As the strategic importance of data has increased, new approaches to customer analytics have emerged as well. As customer interactions with companies grow and diversify, the need to integrate data faster and deliver real-time insights is critical. This presentation explores the underlying trends driving companies to become more data-driven and invest in customer analytics. And, it outlines three types of approaches to capturing, managing, analyzing, and activating customer knowledge and insights.

1. Customer 360
Understanding Your Customers In The Digital Era

2. 2
Contents
• Background
• Why Customer Data Matters
• Data Warehousing and Big Data
• Data Blending Solutions
• Data Management Platforms
• The Road Ahead

3. 3
Background
Moving from “Mad Men” to “Math Men”
‣ When most people think of
marketing and advertising,
they think of the archetype
of the Mad Men era ad
agency
‣ But with surprising speed,
the rise of digital media (and
the accompanying explosion
of customer data) has
revolutionized marketing

4. Why Customer Data Matters
Power is in the hands of the customer
• We are in the Age of the Customer
• Customers have more power, choice
and influence than ever before
• What we think and feel about our
interaction with an organization’s
products and services is increasingly
important due to the rise of social
media
• Consumer behavior has shifted
dramatically in recent years: how we
research, evaluate, purchase, and
engage with brands has changed
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5. 5
Rapid Shifts In Customer Behavior
In recent years, most people have changed…
How they
watch TV
How they
research
What they
expect
How they
communicate
How they
shop
on-demand via
NetFlix, Amazon,
HBO GO, etc
anywhere and anytime
using smartphones and
tablets
based on experiences
with Apple, Amazon,
Trader Joes, etc
using social media;
Facebook; Pinterest
“Showrooming” and
buying it cheaper
online

6. THIS IS A DEAL BIG
6

7. 7
An explosion of new devices

8. An explosion of touchpoints
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9. An explosion of content
9

10. Quantity of global digital data, exabytes
130
1,227
2005
2010
2,720
2012
7,910
2015
Source: EMC/IDC Digital Universe Study, 2011
An explosion of data
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11. Farewell Funnel
During the Mad Men era, the purchase journey
was more predictable and linear
Leads
Prospects
Customers
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12. Hello Decision Journey
Today, the consumer decision journey is non-linear, multichannel, and
consumer-driven
Leads Online
Prospects
Purchase
Review
Online
Chat
Ask FB
Friends
Online
Search
Banner
Ad
Store
Visit
View
Video
Purchase
12

13. 56% of customer interactions
happen during a multi-channel,
multi-event
journey
Source: McKinsey & Co. 13

14. Business Outcomes
Understanding customers and customers decision journeys helps
companies drive significant business outcomes
Marketing &
Advertising
Customer
Service
Retention
& Loyalty
Customer
Experience
CSAT Scores
ROMI
Waste
Call Reduction
Cross Sell
Churn
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Customer Data
Companies have access to lots of data that can help them understand
their customers and customer decision journeys
Online
Review
Online
Chat
Ask FB
Friends
Online
Search
Banner
Ad
Store
Visit
View
Video
Purchase
Social
Media
Mobile
Retail
Survey
Call
Center
Purchase
Chat
Web Branch

16. 16
The Challenge
More often than not, customer data is fragmented and locked away in
physical and organizational silos
Social
Media
Retail
Mobile
Purchase
Call
Center
Survey
Chat
Web
Branch
MARKETING CUSTOMER SERVICE
SALES

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Customer Analytics
New approaches have emerged to help companies unlock and analyze
their customer data
Data Warehousing
Solutions
Data Blending
Solutions
Data Management
Platforms (DMP)
traditional, batch-oriented
ETL data
integration for reporting
and analysis
real-time blending or
mashing of data from
different sources for
analysis
platform to collect,
organize and activate
audience data from any
source; integrated with
execution systems

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Data Warehouse
Traditional approach to integrating data for consistency and quality
• For many years, traditional business intelligence and data warehousing technologies
and approaches have been used to capture and analyze customer data.
• Beginning in the 1990s, companies pulled data from their transactional systems into
separate, centralized data warehouses to support reporting and analysis.
• The typical extract-transform-load (ETL)-based approach to data warehousing
captures data housed in disparate source data systems, transforms the data, and
then moves it into the data warehouse, where the data is arranged in a way to help
facilitate access.
• By centralizing data in the warehouse, companies could create a "single version of the
truth" and avoid the errors and discrepancies that often plagued them when reports
were created from various transactional and source data systems.

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Data Warehouse
Traditional approach to integrating data for consistency and quality
Data Sources Data Cleansing Data Warehouse Example Use Cases
ETL
CRM
ERP
Operational
System
Reporting
Analytics
Flat File Data Mining

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Data Warehouse Challenges
The explosion of data has strained the traditional approach & technologies
• Explosion of data, particularly unstructured data,
generated in recent years has strained the traditional
data warehousing approach and underlying
technologies
• The foundational infrastructure of data warehousing has
been the relational database, which stores data into
tables (or "relations") of rows and columns and is used
for processing structured data.
• As the volume (amount of data), velocity (speed of data
in and out), and variety (range of data types and
sources) of data has increased, relational databases
often aren't able to provide the performance and latency
needed.

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Evolved Data Warehousing
Next generation approaches and technologies for big data analytics
Cloud Computing Big Data
Technologies
Data
Visualization
Cloud computing decreases
cost of computing resources
and creates agility. Resources
spun up and shut down quickly
and easily.
Big data technologies support
greater variety, volume, and
velocity of data. They also
speed the time it takes to mash
up different data sets.
Data visualization provides user-friendly
visual analysis and
helps decision makers move
from insight to action.

22. Data Blending
Approach to blending data from different sources for analysis
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• Historically, analysts used tools like Microsoft Excel or Access in situations where they
needed to analyze data not available in the data warehouse.
• But, in recent years a new type of solution, data blending (also sometimes referred to
as data discovery), has emerged.
• Using data blending tools, analysts themselves can access, cleanse, and blend data
from multiple sources without having to write a line of code.
• These tools allow customer data to be blended together from multiple internal sources
as well as external sources immediately to support a more agile approach to
customer analytics.
• This is increasingly important because if companies know what their customers are
doing better than their competitors, or can get to those insights faster, then they have
a very distinct advantage.

23. Data Blending
Approach to blending data from different sources for analysis
Internal Data Sources
CRM
ERP
Operational
System
Flat File
External Data Sources Data Blending
Market & Customer Data
Example Use Cases
Reporting
Analytics
Data Mining
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Data Management Platforms
Approach to collecting, organizing and activating customer data
• A DMP allows companies to centralize data, both their own online and offline data as
well as third party data, and use it to create target audiences and optimize their online
advertising.
• Using a DMP, companies can measure how campaigns perform for different customer
segments and optimize their media buys and creative elements over time to improve
effectiveness.
• DMPs differ from data warehouses since they more provide more rapid data
integration and are tied to execution systems, such as digital ad execution, content
management and marketing automation systems.
• DMPs are optimized to allow marketers to define target audiences and then activate
campaigns to reach those prospects and customers.

25. 25
Data Management Platforms
Approach to collecting, organizing and activating customer data
Internal Data Sources
Display
(Ad Server)
Web
Analytics
CRM
External Data Sources Data Management Platform
Market & Customer Data
Example Use Cases
Targeted Display
Advertising
Email/ Inbound
Campaigns
Email
Database
Ad
Execution
Mktg
Automation
Advanced
Customer Analytics

26. 26
DMPs Support Demand-Side Platforms
DMPs support programmatic approaches to targeting specific audiences
Marketers
Demand-Side Platforms &
Data Management Platforms
Exchanges
(Supply-Side Platforms)
Publishers
(Websites)
Audiences
DSP/
DMP
DSP/
DMP
DSP /
DMP
DSP/
DMP
DSP/
DMP
DSP/
DMP
DSP/
DMP

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How Do They Compare?
Each approach has benefits and limitations
Data
Warehousing
Data Blending
Data
Management
Platforms
Benefits Limitations
• Integrated data to provide a “single version
of the truth” for reporting and analytics
• Minimizes any performance impact to
operational systems
• Long cycle times to integrate new data
sources
• Business user-driven approach
• Speeds time to integrate and analyze new
data sources
• Increases risk of data quality issues due
to user errors
• May impact performance of operational
systems
• Enables real-time activation through
integration with execution systems
• Speeds time to integrate and analyze new
data sources
• Bringing offline data online results in data
loss

28. The Road Ahead
Your 90 Day Plan: Recommendations to Consider
• Customer analytics is not about the data or technology, but about the business decisions
that the insights enable.
• Customer insights have maximum value when the focus is on real-time insights connected
with front-line execution.
• Many customer insights can be found by mashing up different data pools. But, it is
important to begin with whatever data is available today.
• The best approach is business question or hypothesis-driven. Often the biggest challenge is
to follow the 80-20 rule and identify the 20% of the data that provides the right insights.
• Where possible, begin with simple and then evolve to more sophisticated approaches. For
example, is it possible to approach early attempts at multi-channel, multi-touch marketing
attribution with heuristic approaches? Can you begin predictive modeling using simple,
linear regression models that are easy to understand and implement?
• Keep people, your prospects and customers, constantly in mind in terms of improving their
experience and meeting their needs and expectations.
• Don't just focus on customer acquisition and retention data. There is additional value in
insights derived from the full life cycle of prospect and customer touchpoints.
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29. The Road Ahead (cont.)
Your 90 Day Plan: Recommendations to Consider
• Gain an outside perspective. Consultancies can help provide an assessment of where you
are today and recommend roadmaps and best practices based on their experience with
other clients.
• Rather than approach customer analytics in terms of a single business use case, consider a
full range of uses when determining appropriate levels of investment and communicating the
full strategic value.
• Make learning and talent development a key part of the agenda.
• Take an agile, iterative approach to managing, analyzing and activating data.
• Approach customer analytics as a journey rather than a one-time project. Most companies
require cultural, organizational and process change to become more data-driven--not just a
new data store or technology--and this evolution takes time.
• Success with transforming to data-driven marketing also requires executive support and
involvement. Persuade senior executives to champion and support these efforts.Let me know
what’s working in your workplace
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Contact Me
!
@DaveBirckhead
www.davebirckhead.com