Designing the data driven_Patrick Tripp

Designing the Blueprint for Data-Driven
Customer Experiences
Patrick Tripp
VP, Product Strategy
RedPoint Global

©RedPoint Global Inc. 2018 | Confidential 2
Traditional Customer Journeys are Dead (fix- get Tibet Road)
Source: http://www.complexmania.com

of buyers will pay more
for a better customer
experience, but only
Customer Experience Index.
86%
Customer Expectations Have Risen
of customers feel that
brands consistently
meet their expectations
1%

The Biggest Gaps Between Strategy and Execution
Source: The State of Engagement: Bridging the Customer Journey Across Every Last Mile, RedPoint Global and CMO Council, May 2018.
42%Systems that leverage real-time data
0 10% 20% 30% 40%
37%Organization-wide single view of the customer
36
%
Measurable results that identify the
business impact
30%Budget to execute
27%Cultural shift to prioritize customer’s context
22%Executive-level support for CX priorities
21%Time, talent and technology to
execute consistently
20%Real-time intelligence and analytics

The Impact of Personalization for Retailers
85%of retailers are
focused on leveraging a single,
unified platform for all commerce,
inventory, customer and transaction
data to eliminate individual channel
silos and offer a seamless experience
Boston Retail Partners
40%
of consumers purchase
more from retailers that
provide a more personalized
shopping experience across
all channels
E-tailing Group
89%
of digital businesses are
investing in personalization
to align with customer
expectations
Forrester Research
55%
of respondents are
using two or more
communications channels
to contact a company or brand
Northridge Group
68%
of retailers are focused
on improving the customer
experience by identifying
customers, utilizing customer
facing technology and
empowering associates with
information in real-time
Boston Retail Partners
6-10%
revenue increase by
brands that create
personalized experiences
integrating advanced digital
technologies and proprietary
data their for customers
Boston Consulting Group

2. Recognize Customers from Anonymous
to Known
 Identity Resolution
 Deterministic and Probabilistic
 Respect Privacy
1. Create a Single Customer View
 Customer Data Platform (CDP)
 All Data Sources
 Cadence and Variety
3. Deliver Real-Time Customer Experiences
 Speed and Agility
 Data-Driven Personalization
 Omnichannel Orchestration
Blueprint for Data-Driven Customer Experiences

E-Commerce
Device
Social
Transactional
Physical
● Mailing address
● Physical address
● Name
Contact Graph
● Credit Card #
● Customer ID
● Address
● Device ID
● Mobile Number
● Web Session ID
● DMP ID
● Email Address
● Name
● Social Handle
● Persona Name
● Email Address
● Customer ID
● Loyalty Program ID
Touchpoints
Web/Browser
Email
The contact graph combines fragmented data across many touchpoints to create the unique individual
Potential Identifiers
CDP: Creating the Golden Record

Machine Learning
requires accurate, robust
data management
Dirty data: Missing values, poor
categories, inconsistent
formatting, erroneous values
Noisy data: Conflicting data,
misleading information, too much
detail, poor collection practices
Sparse data: Very few actual
values, too many fields/variables
Inadequate data: Incomplete
collection, biased sampling,
over-correlation
So if you don’t know the
customer’s birth date,
just fill in anything – it’s a
mandatory field. And we
put international phone
numbers in the shoe size
field because that is the
only place it lets us.
Doesn’t that cause
problems later?
Yes.
Any other questions?
The Data Problem: Garbage In Garbage Out

Product
Registration
POSMobileE-com
Name &
Device ID
WebWeb
Product
Details
DMP
Cookies
& Tags
Customer
Profiles
Identity Resolution – Anonymous to Known

©RedPoint Global Inc. 2018 | Confidential 10©RedPoint Global Inc. 2018 | Confidential 10
AI and Machine Learning in Marketing
Business
Rules
Machine
Learning

CRITICAL CAPABILITIES FOR REAL-TIME ENGAGEMENT
Data-Driven Personalization
Leverage online/offline data
and analytics to
automate segmentation
Strike a balance between
timing & context for
value-added engagement
Speed and Agility
MANUAL EFFORT AUTOMATED EFFORT
Deploy machine learning models
and A/B/n to optimize
engagement
Omnichannel Optimization
Critical Capabilities for Real-Time Engagement

From Segmentation…

…to Zero Segment Marketing

85%
Propensity
Score:
77%
Propensity
Score:
62%
Propensity
Score:
EMAIL
GIFT
INTERNET
SALE
TEXT
CODE
PURCHASE
LOYALTY
OFFER
HALF
OFF
NEXT BUY
FREE
PURCHASE
20% OFF
PURCHASE
OFFER
APP FLASH
SALE
PURCHASE
g
Mary Smith
m.smith@yahoo.com
Table
2/11/2016
$879
INACTIVE
Joe Johnson
j.johnson@gmail.com
Jacket
04/21/2017
$49
ACTIVE
Susan Day
susan.day@xyz.com
Shoes
6/19/2017
$299
ACTIVE
Name:
Email:
Purchase:
Last Purchase Date :
CLV:
Active / Inactive:
Name:
Email:
Purchase:
CLV:
Active / Inactive:
Name:
Email:
Purchase:
CLV:
Active / Inactive:
Path to Purchase Optimization

85%
Propensity
Score:
77%
Propensity
Score:
62%
Propensity
Score:
82%
Propensity
Score:
68%
Propensity
Score:
89%
Propensity
Score:
Joe Johnson
j.johnson@gmail.com
Jacket
04/21/2017
$49
ACTIVE
Susan Day
susan.day@xyz.com
Shoes
6/19/2017
$299
ACTIVE
PURCHASE
EMAIL
GIFT
INTERNET
SALE
TEXT
CODE
PURCHASE
LOYALTY
OFFER
HALF
OFF
NEXT BUY
FREE
PURCHASE
20% OFF
PURCHASE
OFFER
APP FLASH
SALE
PURCHASE
WEB SALE HALF OFF
LOYALTY
OFFER
PURCHASE
TEXT CODE INTERNET
SALE
LOYALTY
OFFER
50% OFF PURCHASE
OFFER
PURCHASE
PURCHASE
EMAIL
GIFT
EMAIL
GIFT
Name:
Email:
Purchase:
CLV:
Active / Inactive:
Mary Smith
m.smith@yahoo.com
Table
2/11/2016
$879
INACTIVE
Name:
Email:
Purchase:
CLV:
Active / Inactive:
Name:
Email:
Purchase:
CLV:
Active / Inactive:
Name:
Email:
Purchase:
CLV:
Active / Inactive:
Mary Smith
m.smith@yahoo.com
Table, Chairs
2/11/2016
$999
ACTIVE
Name:
Email:
Purchase:
CLV:
Active / Inactive:
Joe Johnson
j.johnson@gmail.com
Jacket, Belt
04/21/2017
$99
ACTIVE
Name:
Email:
Purchase:
CLV:
Active / Inactive:
Susan Day
susan.day@xyz.com
Hand bag, Shoes
6/19/2017
$463
ACTIVE
Path to Purchase Optimization

Keurig Dr Pepper
• Leveraged RedPoint
on Microsoft Azure to
generate sophisticated
customer flavor profiles
• Product recommendation
engine makes
personalized offers
• Stood up six ongoing
cross-channel campaigns
in a matter of weeks for
the Holidays
• Lift on conversions
based on A/B/n carousel
• Holiday campaigns took
only 15 weeks from design
to live
144% revenue increase
from prior solution

How to Get Started?
Source: http://thecampuscareercoach.com

The Keys to Successful Customer Engagement
Single Customer View
Create a golden record and persist over time
Next Best Actions
Recommend decisions across the customer lifecycle
Personalized Experiences
Perfectly aligned relevance

The power of an
open garden

Steps to Delivering Actionable Analytics and Insights
STRETCH
Gather, merge and clean data
WALK
Deliver triggered marketing tactics
JOG
Coordinate the customer journey
RUN
Optimize using advanced analytics

Exercise 1
Draw in all the sources of customer data in your organization.
CDP

Exercise 2-3
2. For each source, list the identifying data. 3. Then connect the common data types across silos.

2. Recognize Customers from Anonymous
to Known
 Identity Resolution
 Deterministic and Probabilistic
 Respect Privacy
1. Create a Single Customer View
 Customer Data Platform (CDP)
 All Data Sources
 Cadence and Variety
3. Deliver Real-Time Customer Experiences
 Speed and Agility
 Data-Driven Personalization
 Omnichannel Orchestration
©RedPoint Global Inc. 2018 | Confidential 23©RedPoint Global Inc. 2018 | Confidential 23
Blueprint for Data-Driven Customer Experiences

patrick.tripp@redpointglobal.com
aaron.brennan@redpointglobal.com
Follow us on Twitter
@ptripp | @RedPointGlobal
contact.us@redpointglobal.com

Designing the data driven_Patrick Tripp

Designing the data driven_Patrick Tripp

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Designing the data driven_Patrick Tripp

Similar to Designing the data driven_Patrick Tripp (20)

More from National Retail Federation

More from National Retail Federation (20)

Recently uploaded

Recently uploaded (8)

Designing the data driven_Patrick Tripp