The Increasing Criticality of MDM for Personalization for Customers and Employees Master data management seems to be one of those perennial, evergreen programs that organizations continue to struggle with. Every couple of years people say, “we're going to get a handle on our master data” and then spend hundreds of thousands to millions and tens of millions of dollars working toward a solution. The challenge is that many of these solutions are not really getting to the root cause of the problem. They start with technology and begin by looking at specific data elements rather than looking at the business concepts that are important to the organization. MDM programs are also difficult to anchor on a specific business value proposition such as improving the top line. Many initiatives are so deep in the weeds and so far upstream that executives lose interest and they lose faith in the business value that the project promises. Meanwhile frustrated data analysts, data architects and technology organizations feel cut off at the knees because they can't get the funding, support and attention that they need to be successful. We've seen this time after time and until senior executives recognize the value and envision where the organization can go with control over its data across domains, this will continue to happen over and over again. Executives all nod their heads and say “Yes! Data is important, really important!” But when they see the price tag they say, “Whoa hold on there, it's not that important”. Well, actually, it is that important. We can't forget that under all of the systems, processes and shiny new technologies such as artificial intelligence and machine learning lies data. And that data is more important than the algorithm. If you have bad data your AI is not going to be able to fix it. Yes there are data remediation applications and there are mechanisms to harmonize or normalize certain data elements. But looking at this holistically requires human judgment: understanding business processes, understanding data flows, understanding dependencies and understanding of the entire customer experience ecosystem and the role of upstream tools, technologies and processes that enable that customer experience. Until we take that holistic approach and connect it to business value these things are not going to get the time, attention and resources that they need. Seth Earley, Founder & CEO, Earley Information Science Dan O'Connor, Senior Product Manager at inriver

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WEBINAR
WEBINAR
SPEAKERS
Master Data Management and Personalization: Building
the Data Infrastructure to Support Orchestration
SETH EARLEY
CEO & FOUNDER
EARLEY INFORMATION SCIENCE
THANK YOU

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Today’s Speakers
Seth Earley
Founder & CEO
Earley Information Science
Seth@earley.com
https://www.linkedin.com/
in/sethearley/
2
Dan O’Connor
Senior Product Manager
inriver
Dan.oconnor@inriver.com
https://www.linkedin.com/in/
dan-oconnor/

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BeforeWe Get Started
WE ARE RECORDING SESSION WILL BE
50 MINUTES PLUS
10 MINUTES FOR
Q&A
YOUR INPUT IS
VALUED
Link to recording & slides
will be sent by email after
the webinar
Use the Q&A box to
submit questions
Participate in the polls
during the webinar
Feedback survey afterward
(~1.5 minutes)
Thank you to our media partners : CMSWire & Marketing AI Institute
3

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Ideas for a presentation…
1. Introduce the concept of master data and explain why it's important for personalization. Discuss how master data management can help
organizations manage their data more effectively and ensure that the data is accurate, consistent, and up-to-date.
2. Discuss how personalization can benefit organizations and their customers. Explain how personalization can help to improve customer satisfaction,
increase customer loyalty, and drive revenue growth.
3. Explore the different types of personalization, including product recommendations, content personalization, and marketing personalization.
Discuss how each type of personalization can be used to improve the customer experience and drive business results.
4. Explain how master data can be used to support personalization. Discuss how customer data, product data, and other types of data can be used to
personalize the customer experience.
5. Discuss the challenges associated with master data management and personalization. Explain how organizations can overcome these challenges by
investing in the right tools, processes, and people.
6. Provide examples of organizations that are using master data and personalization to improve the customer experience and drive business results.
Discuss how these organizations are leveraging data to create personalized experiences that are tailored to the needs and preferences of their
customers.
7. Discuss the future of personalization and how master data management will continue to play a critical role in driving personalized experiences.
Highlight emerging technologies and trends that are shaping the future of personalization, such as artificial intelligence, machine learning, and
real-time data processing.
8. Finally, conclude your presentation by summarizing the key takeaways and highlighting the benefits of investing in master data management and
personalization. Encourage your audience to consider how these concepts can be applied to their own organizations to drive business results and
improve the customer experience.
4
Source: ChatGPT
I need ideas for a presentation on master data and personalization
4

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Agenda
• Why master data is important for personalization
• Personalization challenges
• Why MDM projects fail or under deliver
• Domains of master data
• Content
• Product
• Customer
• Sales and Financial
• Mechanics of personalization – dynamic product catalogs
• Product recommendations
• Content personalization
• Marketing personalization
• Getting started –tips tricks, areas to pay attention to, governance, etc.
5

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About Earley Information Science
6
Proven methodologies to organize information and data.
SELL MORE
PRODUCT
SERVICE
CUSTOMERS
EFFICIENTLY
INNOVATE
FASTER
1994
YEAR FOUNDED.
Boston
HEADQUARTERED.
50+
SPECIALISTS & GROWING.

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Poll
7
1. Initial investigation
2. PoC’s and small-scale pilots
3. Operationalized for departmental or
functional applications (search,
knowledge management)
4. Enterprise-wide deployment
5. None of the above
Where are you on your MDM journey?

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What is MDM?
8
Gartner definition*:
Master data management (MDM) is a technology-enabled discipline in which business and IT
work together to ensure the uniformity, accuracy, stewardship, semantic consistency and
accountability of the enterprise’s official shared master data assets. Master data is the
consistent and uniform set of identifiers and extended attributes that describes the core
entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies
and chart of accounts.
*https://www.gartner.com/en/information-technology/glossary/master-data-management-mdm

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What is MDM?
9
Gartner definition*:
Master data management (MDM) is a technology-enabled discipline in which business and IT
work together to ensure the uniformity, accuracy, stewardship, semantic consistency and
accountability of the enterprise’s official shared master data assets. Master data is the
consistent and uniform set of identifiers and extended attributes that describes the core
entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies
and chart of accounts.
*https://www.gartner.com/en/information-technology/glossary/master-data-management-mdm
(Oddly, product data is not in this definition)

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The Need for MDM
10
• The data ecosystem of most organizations is highly fragmented
• Systems and processes grow in complexity over time
• Acquisitions require costly work to integrate which does not get fully funded
• When new systems are deployed in many cases, they do not follow a standard architecture
• Naming conventions can be ad hoc, the same concept is referred to by different names in
different systems
• The same name can mean different things in different systems
• Integration and analysis becomes very difficult and time intensive
• Entities need to be mapped and translated to enable analysis
This makes it difficult to perform routine analysis
Answers can vary depending which system is queried
It is often impossible to have a 360-degree view of the customer

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Symptoms of an MDM Problem
11
“We don’t have a consistent view of our customers”
“Our customer data is a mess”
“Product data is incomplete”
“Our product catalog is difficult to navigate”
“Attribute data does not reflect what customers look for”
“Customers and support teams cannot answer questions quickly”
“We don’t know why customers convert”
“It’s difficult to collect metrics because each system has different
ways of describing touchpoints”

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How PersonalizationWorks
12
“We’re using AI”
AI is only as good as the data that powers the algorithm
Personalization depends on understanding the user in detail – who they are,
what they need and how they think about the problems they are trying to solve.
We need to understand the user’s mental model, then present products,
content, and information based on that mental model
To do so, we need to be able to align the descriptors of the customer
(Customer MDM) with products (Product MDM) and content (Content MDM)

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ProductInformation,Contentand the Customer Journey
Internal audiences need to easily
find, share and reuse content, data
and insights to support the
external customer experience
Merchandizers
Product managers
Category owners
Promotions
Marketing plans
Product strategy
Merchandising calendar
MARKETING PROMOTION /
PLANNING
PRODUCT
DEVELOPMENT
Product
Data/Content
Product Content / Product
Assets
PIM
PRODUCT
ONBOARDING
PIM
Manager
Catalog
Manager
Merchandizer
Product Information Management
Campaigns
Email Marketing
Social media
Promotions
DEMAND
GENERATION
$
Marketing managers
Marketing analysts
CONTENT STRATEGY
Editorial manager
Content manager
Category manager
Product content
Product assets
Marketing plans
ECOMMERCE
PERSONALIZATION
STRATEGIES
Purchase history
Demographics
Interest profile
Buyer persona
CUSTOMER SUPPORT
Call Center
Agents
Documentation
Warranty
Knowledgebase
Content/data source
Person/role
Collaboration
PROCESS
Support managers
K-base owner
CUSTOMER
SELF SERVICE
Reviews
Manuals
Knowledgebase
Regional managers
Market Analyst
Merchandizer
Market data
Regional demographics
Store sales
PROMOTIONS
Collaboration, Insights and Knowledge Sharing
Content Optimization
Customer Journey

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Intelligent Content Lifecycle
Internal audiences need to easily
find, share and reuse content, data
and insights to support the
external customer experience
Merchandizers
Product managers
Category owners
Promotions
Marketing plans
Product strategy
Merchandising calendar
MARKETING PROMOTION /
PLANNING
PRODUCT
DEVELOPMENT
Product
Data/Content
Product Content / Product
Assets
PIM
PRODUCT
ONBOARDING
PIM
Manager
Catalog
Manager
Merchandizer
Product Information Management
Campaigns
Email Marketing
Social media
Promotions
DEMAND
GENERATION
$
Marketing managers
Marketing analysts
CONTENT STRATEGY
Editorial manager
Content manager
Category manager
Product content
Product assets
Marketing plans
ECOMMERCE
PERSONALIZATION
STRATEGIES
Purchase history
Demographics
Interest profile
Buyer persona
CUSTOMER SUPPORT
Call Center
Agents
Documentation
Warranty
Knowledgebase
Content/data source
Person/role
Collaboration
PROCESS
Support managers
K-base owner
CUSTOMER
SELF SERVICE
Reviews
Manuals
Knowledgebase
Regional managers
Market Analyst
Merchandizer
Market data
Regional demographics
Store sales
PROMOTIONS
Collaboration, Insights and Knowledge Sharing
Content Optimization
Customer Journey
Product Data Maturity
Content Optimization Maturity
Knowledge Process Maturity
Customer Experience Maturity
Monitored by Metrics and
Governance Playbook to
Track Progress, ROI and
Course Corrections

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Poll
15
1. Product master data
2. Content structure and optimization
3. Customer master data
4. Financial master data
5. Something else (let us know in Q&A tab)
Which areas are most important to your project or
organization?

16. item: color, size resource type
filename
variants
product package alternatives
up-sell
product packages
cross-sell
name
description
(in several
langugages)
brand
market
material
service parts
121 HP
146 HP
Product Data Maturity

17. PIM
• Single Source of Truth for
Product Information
• Product Marketing Focused
to Create a Single Marketing
Message Across All Usage
• Primary Purpose is to
Syndicate Product Data to All
Sales/Marketing Channels
MDM
• Single Source of Truth for
Multiple Domains
• Managing Internal Data
Standardization and
Aggregation
• A system of Tools and
Processes as Part of a Data
Governance Process with a
Goal of Standardizing Data

18. Source
onboard product data
Enrich
enrich product data
Publish
distribute product data
Analyse
optimize product data
BOT
PIM

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Product MDM
19
Product data is sourced from multiple systems
Enriched with merchandizing and marketing data
Populated with attributes that enable dynamic view of product catalog
Audience = Network Engineer

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Product MDM
20
Product data is sourced from multiple systems
Enriched with merchandizing and marketing data
Populated with attributes that enable dynamic view of product catalog
Sort by Audience

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The Integration, Navigation, Retrieval Challenge
21
Order
Management
ERP CRM Support eCommerce
Data Data Data Data Data
Customer
Content
Contract
Customer
Content
Contacts
Account Customer
Personas
Product
Product Contact Info
Customer
Orders
Product
Content
Customer
Prospect
Content
Operations
Data
BOM
Content
PLM
Content
Supply Chain
Data
Customer
Supplier
BOL + M
Transport
Product
Product
Product
Product
Where is the customer master?
Where is the product master?
Where is content?
Where is sales and order history?
20
Sales
Orders

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Most OrganizationsToday:
22
Engineering
PIM, PLM, PCM,
CPQ
Proposal Processes
Content authoring, content
management systems
Marketing
Campaign management,
go to market planning,
offerings development
Sales
Proposal development,
presales support
Support
Self service content, call
center support content
Demand Gen
Content authoring, content
management systems
Presales Support
Pitch decks, demonstration
scripts
Collateral Development
Sales asset management
Content Operations
Content authoring, content
management systems
Email Marketing
Content authoring, content
management systems
Product Data Operations
Product content, product
information
Digital Operations
Content authoring, content
management systems
Field Service
Field service management
systems
Self Service Support
Knowledge curation, knowledge
bases
Promotion Management
Regional Marketing
Engineering Content
Operations
Lifecycle mgt, support,
manufacturing processes
Call Center
Content authoring, content
management systems
AI/ Cognitive Assistant
Content Operations

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Most Organizations in the Future:
23
Engineering
PIM, PLM, PCM,
CPQ
Proposal Processes
Content authoring, content
management systems
Marketing
Campaign management,
go to market planning,
offerings development
Sales
Proposal development,
presales support
Support
Self service content, call
center support content
Demand Gen
Content authoring, content
management systems
Presales Support
Pitch decks, demonstration
scripts
Collateral Development
Sales asset management
Content Operations
Content authoring, content
management systems
Email Marketing
Content authoring, content
management systems
Product Data Operations
Product content, product
information
Digital Operations
Content authoring, content
management systems
Field Service
Field service management
systems
Self Service Support
Knowledge curation, knowledge
bases
Promotion Management
Regional Marketing
Engineering Content
Operations
Lifecycle mgt, support,
manufacturing processes
Call Center
Content authoring, content
management systems
AI/ Cognitive Assistant
Content Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations
AI/ Cognitive
Assistant Content
Operations

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LargeTech Organization
24
Engineering
Content Operations
Product lifecycle
management and technical
documentation
Content authoring, content
management systems, including
Sales
Marketing
Campaign management, go
to market planning, offerings
development
Proposal development,
presales support
Support
Self service content, call
center support content
Create Once, Publish Everywhere
(COPE)
AI/ Cognitive
Assistant Content
Operations

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Componentized Content
25
Can be assembled into various configurations
Messaging can be optimized using machine learning to optimize conversion
Hero image 1
Hero image 2
Hero image 3
Hero image n
Value prop 1
Value prop 2
Value prop 3
Value prop n
Message 1
Message 2
Message 3
Message n
CTA 1
CTA 2
CTA 3
CTA n
Standard footer
Multi variant testing allows
for optimization of
combinations that get the
best result given customer
characteristics (customer
master attribute data)

26. Using Metrics & KPIs to Focus Governance
Measuring here
(business outcomes)
Measuring here
(process indicators)
Enterprise Strategy
Business Unit Objectives
New Business Opportunities
Average Order Size Total Account Revenue
Business Processes Site Traffic Search Relevance
Search
Digital Content
Working & Measuring
here (content, IA,
taxonomy, search, data
fill, etc.) Web
Content
CRM
Processes
enable objectives
L
I
N
K
A
G
E
Leads
Revenue Growth
Content supports
processes
Objectives align
with strategy
CEO: “How will this increase revenue?”
Conversion
Content Scorecards
Process Scorecards
Outcome Scorecards
CTR Fill Rate Content Quality etc.
Digital Team: “How do I know taxonomy / content / search is working?”

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CUSTOMERJOURNEY:LIFECYCLE/ENABLINGTECHNOLOGIES
LEARN CHOOSE PURCHASE USE PAY SUPPORT
MARKETING SALES DISTRIBUTION SERVICE FINANCE SUPPORT
Marketing
Communications
B2B/Channel
Partners
B2C/Retail
Fulfillment
Inventory
management
Product
performance
Billing & payment
Credit & collections
Help & complaints
Repair & returns
ENTERPRISE PROCESSES: DEPARTMENTS/FUNCTIONAL AREAS/ACCOUNTABILITIES
Technologies
Departments
Processes
Accountabilities
Marketing ops
Product marketing
Marketing comm
Digital marketing
Training
Retail/dealers
Web marketing
Channel management
Telemarketing
Sales support
Logistics
Installation
Activation
Service operations
Applications
Quality assurance
Finance
Billing operations
Credit & collections
Customer care
Executive escalations
Call center operations
• Bots (chat, helper,
virtual assistants)
• Event management
• Webinar tools
• Promotion management
• Social media
• Marketing resource
management
• Bots (chat, helper,
virtual assistants)
• Ecommerce
• CRM
• Web content management
• Sales management
• Marketing resource
management
• Bots (chat, helper,
virtual assistants)
• Inventory management
• Supply chain
• Logistics and distribution
• Point of sale and systems
• Bots (chat, helper,
virtual assistants)
• Knowledge base
• Online documentation/
help systems
• Bots (chat, helper,
virtual assistants)
• Ecommerce
• CRM
• Billing system
• Web content management
• ERP/accounting
• Credit card
authorizations/EFT
• Bots (chat, helper,
virtual assistants)
• CRM
• Knowledgebase/
unsupervised support
• Online documentation/
help systems
• Call center call tracking
• Trouble ticketing
Data/Technology Scorecards
Process Scorecards
Outcome Scorecards
Journey Stage
29

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Reactive vs Proactive Personalization
Difference between reactive personalization and proactive
• Reactive personalization is retrospective and more difficult to
automate (chasing trends and changing product content to match
trend)
• Proactive personalization uses other sources of data to optimize
the experience (real time “digital body language”)
Goal is to dynamically display products, content and
supporting information, based signals from “data exhaust”
of the rest of the customer experience technology stack
30

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The User’s“Digital Body Language”
How do we
describe
context?
With
metadata.

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Explicit and Implicit Customer Metadata
Where do we get
metadata?
By collecting signals
instrumented
throughout the user
journey
www.linkedin.com/in/sethearley

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Using a “High Fidelity” Journey Map
31
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Understand the customer journey
Identify details of the customer
Define content needed White Paper Product compare tool
Installation guide
Static Customer Data Dynamic Customer Data

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high-fidelity customer journey model
What does it take to do this right?
customer model
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
32
INTELLIGENT
PERSONALIZATION
Component content model
User journey/customer model
Product data model
Knowledge architecture
Static metadata:
(industry, role, interests,
firmographics, etc.)
Dynamic metadata identifies changing, real time. signals about
customer goals and intent while they go through their journey
Customer Data
Platform
Action = Download white
paper
Action = Product
compare, purchase
Action = Download
installation guide
Action = Open offer email,
click through to site, click
offer
Dynamic customer model
Dynamic metadata:
campaign responses, click
through, recent purchases,
new goals change customer
metadata model, and
therefore audience
descriptors real time
Delivering Personalized Customer Experiences –At Scale

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high-fidelity customer journey model
Dynamic customer model
customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
33
INTELLIGENT
PERSONALIZATION
Component content model
User journey/customer model
Product data model
Knowledge architecture
Static metadata:
(industry, role, interests,
firmographics, etc.)
Dynamic metadata:
campaign responses, click
through, recent purchases,
new goals change customer
metadata model, and
therefore audience
descriptors real time
Customer Data
Platform
Top of funnel content
(background on the issues
and challenges)
Content type = White Paper
Topic = Introduction
Industry = Insurance
Stage = Awareness
Role = Technical
Product = Basic Widget
Product
Offer = New customer
Action = Download white
paper
1
Delivering Personalized Customer Experiences –At Scale
What does it take to do this right? Dynamic metadata identifies changing, real time. signals about
customer goals and intent while they go through their journey

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high-fidelity customer journey model
Dynamic customer model
customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
34
INTELLIGENT
PERSONALIZATION
Component content model
User journey/customer model
Product data model
Knowledge architecture
Static metadata:
(industry, role, interests,
firmographics, etc.)
Dynamic metadata:
campaign responses, click
through, recent purchases,
new goals change customer
metadata model, and
therefore audience
descriptors real time
Customer Data
Platform
Top of funnel content
(background on the issues
and challenges)
Content type = White Paper
Topic = Introduction
Industry = Insurance
Stage = Awareness
Role = Technical
Product = Basic Widget
Product
Middle of funnel content
(product selector,
comparisons)
Content type = Product
compare tool
Topic = How to decide
Industry = Insurance
Stage = Shop
Role = Technical
Product = Deluxe Widget
Offer = New customer
Offer = New customer
Action = Download white
paper
Action = Product
compare, purchase
2
What does it take to do this right? Dynamic metadata identifies changing, real time. signals about
customer goals and intent while they go through their journey
Delivering Personalized Customer Experiences –At Scale

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high-fidelity customer journey model
Dynamic customer model
customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
35
INTELLIGENT
PERSONALIZATION
Component content model
User journey/customer model
Product data model
Knowledge architecture
Static metadata:
(industry, role, interests,
firmographics, etc.)
Dynamic metadata:
campaign responses, click
through, recent purchases,
new goals change customer
metadata model, and
therefore audience
descriptors real time
Customer Data
Platform
Top of funnel content
(background on the issues
and challenges)
Content type = White Paper
Topic = Introduction
Industry = Insurance
Stage = Awareness
Role = Technical
Product = Basic Widget
Product
Middle of funnel content
(product selector,
comparisons)
Content type = Product
compare tool
Topic = How to decide
Industry = Insurance
Stage = Shop
Role = Technical
Product = Deluxe Widget
Post purchase support
content (install guides,
troubleshooting info)
Content type = Installation
guide
Product = Deluxe Widget
Offer = New customer
Offer = New customer
Action = Download white
paper
Action = Product
compare, purchase
Action = Download
installation guide
3
Delivering Personalized Customer Experiences –At Scale
What does it take to do this right? Dynamic metadata identifies changing, real time. signals about
customer goals and intent while they go through their journey

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high-fidelity customer journey model
Dynamic customer model
customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
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INTELLIGENT
PERSONALIZATION
Component content model
User journey/customer model
Product data model
Knowledge architecture
Static metadata:
(industry, role, interests,
firmographics, etc.)
Dynamic metadata:
campaign responses, click
through, recent purchases,
new goals change customer
metadata model, and
therefore audience
descriptors real time
Customer Data
Platform
Top of funnel content
(background on the issues
and challenges)
Content type = White Paper
Topic = Introduction
Industry = Insurance
Stage = Awareness
Role = Technical
Product = Basic Widget
Product
Middle of funnel content
(product selector,
comparisons)
Content type = Product
compare tool
Topic = How to decide
Industry = Insurance
Stage = Shop
Role = Technical
Product = Deluxe Widget
Post purchase support
content (install guides,
troubleshooting info)
Content type = Installation
guide
Product = Deluxe Widget
Product = New and
Improved Super Widget
Post purchase nurture
content (how to get the
most from your Deluxe
Widget)
Content type = User tips
Product = Deluxe widget
Content type = Promo
Product = Super Deluxe
widget
Offer = New customer
Offer = Existing customer
Offer = New customer
Action = Download white
paper
Action = Product
compare, purchase
Action = Download
installation guide
Action = Open offer email,
click through to site, click
offer
4
What does it take to do this right? Dynamic metadata identifies changing, real time. signals about
customer goals and intent while they go through their journey
Delivering Personalized Customer Experiences –At Scale

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www.earley.com
Personalization at Scale Begins with a well-designedArchitecture
• Product data, content, the customer
experience and knowledge of how you
solve customer problems are the
foundation of your value.
• You can’t automate a mess
• You can’t automate what you don’t
understand
• Simplicity is hidden complexity
• Clean data is the price of admission
• Identify user journeys, data sources and
data owners
• Define governance, curation, and
scalable processes
37

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Getting Started – Map Data => Process => Outcome along the Customer Journey
38
1. Map the customer journey –what does the customer lifecycle look like internally and what
does the experience look like externally
2. Survey data sources that support each stage of the customer journey
3. Focus on the most important technologies for each stage of the journey
4. Determine the health/quality of data for the most important technologies supporting each
stage
5. Identify the accountabilities for each stage with process and outcome metrics
6. Gather baselines metrics for:
1. Data quality: completeness, accuracy, consistency, fitness to purpose
2. Processes that are support the customer at that stage
3. Business outcomes
7. Project the impact of an improved process or user experience on outcomes supported by
improved master data

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Getting Started – Start Simply
39
1. Don’t try top-down large scale MDM programs
2. Select a stage of the journey that is performing below expectations
3. Focus on upstream data and processes on that stage
4. Define customer attributes that describe intent at each stage
5. Experiment with product recommendations based on attributes (changing
assortments, surfacing products that align with customer persona processes,
etc.)
6. Cleanse the data as necessary (for a segment of customer, category of
product, etc.)
7. Include content modeling and metrics as part of the process
8. AI is part of as solution, it is not the entire solution

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Getting Started
40
Schedule a consultation with EIS
• Product data readiness assessment
• Wordmap Commerce demo (with your data)
To schedule a conversation, send a note to:
Carolyn.Southwick@earley.com

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Q&A
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Additional Reading
42
From Earley Information Science
title
link
title
Link
title
Link

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EarleyAI Podcast
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Listen to the Earley AI Podcast to explore what's
emerging in technology, data science, and
enterprise applications for artificial intelligence and
machine learning and how to get from early-stage
AI projects to fully mature applications.
Found wherever you listen to podcasts, including…
Henrik Hahn,
Chief Digital Officer,
Evonik
Dr. Mark Maybury, former
CTO at Stanley, Black &
Decker
RECENT EPISODES

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Contact
Seth Earley
CEO
Earley Information Science
________________________________________________
Cell: 781.820.8080
Email: seth@earley.com
Web: www.earley.com
LinkedIn: https://www.linkedin.com/in/sethearley
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Senior Product Manager inriver
Dan.oconnor@inriver.com
https://www.linkedin.com/in/dan-
oconnor/

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Thanks!
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