Driven Calculations
Logical Application Flow – ClickStream Data
StoresIngest
Batch
Aggregations
Also Viewed
Most Popular
Raw Data
(S3)
APIs
1
2
4
3
Event and
Item Key
Spaces
C*
Summarizes

8. Innovation Loves Adversity
Building an unproven future.
We had to Start Somewhere
• Big Data (Hadoop)
• Data Science
• And lots of data
What did Personalization do for Best Buy
• Processed more data than the rest of the company combined
• Helped Best Buy be a top 5 performing stock of 2013
• Changed how Best Buy engages and understands customers
Headlines from 2014 - Present

9. SomethingWas Missing
Unknown factors were in play
We Started to Notice Cracks in the System
• Algorithms would go flat
• More data didn’t solve the issue
• More complex algorithms didn’t solve the issue
Personalization
Big Data
Data
Science
The Problem was Bigger than Best Buy
• Other teams at other companies had the same issues
• Our happy model of success was temporary
• Other factors were influencing success, but we didn’t know what

10. “The STAV Cycle”
Strategy /
review
Technology
implementation
Analytics
Data
visualization
“gaining insight and telling stories with data”
Developed in 2014 with David Quimby to help talk about data and business problems

11. TheQuestto
FindtheMissing
Piece
Hints and Possibilities

12. A Quest for Information
From 2012 – 2014, I sought out experts in tech and
personalization
Information was Plenty, True Answers
Were Not
• Data Science was the answer to everything, according to
most
• All tried the same methods but few could produce a real
answer for improvement
• Thousands spent on conference, travel to speak with
experts, and every book on personalization; all lacked of
real answers

13. Aspects of Personalization as We Understood It
Targeted offers
Intelligent
Customer
segments
Business Rules
Contextual Ads
Ad Server
Relevant
Guided Selling
Recommendations
Personas
Targeted Messaging
Targeted Content
User Generated Content
Cross-Sell
Intent-Based search Up-Sell and
Cross-sell
User Profile
Counter Offer
Need Based Shopping
Social Marketing Behavioral Targeting
Social and interests Profile
Location/Proximity
#kpnicholas
2011-2016

14. Transformation Strategy
Focus on
Productivity
Focus on Customers
Enhancement
Focus on a
Platform
HowtoPlay
HowtoWin
Capabilities and
Operating Model
Innovation Business
Model
Talent and Culture
Partner Ecosystem
Model
Data and Connected
Infrastructure
Change the Game Harness the Platform Go Together, Go Far Building Data,
Insights, Action
into our DNA
All About Outcomes

15. Framing Personalization
Personalization
Strategy: Customer and Business Value
Analytics: Who, How, Where?
IT: What Tools and Why
Social Sciences and Design:
Understanding the Human
Condition
- Measurable Results
- Speed to market
- What is important for the customer
- ROI
- Spark, Hadoop
- Cassandra, The Cloud
- Pig, Hive, Python
- HDFS
- Understanding the Customer
- Social trends and how to use them
- UX
- Design Theory
- Data Scientist vs. Statistician
- Languages
- Retain, Train
- Recommenders
- Social vs Technical
Developed around 2014

16. Personalization Expands
Unknown factors were still in play
An Improved Understanding of Data
• Human behavior doesn’t run on objective behavior, it runs on
context and meaning
• To change perceptions, you have to change the story
• Independent and creative thinking is what always drove
progress and civilization, yet data science is essentially a copy
and paste field which is why it has and will never live up to its
promise until that changes
Personalization
Big Data
Data Science
Design
Data
Governance
An Improvement But Not a Solution
• Design helped to give depth of experience to customers
• Governance helped us manage our data better
• However, we still lacked a true understanding of customer intent

17. Personalization is a Lot LikeWorld Building
Great stories draw the audience into their fictional world. Great
personalization, draws the user into a new world of their own creation.
Expanding my Quest Globally
• 2014-2017, a global journey to learn how other cultures
and countries approach personalization
• Data is a second order simulacrum of the true self
• Physical or digital, identity and spaces are constructed
not by ourselves alone but by the network
• Perhaps we had some things all wrong

18. Activity CenteredThinking
Understanding Self
Perception vs Reality: People act on perception, not fact.
Oversimplify and you Breed in
Weakness
• Our identity is tied up in the things we own and we seek
to create our perception of identity with those things we
own
• Profundity – great depth of knowledge or thought. If
you really want to attach with customers, profundity is a
critical aspect. All marketers are liars, because they
must lie to themselves. You want your product to
remain in thought after the experience so that they
become a part of the customer’s identity
Actions and
Touchpoints
Social
(online)
Offline
Work
Offline
Persona
Online
Persona
Devices
Phone

19. AwarenessWithout Understanding
Lots of Ideas, how do they fit?
Many Moving Parts Without a Theme
• Personalization plays on both our desires to improve
ourselves but without losing what makes us, us.
Individuality and identity are a huge part of the context
and conversation
• The journey is a story because we as a species love
stories and the arches the characters take. Often our
personas are a character we adopt for a situation and
those arches we wish to play are part of the storyline
journey we want to create
• What does an experience/story need in order to be
relatable to its audience?
Big Data
Marketing
Personas
Behavioral
Economics
Design
Theory
Behavioral
Science
Data
Science
Abstract
Concepts

20. Personalization’s
MissingPieces
CometoLight
Answers are always complex

21. Design and Systematic Innovation
Personalization Solves a Contradiction
Design Showed the Problem
• Every truly disruptive innovation solves a contradiction.
Every solved contradiction was once an unsolved
contradiction
• Organizations/teams usually forge ahead without
defining a problem. Without defining the contradiction,
you can’t actually solve the problem
• Personalization solves a contradiction which associates it
with a well-defined problem and has a non-linear
solution (a radical innovation)
Standardization
(mass
marketing/mass
production)
Personalization/
Mass
customization
Null configuration
Customization
(craft/artisan)
High
Scalability
Low
Low HighIndividuality

22. The Genius of Sergio Leone
The Good, the Bad and the Ugly
Sergio Leone’s Method
• Most movies had the script written, then shot, then
scored
• Leone’s method was to score the movie as it was written
and then shoot
• Leone played the music on set while filming, creating a
deep emotional experience shown in the actor’s
expressions and better timing of sequences in a shot
than was achieved without the music
• How something is presented is more important than
other factors like price

23. How PersonalizationWorks
Sublation – combination without a loss.
Evoke In Motion
• Evoke in motion – Emotions and motion go together.
Helping a customer evoke emotion and motion, helps to
engage them in their story arch. Making your customer
feel and act upon that emotion to create a perception
• Multiplicity - Undermining a single master narrative with
a network of intertext that make meaning of choice.
Personalization disobeys because most shopping
experiences are devoid of choice and have a beginning
and end. Personalization doesn’t have an middle or end,
it just evolves
Personalization
Philosophy
(Concepts)
Behavioral
Sci/Econ
Design
Data
Engineering
Data
Science
Social
Science and
Marketing
Data
Governance

24. Heart Graphic by Accenture
Philosophy of Personalization
ATEMPORALITY
IDENTITY
NETWORK CULTURE
HETEROTOPIAS
An unmooring from historical
methods. We are currently
experiencing an atemporal period of
consumer preferences.
Spaces, like identity, are constructed. Our
abilities are limited by the space in which
it is produced. Our identity is tied up in
the things we own and we seek to create
our perception of identity with those
things we own.
Extremely fluid, often poorly
organized but seemingly
persuasive even when wrong.
The network is us as a group,
understand the network to
understand the person.
A dynamic space of layers and
meaning. Margin spaces to explore
non-standard methods. We don’t
have a dominant culture, we have
many, personalization creates the
space for the identity.
The True Drivers of the Personalized World

25. BehavioralAspects
It’s always better to reduce anxiety and ambiguity than improve speed.
Human behavior is essentially social.
In all forms of society, there are three
constant characteristics:
• We always live in groups
• Groups have hierarchy with a defined structure of power
• People need to have a system of meaning that helps
them make sense of the world
These characteristics help people to get along, get ahead
and create meaning. Finding meaning, is the key to
understanding why people make decisions. People have a
need for meaning and structure in their life. How people
achieve these goals are determined by their personality.
Offline Self
Device Self
Online Self
Clash between
Today and Future
Aspirational
You
Present You
1-1

26. Behavioral Economics
JP Morgan: Everyone has two reasons for doing something, the
real reason and the good reason.
Strategic - “Transform the Business”
Focused on the ‘top-line’ value drivers associated
with growth, innovation and scale.
Tactical – “Run the Business”
Focused on the execution-based
savings that can be realized based on
optimizing the data model and data
maintenance processes.
Operational - ”Grow the Business”
Focused on savings driven based on improving
data quality, deploying a sustainable
governance model and optimizing our data
(and related) processes.
KPI’s
Cost of Business
Time to Complete
Number of Systems / Interfaces
Attributes & Hierarchy
(and related) Data Quality
Customer Journey By Numbers
Single Version of Truth
Supply Chain Optimization and Planning
Behavior Change or Encouragment
Estableshment of Meaning
Customer Profitablitiy Analysis
Creation of a new Customer Journey
Value Drivers

27. Design Strategy
Determines which stories need to be communicated to
whom and how to use the technologies and structures
before us to do so.
http://en.wikipedia.org/wiki/File:PazuzuDemonAssyria1stMilleniumBCE.jpg #kpnicholas

28. Desktop Mobile Tablet In Store Publications TV/Radio
Product
Packaging
Customer
Support
Ecommerce
Customer Profile
Product and Service Content
Social Media Opportunities
Advertising and media
Sales’ Support and Tools
Apps
Personalization
Customer Profile
Email
Execution of Design and Behavior
#kpnicholas

29. Wisdom
Collective application
of knowledge into
action
Knowledge
Experience, values, context
applied to a message
Information
A message meant to change receiver’s
perception
Data
Discrete, objective facts about an event
Experience
Grounded Truth
Complexity
Judgement
Heuristics
Values & Beliefs
Quantitative
Contextual
Evaluative
Qualitative
Intuitive
Informative
Quantitative
Connectivity
Transactions
Informative
Usefulness
Quantitative
Cost, Speed
Capacity
Timeliness
Relevance,
Clarity
Adding Value:
Action-oriented
Measurable efficiency
Wiser decisions
Adding Value:
Contextualized
Categorized
Calculated
Corrected
Condensed
Adding Value:
Comparison
Consequence
Connections
Conversations
Transitioning to emerging technologies
+
Human/Machine
=
Transformation
Data Lakes without Substance lack any meaning.
Data Engineering Plays a Better Role

30. Data Engineering
Collection of data streams. The more ways you can look at something, the richer you are.
Cultural
Behavioral
Product
Clickstream
Local
Events
• Probability of engagement /LTV
• Probability of Growth/Churn
• Network Patterns (Human)
• Life Changing Events
• Server Response Time
• Data Reliability
• Network Patterns (Tech)
Enriched data for
downstream teams

31. Data Science as aTeam Player
Signals have attributes depending on their representation in time or frequency. Domain can
also be categorized into multiple classes
All signal types have certain qualities that describe how quickly signals can be generated
(frequency), how often the signals vary (rate of change), whether they are forward
looking (quality), and how responsive they are to stimulus (sensitivity)
Rate of Change
(Slow or Fast)
Quality
(Predictive or Descriptive)
Sensitivity
(Sensitive or Insensitive)
Frequency
(High or Low)
Sentiment
Expressed as
positive, neutral,
or negative, the
prevailing
attitude towards
and entity
Behavior
These signals
identify
persistent
trends or
patterns in
behavior over
time
Event/Alert
A discrete signal
generated when
certain
threshold
conditions are
met
Clusters
Signals based on
an entity’s
cohort
characteristics
Correlation
Measures the
correlation of
entities against
their prescribed
attributes over
time
Humans don’t act logically.

32. The Soft Side of Data Science
A Maturity Model: Four Phases of Data-Driven Culture
non-
quantitative
(“intuitive”)
quantitative /
static
(“statistics is not
machine learning”)
quantitative /
dynamic
(a culture of machine
learning / experimental
design)
quantitative /
dynamic with
human
intelligence
(a culture of machine
learning / experimental
design)
Human behavior doesn’t run on objective behavior, it runs on context and meaning

33. Design of Experiments (DoE) Social Science and Marketing
Ideas
Build
Product
Measure
Data
Learn
Minimize total time through the loop
Experimental design of new data products requires a
framework that allows us to test ideas quickly and
allows us to get to answers for production products
faster. DoE and Lean innovation can help us do that.
Working with production product teams, business
owners and UX to frame up experiments will help Big
data and Data science teams get to results faster,
using this model.
Innovation at the edges, build for the middle and you have
competitors, for the edge and you own the market.

34. Social Science and Marketing
We Begin To build A Trusted Relationship
Imagine the Potential Realize the Potential
Customer Journey
Perception Journey
Emotional Journey
Moment of Truth
Signaling Actualizing Personal Growth
Personalization Influencers
Social Marketing
Network Influencers
Search and
Browse
Peer Reviews Email
Data Refining
Recommenders
Positive
Psychology
Community
Building
Growth via new experiences
Network Influencing
Validation
Marketing is not about optimizing against our competitors, it is about optimizing the experience in the context you
and the customer define. Don’t compete, monopolize so you can removed the competitors from the equation.

35. Traditional Data Governance is Not Enough
Data
Governance
Risk
Mitigation
Data
Governance
Data Quality
Master Data
Management
Legal and
Ethics
Team Building
Data Strategy and Governance is about building a
single point of truth which allows:
• We should look to improve the experience of the
customer
• Innovation takes place around the edges, be
comfortable with ambiguity
• Over administrative often kills the ability to be
successful.
“The typical data quality issues, as well as the typical impacts of poor data quality. The increased costs are
dramatic: between 8 and 12% in revenue and between 40-60% in expenses.” Thomas C Redman author of
“The Impact of Poor Data Quality”

36. The Soft Side of Data
culture strategy
Culture Precedes Strategy
strategy technology
Strategy Precedes Technology
Using mathematical models, often won’t work because how we perceive things is not
objective. The way you get people to behave in a certain way, may be oblique

37. How PersonalizationWorks
The Lessons Learned.
Personalization is Unique, because We
Are
• We are not designed to see the world objectively.
Evolution doesn’t care about accuracy, it cares about
fitness. We notice contrast more than absolute values.
This is why using math and logic, doesn’t always work,
because we forget the basics of what nature does
• Don’t just test or build the obvious, test and build the
unconventional too. Everyone competes to be logical so
they end up in the same place which is the most
saturated part of the market. Real winners don’t win by
being logical like everyone else, they win by being
unconventional
Personalization
Philosophy
(Concepts)
Behavioral
Sci/Econ
Design
Data
Engineering
Data
Science
Social
Science and
Marketing
Data
Governance

38. Brainswarming
When a team runs into a problem they have difficulty solving, all
teams come together in a brainswarm model to help come up
with a solution. Use the network of the team to solve problems,
more points of view makes a team rich and more effective at
engaging customers in new experiences.

39. Does thisWork?
Results matter, so here are the results of this method
There is Still a Lot to Do.
• Improved conversion rates by 400% in 3 months
• Reduced the cost of data management from $8 million
to $3 million a year
• Increased customer lifetime value by improving repeat
customers
• Better employee satisfaction, turnover dropped from
15% to 2% Break Even
Break Through
Break Away

40. The Future
Is our online self, representative of the whole? Can your
online persona create a persona of its own and give you a
different reaction online than in real life?
There is Still a Lot to Do.
• AI needs to learn to be trivial. AI doesn’t practice
defensive decision making which can allow it to be
creative where sometimes humans are not. But we
have to make sure it doesn’t have the biases of the
trainer
• The meaning of a signal is changing, to what is the
question still unanswered
• Perception will continue to drive reality

41. ThankYou
Edward Chenard
Edward@echenard.com
www.echenard.com