Marketers have more data available than ever but struggle to pull in together in a usable format. Customer Data Platforms promise to solve this problem by offering easy-to-deploy systems specializing in data unification and sharing. But can CDP really deliver on its promise? This workshop will equip you to understand the definition of a CDP, how CDPs differ from other systems, which features are shared by all CDPs and which are found in only some, the most important CDP use cases, how to select the right CDP, how to manage a successful deployment and where to look next for more information.

1. Using CDP to Make the Most of
Your Customer Data
David M. Raab
Customer Data Platform Institute
Martech Boston
October 1, 2018

2. CDP Overview

3. Growth

4. Why CDP? Why Now?
• More data: sources, types, volumes
• More demand: customers expectations, digital
transformation, marketer sophistication
• New worries: privacy compliance, loss of third
party data, Google/Facebook/Amazon control
• New technology: API connectors, unstructured
data stores, cloud storage, AI applications
• Resources available: experienced users,
developers, funding

5. Behind the Definition
• Packaged software = faster, lower cost, less
risk, more mature
• Persistent, unified customer database = data
from all sources, complete customer view,
organized for customer management
• Accessible to other systems = easy to share
data, change systems without disruption

6. CDP Components
Data Decisions Delivery
DMP
Web
CMS
Mobile
Email
External
Mobile
Web
CRM ExposeIngest Process
Standardize
and transform
Link identities
Aggregate and
index
Reformat
and
expose
Load data
Analytics Engagemen
t
Segment
Predict
Personalize
Orchestrate
Budget Content

7. Why It Works
• Prebuilt components save development time
• ‘Big data’ technology makes data
management easier than before
• It’s designed to one thing well
• Vendors are customer data specialists

8. CDP Use Cases

9. Strategy is a method to a goal.

10. Strategy vs Programs vs Resources
Strategy Programs
(Actions)
Resources
Low
Cost
High
Quality
Close
Relation
High
Status
Data
Sources
Data
Mgt
Delivery
Systems
Staff
Skills
1 Paid search 1 1
1 1 Display ads 1 1
1 1 1 Review Mgt 1 1
1 1 Coupon mailing 1 1
1 1 1 Field Events 1 1
1 1 Retargeting email 1 1
1 1 Recommendations 1 1
1 1 Personalize Web site 1 1 1
1 1 Loyalty Rewards 1 1 1
1 Personal Shop-bot 1 1 1
1 Extended Warranty 1
1 Sweepstakes 1 1
1 1 Influencers 1 1

11. Low Cost
Strategy Programs
(Actions)
Resources
Low
Cost
High
Quality
Close
Relation
High
Status
Data
Sources
Data
Mgt
Delivery
Systems
Staff
Skills
1 Paid search 1 1
1 1 Display ads 1 1
1 1 1 Review Mgt 1 1
1 1 Coupon mailing 1 1
1 1 1 Field Events 1 1
1 1 Retargeting email 1 1
1 1 Recommendations 1 1
1 1 Personalize Web site 1 1 1
1 1 Loyalty Rewards 1 1 1
1 Personal Shop-bot 1 1 1
1 Extended Warranty 1
1 Sweepstakes 1 1
1 1 Influencers 1 1
2 2 4 4

12. 12
Strategy <-> Programs <-> Resources

Use Cases

Tech Requirements

13. CDP Primary Use Cases
Things a CDP makes possible:
• Access Unreachable Sources
• Analyze Unified Customer Data
• Access Unified Customer View
• Orchestrate Unified Experience
• Reduce Operating Costs

14. CDP Use Cases vs Features
Access
Unreachable
Sources
Analyze
Unified
Customer Data
Access
Unified
Customer View
Orchestrate
Unified
Experience
Reduce
Operating
Costs
Example:
analyze
game
behavior log
analyze
customer
journey
feed churn
score to CRM
feed chatbot
log to call
center agent
automate
welcome kit
production
Features:
• Ingest any data type x x x x
• Prebuilt connectors x x x
• ID management x x x
• Persistent storage x x x x x
• Create ‘golden record’ x x x
• Real time query x x
• Reformat for access x x x x x
• Offer arbitration x
• Real time interaction x

15. Use Case Definition
Goal: Detect churn-risk events
Value: Reduce churn
Nbr Customers 100,000 / yr; reduce churn from 25% to 23% = 2,000 net
Value/Customer $200/customer; x 2,000 customers = $400,000 / yr
Measures: events detected, actions taken, churn rate
Steps
1 2 3 4 5
Task: ingest event
data
link events to full
customer profile
assess and send
alerts
react to alert measure
impact
• Inputs web, phone
events
selected events,
customer data
data, rules,
models
customers plus
actions
action history,
customer data
• Processes parse for key
events
identify customer,
assemble profile
score for risk, run
rules, pick action
send list to
delivery system
test to measure
impact
• Outputs key events
w/customer ID
event and profile
data
list of customers
plus actions
messages to
customers
long term
behavior report
• Systems web, CRM,
CDP
CDP CDP, rules,
predictive
CDP, delivery CDP, CRM
• People IT to set up,
data analysts
IT, data analysts model builders,
retention team
retention team,
delivery team
retention team,
analysts
• Gaps CDP, list of
events
CDP CDP, predictive
models
CDP, delivery
integrations
CDP, test
design skills

16. Delivery
execute email web mobile mail call center social ads display ads search ads POS
optimize “ “ “ “ “ “ “ “ “
create content “ “ “ “ “ “ “ “ “
message connectors “ “ “ “ “ “ “ “ “
audience connectors “ “ “ “ “ “ “ “ “
Ingestion connectors “ “ “ “ “ “ “ “ “
access connectors “ “ “ “ “ “ “ “ “
Decisions
campaigns
no-code
interface
multi-step
multi-
channel
journey
framework
schedule
based
trigger
based
real time
interact
rules select
messages
scores in
rules
analytics segment BI/ explore
ideal
customer
manual
models
automated
models
recommend
products
incremental
attribution
content
no-code
templates
store workflow
cross-
channel
dynamic
content
auto-test/
optimize
auto-classify
text, video
admin budgets
project
mgmt
marketing
plans
simulate
results
optimize
channel $
Data
ingestion structured
semi- & un-
structured
no-code set-
up
high
volume
batch real time stream API
find
deltas
storage raw detail
multi-table
data model
auto-add
attributes
manage PII
in-
memory
dynamic
scaling
industry/ B2B
data models
clean &
standardize
read
external
identity stitch offline match cross device
identify
devices
persistent
ID
lead to
account
anonymous
to known
golden
record
enrichment location intent personal postal B2B data
feature
extraction
external
device graph
external ID
graph
access extract API SQL/HQL
analytical
data sets
prebuilt
connectors
Martech Function Inventory

17. Function Mapping
Function Need Function Need Function Need
Ingestion Identity Access
 structured  stitch  extract
 semi- & un- structured  offline match  API access
 no-code set-up  cross device match  SQL/HQL
 high volume  identify devices  analytical data sets
 batch  persistent ID  prebuilt connectors
 real time  lead to account
 stream  anonymous to known
 API ingestion  golden record
 find deltas Enrichment
Storage  location
 raw detail  intent
 multi-table data model  personal
 auto-add attributes  postal
 manage PII  B2B data
 in-memory  feature extraction
 dynamic scaling  external device graph
 industry  external ID graph
 clean & standardize
 read external

18. Let’s take a
break.

19. CDP Need

20. CDP Heuristic
Do you have
customer
data?
Is it unified,
persistent, and
accessible?
You need a
CDP

21. CDP Heuristic
What are your
business
needs?
Where are
your gaps?
Is CDP the best
option to fill
those gaps?

22. CDP
Customer Data Solutions
Full
Service
Neustar,
Tapad
InfoSys,
Wipro
Epsilon,
Wunderman,
Deloitte
Self-
Service
Zapier,
Tableau
Tealium,
Celebrus,
Amperity
Treasure
Data, Lattice,
Fospha
BlueConic,
Lytics,
RedPoint
Sailthru,
QuickPivot
V12
Adobe,
Salesforce,
IBM, Oracle
IT
Service
Informatica,
Mulesoft
SQL Server,
Hadoop,
MongoDB
SAS, R,
RapidMiner
Certona,
Bloomreach
SiteCore,
MailChimp
Twilio
Tools Database Analytics Engagement
Channel
Delivery
Multi
Channel
Scope
ServiceLevel--
CDP

23. Self-Service Solutions
Customer Management Architecture
Data Decisions Delivery
systems that create and store
customer data
systems that decide which treatments
to give customers
systems that deliver customer
treatments
Source
Systems
Customer
Database
Customer
Analytics
Personalization
(Message
Selection)
Execution (Message
Delivery)

24. Self-Service Solutions
Customer Management Architecture
Data Decisions Delivery
systems that create and store
customer data
systems that decide which treatments
to give customers
systems that deliver customer
treatments
Source
Systems
Customer
Database
Customer
Analytics
Personalization
(Message
Selection)
Execution (Message
Delivery)
←‒‒Silos‒‒→ (gap) (gap) (gap) ←‒‒‒‒‒‒‒Silos‒‒‒‒‒‒‒‒→

25. Self-Service Solutions
Customer Management Architecture
Data Decisions Delivery
systems that create and store
customer data
systems that decide which treatments
to give customers
systems that deliver customer
treatments
Source
Systems
Customer
Database
Customer
Analytics
Personalization
(Message
Selection)
Execution (Message
Delivery)
←‒‒Silos‒‒→ ←Data CDP→ ←‒Analytics‒→ ←Personalize→ ←‒‒‒‒‒‒‒Silos‒‒‒‒‒‒‒‒→
←‒‒Silos‒‒→ ←‒‒‒‒Analytics CDP‒‒‒‒→ ←Personalize→ ←‒‒‒‒‒‒‒Silos‒‒‒‒‒‒‒‒→
←‒‒Silos‒‒→ ←‒‒‒‒‒‒‒‒Personalization CDP‒‒‒‒‒‒‒‒‒→ ←‒‒‒‒‒‒‒Silos‒‒‒‒‒‒‒‒→

26. Self-Service Solutions
Customer Management Architecture
Data Decisions Delivery
systems that create and store
customer data
systems that decide which treatments
to give customers
systems that deliver customer
treatments
Source
Systems
Customer
Database
Customer
Analytics
Personalization
(Message
Selection)
Execution (Message
Delivery)
←‒‒Silos‒‒→ ←‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒Marketing Suite‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒→
←‒‒Silos‒‒→ (gap) ←‒‒‒‒‒‒‒‒‒‒‒‒‒‒MAP/CRM/eCommerce‒‒‒‒‒‒‒‒‒‒‒‒→
←‒‒Silos‒‒→ (gap) ←‒‒‒‒Orchestration‒‒‒‒‒→ ←‒‒‒‒‒‒‒Silos‒‒‒‒‒‒‒‒→

27. Self-Service Solutions
Customer Management Architecture
Data Decisions Delivery
systems that create and store
customer data
systems that decide which treatments
to give customers
systems that deliver customer
treatments
Source
Systems
Customer
Database
Customer
Analytics
Personalization
(Message
Selection)
Execution (Message
Delivery)
←‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒CX Cloud (theory)‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒→
←CX Cloud→ (gap) ←‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒CX Cloud (reality)‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒→

28. CDP vs Other Systems
CDP
Data
Lake/WH
DMP
Data Hub,
Tag Mgr
MAP,
CRM
Marketing
Cloud, JOE
packaged
software      
persistent      
unified
customer data      
open access      
decisions &
delivery ?     

29. Architecture is Destiny
purpose technology
CDP store and activate full
customer detail
NoSQL storage, ID resolution,
SQL/API to share & activate
Data
Warehouse
analyze structured data SQL (RDBMS) with star schema
Data Lake store and access any: NoSQL, SQL, flat file
DMP select ad audiences flat file w/cookies as keys
IPaaS/ Tag
Manager
move data between systems APIs and process flows (no storage)
Marketing
automation
segment-based ad campaigns SQL database, email as ID, largely
fixed data structure
CRM agent/sales interactions Normalized SQL database
Marketing
Cloud/JOE
omni-channel customer
interactions
separate SQL database per
component

30. CDP
Deployment

31. CDP Product Features
Shared CDP Features Data Load Web Site Analytics
•Retain original detail •JSON load •Javascript tag •Segmentation
•Persistent data •Schema-free data store •Cookie management •Automated predictive
•Individual detail •On-premises option Mobile Apps Engagement
•Vendor-neutral access Identity Management •SDK load •Content selection
•Manage PII •Persistent ID Digital Ads •Multi-step campaigns
•Deterministic match •Audience API •Real-time interactions
•Probabilistic match •Cookie synch
Data Access Offline
•API/query access •Postal hygiene
•Real-time access •Name/address match
Business to Business
•Account-level data
•Lead-to-account
match

32. CDP Product Feature Frequency
(CDP Institute Sponsors)
0 5 10 15 20 25 30
Real-time interactions
Multi-step campaigns
Content selection
Engagement
Automated predictive
Segmentation
Analytics
Lead-to-account match
Account-level data
Business to Business
Name/address match
Postal address hygiene
Offline
Cookie synch
Audience API
Digital Ads
SDK load
Mobile Apps
0 5 10 15 20 25 30
Cookie management
Javascript tag
Web Site
Real-time access
API/query access
Data Access
Probabilistic match
Deterministic match
Persistent ID
Identity Management
On-premises option
Schema-free data store
JSON load
Data Load
Manage PII
Vendor-neutral access
Individual detail
Persistent data
Retain original detail
Shared CDP Features

33. CDP Institute Product Comparison
Data Management
• Data Access
• Identity Match
• Un/Semi Structured Data
• Web Site
• Mobile Apps
• Digital Ads
• Offline
• B2B
Analytics
• Segmentation
• Automated Predictive
Engagement
• Content Selection
• Multi-Step Campaigns
• Real-Time Interactions
www.cdpinstitute.org

34. Amperity Tealium AgilOne Quaero CrossEngage RedEye
Ascent360 Celebrus NGData Evergage Blueshift Lytics Alterian
mParticle Lattice Engines BlueConic Zylotech BlueVenn Optimove
PRDCT Lexer SessionM QuickPivot Lemnisk RedPoint
Shared CDP Features
Retain original detail Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Persistent data Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Individual detail Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Vendor-neutral access Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Manage PII Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Distinguishing Features
Data Management
Base Features
API/query access Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Real-time access Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Persistent ID Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N
Deterministic match Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Probabilistic match Y Y N N N N Y Y Y Y N N Y Y Y N N N N Y N Y N Y Y
On-premises option N N N N N Y N N N Y N Y Y N Y N N N Y Y N N N Y Y
Un/Semi-Structured
JSON load N Y Y Y Y Y Y Y Y N Y Y Y Y Y N Y Y Y Y Y Y N Y Y
Schema-free data store N Y N Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y N Y N
Web Site
Javascript tag Y N Y Y Y N N N Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y
Cookie management N N Y Y Y Y N N Y Y Y Y N Y Y Y Y Y Y Y N Y Y Y Y
Mobile Apps
SDK load N N Y N Y Y N Y Y N Y Y N Y Y N N Y N Y Y Y Y Y N
Digital Ads
Audience API Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Cookie synch N N Y Y Y Y N N N Y Y N N Y Y Y Y Y N Y Y Y Y Y Y
Offline
Postal address hygiene Y Y Y N N N Y N Y N N Y Y N Y Y N N Y N Y Y N Y Y
Name/address match Y Y Y Y N N Y N Y N N N Y N Y Y N N Y N Y Y N Y Y
Business to Business
Account-level data N N N Y N N Y N Y Y N N Y Y Y Y Y N Y N N Y Y Y Y
Lead-to-account match N N N Y N N Y Y Y Y N N Y Y Y Y N N Y N N Y Y Y Y
Analytics
Segmentation Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Automated predictive N N N N N N Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y
Engagement
Content selection N N N N Y Y N N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Multi-step campaigns N N N N N N N N N N N N N N N Y Y Y Y Y Y Y Y Y Y
Real-time interactions N N N N Y Y N N Y Y Y Y N Y Y N Y Y Y Y Y Y Y Y Y
Data Analytics Engagement
Vendors
by
Segment

35. Feature Mapping
Use Case
1
Use Case
2
Use Case
3
Use Case
4
Use Case
5
Use Case
6
Use Case
7
Use Case
8
Use Case
9
Shared CDP
Features
Retain original detail
Persistent data
Individual detail
Vendor-neutral access
Manage PII
Data Load JSON load
Schema-free data store
On-premises option
Identity
Management
Persistent ID
Deterministic match
Probabilistic match
Data Access API/query access
Real-time access
Web Site Javascript tag
Cookie management
Mobile Apps SDK load
Digital Ads Audience API
Cookie synch
Offline Postal address hygiene
Name/address match
Business to
Business
Account-level data
Lead-to-account match
Analytics Segmentation
Automated predictive
Engagement Content selection
Multi-step campaigns
Real-time interactions

36. What are your selection process
challenges?

37. Selection Project Toolkit
• Use Cases
• Vendor Comparison
• Demonstration Scenarios
• Request for Proposal
• Proof of Concept
• References
• Evaluation Matrix

38. Selection Mistakes to Avoid
• Missing requirements
• Look only at ‘leaders’
• Jump to conclusions
• Limited research
• Informal process

39. Tech Isn’t the Problem

40. Readiness Assessment
CDP Readiness Assessment Checklist
Management Support
customer centric approach
data-driven decisions
help to remove internal barriers
fund core martech
fund edge / test martech
Marketing Strategy
offer arbitration rules
customer journey goals
clear brand position, value prop,
target customers, business
strategy
Internal Skills
marketing analytics
marketing program/interaction
design
marketing technology (in
marketing or IT group)
Source Systems
existing data documented
adequate data quality
on-going governance in place
data accessible for extraction
API connections for real time
access
shared customer ID in place
Delivery Systems
personalized, dynamic content
shared content & decision rules
implemented separately by system
central decision rules (push content
ID & data to delivery systems)
central messaging (push render-
ready content to delivery systems)
batch connections to central
systems
real-time interactions with central
systems
Organization
channel managers use shared
segments, contents, decision rules,
programs
customer metrics replace channel
metrics as basis for compensation
customer metrics supplement
channel metrics
segment managers control cross-
channel programs
Measurement
program metrics (volumes, costs,
responses)
customer metrics (conversions,
lifetime value)
segment and cohort metrics
compare performance vs plan and
past, inc. exception reports
results by segment and cohort
testing and analytics to estimate
promotion impact (ROI)

41. Business Case Worksheet
Business Case Worksheet
Financial
Current
1st Year
Change
3 Year
Change
Non-Financial
System Costs Customer
expectationsSoftware
Implementation Competitive
pressureOperations
Internal (Staff, Training, etc.) Pain/crisis
responsetotal
Compliance
System Impact
New Customer Margin Control
New Customer Cost
Existing Customer Margin Case studies
Existing Customer Cost
Retained Customer Margin
Retained Customer Cost
Indirect Value (Referrals, etc.)
total
Net Financial Impact

42. Deployment Sequence
Sample Deployment Sequence
Capabilities Added
Components required:
Variable
Messages
Customer
Segmentation
Business
Prospecting
Sales
Automation
Data
area demographics new X
consumer data new X
business billing data new X X
customer data new
call center data new
business lists new X
Systems
new database new X X X
scoring new X X
campaign management new X X
sales automation new X
People/Skills marketing marketing sales sales
Processes profiling segmentation ideal customer account mgt
Value $100,000 $100,000 $50,000 $100,000
Cost $150,000 $50,000 $20,000 $50,000
Risk low low mid mid
x indicates component is required for that stage shaded boxes are components added for that stage

43. Project Planning
Sample Deployment Plan
Resources
Client Resources Vendor Resources
executive sponsor: executive contact:
project lead: account manager:
data analyst: project manager:
marketing analyst: solution architect:
IT analyst: database engineer:
solution engineer:
Timeline
Task Activities Deliverables Start Date End Date
Kickoff • client meetings to define goals and
requirements, build project plan, assign team
• project goals
• detailed project plan
Business Discovery • client interviews to document current
processes and systems, define detailed
requirements
• process documentation
• functional specifications
• user acceptance plan
• revised project plan as needed
Data Discovery • connect to existing systems
• assess existing data
• define data feed requirements
• data source list
• database design
• data update plan (sources, elements,
frequency, collection methods, etc.)
Database Build • provision hardware/ software
• set up data structures
• set up data transfer processes
• set up load/update processes inc. identity mgt
• initial data load and audit
• create automated load/update processes
• parallel tests as needed
• final user acceptance
• database design
• process flow design
• audit reports
• deployed database
• functioning load/update process
• documentation and training
• user acceptance
Data Access • create automated extract processes
• document and deploy API/query connections
• design and deploy access tables
• functioning extract processes
• credentials and documentation for access
• functioning access tables
Application
Deployment
• provision and configure applications (analytics
& customer engagement)
• user acceptance tests
• functioning applications
• documentation and training
• user acceptance

44. Final Discussion
• What did you learn?
• What didn’t you learn?
• What do you do next?

45. Thank You!
www.cdpinstitute.org
draab@cdpinstitute.org