DMA - Art of Targeting and Personalization

Data | Domain | Delivery
Presented to:
Art of Targeting & Personalization
Stephen H. Yu
Associate Principle, Analytics and Insights

1Confidential eClerx – An ISO/IEC 27001:2005 Certified Company March 16
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What we will cover
DATA LANDSCAPE INSIGHTS VIA ANALYTICS WHY MODEL?
ART OF TARGETING "ANALYTICS-READY"
ENVIRONMENT
PROPER PERSONALIZATION
VIA DATA AND ANALYTICS

Where the Data Movement is Going
 No guessing game – You MUST know your target
 Vast amount of online & offline data collected
 But are they being used properly?
 Analytics play a huge role in prospecting & CRM
 Short paced marketing cycle getting shorter
 Marketers must stay relevant to their target to cut through the noise
 Huge difference between advanced marketers and those who are falling behind
 And it’s all about proper “Personalization”
Winners are the ones who know how
to stay relevant with their target by
wielding the power of data faster.

It is NOT about Channels or Technologies
 There is no such thing as an “online person”
– It is almost offensive
– Channel-centric view confuses buyers
– New channels and technologies in the future
» What then?
 This data business should be about “People”
– No one is one-dimensional
– “Buyer-centric” point of view
» Should NOT be channel-, product-, division- or brand-centric
» But most marketers are
» Buyer-centric data structure leading to proper “Personalization”
 Never about the technology, but about the people who are looking
at the new device (or even thin air)
– And they are in control, not marketers!
“The Future of
Online is Offline”
– Stephen H. Yu, 2002

 Take the data seriously, not just your
gut feelings
 Define the goals first, then control
the flow of data
 Don’t blindly trust machine based
solutions
 Be logical, as there are no toolsets
that read minds
 Set specific goals for small
successes
It is about the Data Users, too
 Don’t be a “Data Plumber”, but a
businessman
 Don’t be technology oriented, but
solution oriented
 Don’t do things just because you
can
For Decision Makers For Data Scientists

Leading with Analytics
“A database is
not just a sum of
all data sources,
and Analytics is
not just an array
of statistical
techniques”
1.Business Goals
2.Answers via Analytics / Modeling
3.Databases Optimized for Analytics
Solution design based on business goals, not around capabilities

What 1:1 Marketing is about
 Marketers must know:
– Whom to contact, and
– What to say, if they decided to contact
someone
» What to offer through what channel and
when
 Analytics help marketers with both:
– Targeting, and
– Personalization
Everyone is being
bombarded with
marketing
messages through
multiple channels

Different Types of Analytics
“ANALYTICS” MEANS DIFFERENT THINGS…
BI (Business
Intelligence) Reporting:
Display of success metrics,
dashboard reporting
Descriptive Analytics:
Profiling, segmentation,
clustering
Plus, “Prescriptive Analytics” for All Stages
Predictive Modeling:
Response models, cloning
models, value models,
revenue models, etc
Optimization:
Channel optimization,
marketing spending analysis,
econometrics models

Insights through Data Refinement
Insights are derived from data through the refinement process
 Data Collection by
Channel
 Rapid Data Retrieval
 Basic Dashboard
 Data Hygiene and
Standardization
 Consolidation and
Summarization
 Advanced Analytics
including Statistical
Modeling
 Comprehensive
Dashboard and BI
Reporting
 Ad hoc Reports
 Campaign Targeting
and Management
 Personalization
DATA PLAYERS MUST EXCEL IN:
Collection Refinement Delivery

Why Model?
Models summarize complex data into simple-to-use “scores", and fill
in the gaps by converting “unknowns” to “potentials”
 Increase targeting accuracy
 Reduce costs by contacting less/smart
 Stay relevant with target customers
 Achieve consistent results
 Reveal hidden patterns in data
 Reach marketing automation faster
 Expand the target universe
 “Supposedly” save time and effort

Why NOT Model?
 Universe too small
 Predictable data not available
 1:1 marketing channels not in
plan
 Tight budget
 Lack of resources
Really? Remember 1:1 Marketing
is about:
 Knowing whom to engage
 Knowing what to offer if you
decided to engage someone
Models provide answers for both

What is a Model?
Target vs. Non-Target, such as
 Buyer vs. Non-Buyer
 Responder vs. Non-Responder
 Loyal vs. Attrition
 High Value vs. Low Value
Defining target and non-target is equally critical
“A model is a
mathematical expression
of differences between two
dichotomous groups”

Art of Targeting
Remember T, C, M
1.Target
2.Comparison Universe
3.Methodology
 Defining the proper target is most critical even
more than the methodology
 Marketers must get involved in Target
Definition
– State the goals and usages clearly
– Don’t be a bad patient demanding specific
prescriptions
“Some targets
are not what
they seem…”
Start by hanging
the target in the
right place

Defining the Target (or Targets) (1/4)
 How frequent is frequent enough?
 How much is high enough value?
 How big is the size of the ideal target?
 Continuous Target

Multiple distinctive segments in
the target universe
For example,
 Infrequent Big Spenders
 Frequent Small Spenders
 New Customers
 Dormant Customers
 Geographic targets
 Demographic segments
 Multiple Targets

 Multi-step approach for multi-step
sales/marketing
‒ Sales pipeline (various stages of
lead qualification)
‒ Open-Click-Browse-Convert-Repeat
cycle
 Very narrow target in a big universe
 Sub-targets within major segments
Target within a Target

Inversely Related Targets
For example,
 Frequent shoppers with
low average spending
 Responsive prospects
with bad credit
 Build multiple models
and find cross-sections

Any Pain Implementing Models?
Modelers are fixing data all the time
Repeatedly rely on a few popular variables
Always need more variables
Takes too long to build models and deploy them
Inconsistencies shown when scored
Disappointing results!

Predictive Modelling is all about
“Ranking”
12 3
 Relational or unstructured
databases won’t cut it
 Must create “Descriptors”
that fit the level that needs to
be ranked
 Households
 Individuals
 Companies
 Email Addresses
 Products
Ultimately, models must
properly “Rank”
Define the level of
data accordingly

Single View of Customer
Web
CRM
Email
POS
Social
Media
Call
Centre
Mobile App
Mobile App
Call
Centre
CRM
Social
Media
POS
Web
Email

Marketing Database Supporting Analytics
 Database Optimized for Analytics
– Analytics supporting efficient Targeting and Personalization
– “Buyer-Centric” Portrait
» Transform Channel-, Product-, Division-, or Brand-Centric data to “Descriptors” of the
Target
 The Solution – “Analytical Sandbox”
– Additional table(s) without overhauling existing DB structure
– Ideal environment for:
» Analysts and analytical toolsets
» Model maintenance/scoring
– Finished groundwork for level-playing field
» Data Hygiene/Standardization
» Categorization/Tagging/Binning
» Data Consolidation
» Variable Creation
– End-to-end run – from data collection/refinement to campaign execution/backend
analysis

“Analytical Sandbox” Conceptual Flow
Analytics is not just about statistical techniques
Realign data to achieve accurate, consistent and speedy results
Hygiene / Edit
Categorization
Consolidation
Summarization
Variable
Creation
Model
Development
Model
Application
Reporting
Knowledge
Sharing
Results Analysis
Attribution
Analytical Sandbox

Customer-Centric Environment
MASTER
CUSTOMER
TABLE
W/ CONSISTENT
ID
TRANSACTION
HISTORY
PRODUCT &
AFFINITIES
OTHER
ACTIVITIES
DIRECT
MARKETING
PROMOTION
HISTORY
EMAIL
PROMOTION
HISTORY
OTHER
MARKETING
PROMOTION
HISTORY
DM
RESP
EM
RESP
Descriptors of Customers by
Product, Time-series, Amount,
Activity, Status, Etc.
Descriptors of Customers by
Promo/Response
(Adequate, Over & Under)
BEHAVIOR CHANNEL
Summarization &
Attribution
Summarization &
Variable Creation
Customer-Centric View

Monetary
Frequency
Recency
Sample Variables after Summarization
 Weeks since last online purchase
 Years since member sign up
 Days since last delinquent date
 Months since last response date
 Orders by offer type
 Orders by product/service type
 Payments by pay method
 Average days between transactions
 Total $ past 24 months
 Life-to-date spending
 Average dollars by channel
 Average dollars by product type
BEFORE AFTER SUMMARIZATION

 Addressing your customers by their first
names?
 Suggesting more of the same products
that they just purchased through
collaborative filtering?
 Collecting explicitly expressed
preferences and reacting to them?
 Keeping in touch with your customers
all the time?
 Customizing emails and landing pages
based on customer preference?
 Knowing when to contact through what
channel?
About Personalization
 But, maybe you are
‒ “Personally” annoying your
customers and prospects
‒ Personalizing a fraction of the base
and completely ignoring the others
‒ Personalizing sporadically only
when obvious trigger data become
available
Personalization is the big
buzzword now, but what does it
mean?
They are all better than copying and
pasting the same content to
everyone…

“Personalization is
about the Person”
Look at it from the
customer’s point of
view…
 Transform transaction, event, or product level data to:
‒ Describe people, not product
‒ Create 360-degree Single Customer View
(“Analytical Sandbox”)
 Develop “Personas”, then match products to them
‒ Not the other way around
‒ Fill in the gap with modeling
Personalization is about the Person
 No one is just an “online” or an “offline” person
 “Personalization Engines” are often overrated
(especially product level collaborative filtering is on
auto-drive mode)
 Raw SKU level data are utterly inadequate for
personalization

Verified “known” explicit data are
scarce even in this age.
 Data are often missing for targets who are:
‒ New to business
‒ Dormant
‒ New to channel
‒ Hiding their tracks
But, most personalization efforts are
done based only on “known” explicit
data!
 Need to maximize the value of available
data, even implicit or anonymous data
Even now, real data are hard to come by
DATA COVERAGE
RICHNESSOFDATA

Sample PersonasRaw Data
 Demographic / Firmographic
 Transaction Data / RFM Data
 Products & Services Used
 Promotion / Response History
 Channel Usage Data
 Lifestyle / Survey Responses
 Delinquent history
 Call / Communication Log
 Movement Data
 Survey / Social Media / Sentiments
Data to Answers via Modelling
 Likely to buy a luxury car
 Likely to take a foreign vacation
 Likely to be a wine enthusiast
 Likely to have a home office
 Likely to be a risk averse investor
 Likely to respond to free shipping offer
 Likely to be a high value customer
 Likely to be qualified for credit
 Likely to upgrade/leave/come back
Formulate the answers through modeling

1.Not even considering personalization yet; still spraying the same HTML to everyone
2.Some personalization is considered, but do not know where to start
3.Identified basic toolsets for personalization, but do not have specific data or technology roadmap
4.Created the data roadmap, but did not start thorough data inventory
5.Identified required data sources, but datasets are not cleaned up or consolidated for 360-degree
view of customers
6.Datasets are ready for personalization, but only with “known” explicit data; statistical modeling to
fill in the gaps is not considered yet
7.Tested personalization engines through major marketing channels of choice, employing collected
“known” explicit data
8.Creating model-based “personas” with all available data, filling in the gaps with statistical
techniques
9.Personalizing most messages and offers through every touch point, employing explicit data (“hot”
data) and implicit/inferred data (“personas”)
10.Collecting and utilizing results data to enhance targeting models, personas and personalization
engines continuously, leading to full automation
Data & Analytics Steps towards Proper
Personalization

Holistic Approach to Personalization
Data, analytics, content management and delivery working in
conjunction
INSTALL
PERSONALIZATION
ENGINE
TEST THE
ENGINE WITH
SIMPLE
SEGMENTS
PERSONALIZE
EVERY
MESSAGE
 Deployment of interactive
display capabilities
– Web
– Email
 Ground work for next steps
 Data-driven
personalization - secure
access to deeper data
 Employ all available
“known” explicit data
 Employ model-based “personas”
 Convert “unknown” to
“potentials”
 Extend personalization to
customers with little or no history
1 2 3
PHASE PHASE PHASE

Multiple Dimensions of a Person
Frequent Traveller
Early Adapter
Family Oriented
Bargain Seeker
Examples of Personas:
No one is one-dimensional

 More about messaging than targeting
 Pin a target individual into one
segment at a time
 Hard to update with reliable
consistency
 Group them first, describe them later
‒ End up calling everyone in a
segment the same way
Segments vs. Personas
 Built for 1 attribute at a time
 Describe an individual with multiple
attributes
 Identifies dominant characteristics of
a person via side-by-side comparison
 Each persona represents diverse
array of data
 Easy to update, 1 persona (i.e., one
model) at a time with consistency
 Ready for multi-channel marketing
Clustering/Segmentation/Cohorts Personas

“Personas” built for specific attributes
 Project small “known” attributes to large universes of “unknowns” in form of
model scores
 Fill in data gaps leaving no missing value – Scores for every record using all
available data
 Enable side-by-side comparison of attributes – Quickly find dominant
characteristics of an individual
 Simplify matching process between individuals and best suitable
products/services
 Support message “rotation” for an individual customer using multiple personas
 Lead to marketing automation – Simple scores are no burden to personalization
engines
Model-based Personas in Action

Examples of Model-based Personas
 Frequent Flyer
 Foreign Traveler
 Luxury Hotel
 Gourmet
 Wine Enthusiast
 Adventure Seeker
 Young Family
 Budget Conscious
 Family Oriented
 Romantic
 High-end / Luxury
 Seasonal
 Frequent Small Gifts
 Pre-packages
 Bargain Seekers
 Specialty Items
 Corporate Purchase
 Home Office
 Consumables /
Repeat Purchase
 Big Ticket Items
 Technology Buyers
 Early Adopters
 Trend Followers
For Hospitality Industry For Gift Industry For Business Solutions

 Modern 1:1 marketing is about proper targeting and personalization with the buyer-
centric view
 Business goals first; it is not about data or technology
 Invest in analytics – models can pack large amount data into simple answers to questions
 Databases must be optimized for analytics and modeling – maintaining consistency is the
key
 Add “Analytical Sandbox” to the existing data environment for end-to-end efficiency
 Personalization is about the person, not channel
 Expand the horizon: Personalize all the time for everyone through every touch point
 Move from simple segments to personas for constant and effective personalization
Key Takeaways

Questions?

DMA - Art of Targeting and Personalization

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