1) The document discusses key trends in customer data and analytics including increasing data volume, velocity and variety; consumers demanding personalization; the impact of mobile and IoT; and data science and machine learning going mainstream. 2) It highlights challenges like data silos and the need for integrated customer profiles to power personalized experiences across channels. 3) The use of predictive analytics at a fashion brand called Mavi is discussed, showing how predictive marketing helped reactivate 20% of lapsed customers and increase revenues by 7%.

1. The State of Big [Customer] Data
How To Enable The Customer-Centric Enterprise

2. Introduction
Today’s companies are competing for
mindshare, share of wallet and customer
loyalty. Those who are succeeding are doing
so because they’ve found a way to harness
the power of data. To understand business
dynamics, to forecast and predict business
needs, to make the right strategic decisions,
and most importantly, to better service the
customer.
We believe that there are four key trends
impacting the state of customer data:
Did you know?
5 exabytes
of data are
created each
day.
Source: Gartner. Gartner Digital Marketing Conference 2015.
Key Trends
Data volume, velocity and variety are increasing.
Consumers demand personalization.
Mobile and the Internet of Things are changing the way consumers
experience and interact with brands.
Adoption of data science and machine learning is increasing beyond BI and
Customer Intelligence teams.

3. Digital, social and mobile have created an explosion
of data. According to Gartner, 5 exabytes of data
is created each day which is 2 times more data
created per second than the entire Internet in 2000.
Previously owned by the office of the CIO, data is
integral to all aspects of a business. But the new
imperative is figuring out how to access, analyze
and act on customer data faster, more efficiently
and at scale. According to a survey of more than
600 companies worldwide by Econsultancy, 62% of
respondents indicated they often feel overwhelmed
by the volume of incoming data, and a staggering
85% are unable to extract the full value from the
data sources to which they have access.
w The Impact:
Data silos are growing, increasing the need for
integrated data
Fiveyearsago,sheerdatagrowthmayhaveposed
challenges, but the declining cost of storage,
and the rise of cost-effective cloud-based
alternatives, have made it easier for enterprises
to scale. However, new channels and more types
of interactions typically mean more point-based
solutions for managing these interactions. Until
channels mature and capabilities are integrated
or absorbed into larger applications, the data sits
in yet another silo, increasing the demand for
integrated data.
TREND #1: Volume, velocity & variety of data are increasing

4. Intuitively we all know that receiving the right
message, at the right time, on the right channel,
improves the customer experience. But today’s
consumer is bombarded with choices and has
growing expectations that retailers are relevant in
their communication. What once was a nice-to-
have is now table-stakes for customer retention.
According to RightNow Customer Impact Report,
78% of consumers are more likely to be a repeat
customer when they receive personalized
experiences. And 9 out of 10 of those consumers
are even willing to pay up to 25% more. Without
it, 50% of consumers would consider abandoning
their loyalties to retailers who don’t deliver relevant
offers.
Where are retailers focused? The top two areas for
growth over the next three years include:
1. Personalizing the shopping experience by
understanding the customer’s online browsing
history is expected to grow by 1060%
2. Offering associates the ability for suggestive
selling based on the customer’s closet is
expected to grow by 510%
Source: “2015 POS/Customer Engagement Survey,” Boston Retail
Partners.
TREND #2: Consumers demand personalization
Gartner predicts
by 2018,
organizations
that have fully
invested in
all types of
personalization
will outsell
companies that
have not by 20%

5. In this example, Jennifer engages with a brand on multiple channels, sometimes using a slightly different
name, email or address. Without a single customer profile, Jennifer is being treated like four very different
consumers. But when combining her activity, we now have a very different perspective on who Jennifer is, how
she’s engaging and what is relevant to her.
w The Impact:
A single customer profile is the path to multi-channel personalization
While data warehouses are a viable solution, data access and actionability can be limited. Delivering
relevant and personalized experiences rely on achieving a single customer profile. Econsultancy
found that 51% of marketers strongly agree that a single customer view is critical to long-term
success with 70% of companies indicating they are just starting the process of data integration to
get to a Single Customer Profile (SCP).
TREND #2: Consumers demand personalization

6. What is a single customer profile?
A single customer profile combines customer data from multiple data sources into a single view and
generally contains 3 types of data:
• Profile data (stated/
explicit data): This
is a combination of
demographic data such as
gender, age and location
and other collected data
such as stated interests you
might capture in a
registration form. In its
simplest form, profile data
should tell you more about
who the customer is and
their relevant contact
information. For lifestyle
segments, predictive
clusters and persona based
information, see predictive
data below.
•
•
•
•
Behavioral data (implicit
data): Behavioral data
is a combination of
transactional data and
interaction data. This
includes things the
customer does when
making purchases and
other types of interactions such as browsing your website, opening your emails, writing a review or
calling customer service.
Predictive & cluster data (derived data): The distinction with predictive data is that they are derived
data using algorithms or statistical models based on a series of inputs. Lifecycle cluster, for
example, is derived based on first order date and days since last purchase, whereas product cluster
uses a complex algorithm to identify which products you (and people like you) are likely to buy.

7. eMarketer projects 69% of the population worldwide will have a mobile phone - half of them smart phones
- by the end of 2017. And Gartner estimates there will be 26 billion Internet of Things by 2020 - a 30-fold
increase compared to 1 billion just five years ago. This constant connectivity in our everyday lives will require
that publishers, brands and retailers understand the role that mobile and other devices play at various touch-
points throughout the consumer journey. The ultimate goal is to determine how best to leverage these
devices to reduce friction, engage in real-time and create experiences that seamlessly bridge physical and
digital interactions.
Real-time, on the other hand, introduces a new dimension that requires the ability to capture, analyze and
deliver multiple data points (sometimes hundreds or thousands) in a matter of milliseconds. According to a
survey conducted by Boston Retail Partners of over 500 retailers, 44% of retailers indicated real-time as a top
priority in 2015.
TREND #3: Mobile and the Internet of Things (IoT) are changing
the way consumers experience and interact with brands

8. w The Impact:
Channels are blurring and brands need to be able to connect online and offline interactions, in near-
time or real-time
While developing a single customer profile is one path to personalization, tracking and engaging
consumers across channels with relevant experiences requires more than just integrated data and a
single profile. As consumers navigate from their home computer to their mobile device, from email to
social, and from eCommerce to store, tracking and attributing their digital and physical
interactions to a single customer record requires a sophisticated approach to customer data
management.
Until recently, it could take months or years to accomplish the data integration needed to build a single
customer profile and to link and deduplicate all customer data. Recently, more automated solutions have
emerged that make data integration and data cleansing much easier. These solutions often use standard
data models that make it easier and faster to standardize customer data across channels, and typically
include:
• Data validation and standardization such as postal and email address to increase the accuracy for
deduping algorithms and ensure email and direct mail are getting the highest delivery rates.
• Data cleansing components such as correction of names to increase the accuracy for deduping
algorithms and to enable basic personalization.
• Identity resolution and deduping engine which applies algorithms that link customer records even if
there is no exact name, email or address match.
• Data enrichment which includes genderization and deduplicated records at the contact and
household or account level.
• Ongoing cleansing at regular intervals – ideally daily - ensures data remains accurate and up-to-date.
As the old saying goes, “Garbage in. garbage out.” The always-on, always-connected consumer is
amassing multiple data points with every interaction. If you don’t have a process for keeping
those interactions clean and up-to-date, your data won’t just be incomplete, it will be inaccurate -
and in many cases - misleading.
TREND #3: Mobile and the Internet of Things (IoT) are changing
the way consumers experience and interact with brands
10-20%average number of duplicate
customer records
1 out of 10of your best customers are
misclassified without data cleansing
25%average % LTV is
underestimated without data quality

9. Once reserved for the Fortune 1000 and the most
sophisticated of companies, the democratization
of data science and machine learning is underway.
A recent survey of 132 marketing executives by
AgilOne found that 76% of marketers used some
form of predictive analytics in their marketing in
2015, which is up from 69% in 2014*.
The acceleration is fueled by three factors:
1. Customers are demanding more meaningful
relationships
2. Early adopters show that predictive marketing
delivers enormous value
3. New technologies are available to make
predictive marketing easy
How your peers are using predictive
According to recent research from Gartner:
• 48% of marketers analyze historical data to
understand patterns and performance and
make inferences about the future
• 18% use statistical models to simulate
scenarios and make predictions about the
future
• 21% use predictive analytics to allow real-time
calculations to trigger events
Sources:
2nd Annual Predictive Marketing Survey, AgilOne, January 2015.
How Leaders Use Data in Marketing, Gartner, Digital Marketing
Conference 2015
TREND #4: Data science and machine learning go mainstream
with predictive marketing
Three Most Commonly Used
Predictive Analytics:
1. Unsupervised learning (such as clustering
models): Unsupervised learning tries to find
hidden patterns in data, without explicitly
trying to estimate or predict an outcome.
And is often used to identify specific clusters
based on things like products, brands or
purchase behavior. If segmentation is the
process of putting customers into groups
based on similarities, then clustering is the
process of finding similarities in customers
so that they can be grouped.
2. Supervised learning (such as propensity
models or predictions): Supervised learning
estimates an output given an input, after it
is trained with sample inputs and a target.
Supervised learning algorithms try to
predict the most probable outcome based
on variables like a customer’s last purchase
date, web engagement, demographics and
source of acquisition.
3. Collaborative filtering (more commonly
used for recommendations): Allows us to
understand hidden patterns and similarities
in the data to accurately predict the best next
steps, outcomes, products, or content for the
user or a given event.

10. TREND #4: Data science and machine learning go mainstream
with predictive marketing
w The Impact:
Predictive marketing delivers unprecedented insights into customer behavior that can be
leveraged across the organization to create a customer-centric enterprise
Predictive analytics is an umbrella term to cover a variety of mathematical and statistical techniques
to automatically find patterns in data and make predictions about the future. When applied to
marketing, predictive analytics can predict future customer behavior. More important than the
technical definition, predictive marketing is a new way of thinking about customer relationships,
powered by new technologies in big data and machine learning.
With predictive marketing, organizations can:
1. Deliver more targeted and effective promotions and campaigns, increasing usage of value-added
services.
2. Gain better insight into both large and small communities of interest.
3. Utilize precise, accurate, and fast polling of big data to identify customer-related issues, including
propensity to purchase, likelihood to churn, and prospective credit risks.
4. Identify market gaps and turn them into revenue.
5. Match customer eligibility, inventory availability, and profitability to prioritize offer presentations
and deploy a next-best-activity solution.
6. Optimize campaigns to maximize retention, cross-sell, and up-sell across marketing channels,
increasing revenue.
7. Proactively identify and target customers at risk of churning well before the loss.
8. Create forecasts for assortment planning, shelf replenishment, pricing and promotion analysis,
store clustering, store location selection, and sales and purchasing planning.
9. Achieve better accuracy in sales forecasts.
Predictive marketing is only as good as the data you put into it. If your predictive
marketing cloud doesn’t have strong data management capabilities – your models and
derived insights will be useless.

11. High-fashion clothing brand reactivates 20% of lapsed customers
A good example of a mid-market company that has achieved significant success with predictive
marketing is Mavi, a high fashion clothing manufacturer and retailer based in Istanbul, Turkey. Mavi
is known for its organic denim favored by celebrities and supermodels. Mavi operates about 350
multinational stores and sales channels in the U.S., Canada, Australia, Turkey and 10 European
countries. Mavi started with a single predictive marketing campaign about five years ago. When Mavi
first got started, each department, including marketing and IT, used its own set of marketing reports
and customer data, including key performance indicators. This led to cumbersome cross-referencing
and impeded important decision making. Like many companies, the Mavi marketing team initially didn’t
have access to customer data without relying on IT resources. This was the first problem that the team
tackled. Mavi deployed a modern, cloud-based predictive marketing solution in 2009. This allowed the
company to consolidate, cleanse and de-dupe their customer data on a daily basis. Now they were ready
to start using data in hyper-personalized campaigns.
One of the first predictive marketing programs that Mavi tested was a program around specific buying
personas. Mavi used predictive analytics to find groups of people with distinct product preferences. In
predictive lingo these are called product-based clusters. Mavi found at least three very different groups
of shoppers: customers who favored mostly woven shirts, others who favored beachwear, whereas
a third persona mostly shopped for new season high fashion and accessories. Mavi started to use
these personas to implement more targeted marketing campaigns via email and SMS. Specifically, it
implemented a re-engagement campaign for lapsed customers that featured the right types of products
and creative with the right customers. Using these clusters, Mavi was able to reactivate 20% of lapsed
customers. This was a big breakthrough since every customer saved or reactivated reduces Mavi’s need
to acquire new customers.
Mavi today is running over 80 different predictive marketing programs in a year. Collectively, these
campaigns helped to increase Mavi revenues by 7% in the first few years - which is a huge sum on a
dollars and cents basis. Mavi is still finding new ways to increase customer lifetime value and with every
campaign launched this number is pushed up higher.
Elif Oner, Mavi’s head of customer relationship management,
recommends all marketers to get started with predictive marketing.
She says: “Start small and pick just one program and build on
that success.”

12. Visit www.agilone.com/academy for these helpful resources:
Data-driven marketing can increase a company’s rev-
enue and improve the customer experience. However,
there are a few common mistakes that are made when
implementing this type of marketing strategy. Learn
what they are in this comprehensive eBook.
Introductory Guide to
Clustering
The Definitive Guide to
Predictive Analytics
The Definitive Guide to
Customer Lifetime Value
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