IBM - Using Predictive Analytics to Segment, Target and Optimize Marketing

Divide & Conquer
Using Predictive Analytics to Segment, Target and Optimize Marketing

February 2012
Trip Kucera, David White

~ Underwritten, in Part, by ~

February 2012
Divide & Conquer: Using Predictive Analytics
to Segment, Target & Optimize Marketing Analyst Insight
Data collected by the Aberdeen group in August 2011 (Predictive Analytics for Aberdeen’s Insights provide the
Sales and Marketing: Seeing Around Corners) found that companies using analyst's perspective on the
predictive analytics enjoyed a 75% higher click through rate and a 73% research as drawn from an
higher sales lift than companies that did not use this technology. However, aggregated view of research
new research published here (based on data collected in December 2011) surveys, interviews, and
shows that even among users of predictive analytics there are significant data analysis
differences in the level of business performance achieved. These
performance differences accrue from subtly different applications of
technology, as well as different capabilities within the organization. This new
research reveals the best practices sales and marketing organizations can
Defining Leaders & Followers
adopt to maximize their return on investment (ROI) in predictive analytics.
Aberdeen measured the overall
performance and effectiveness of
Leaders Deliver Significantly Higher ROI
marketing organizations that
Leaders (the top-performing 35% of companies) outperform Followers (the participated in our survey, based
remaining 65% of companies) in several aspects of marketing performance. on their aggregate performance
Figure 1 illustrates three of these metrics. on 4 criteria. The top performing
35% of companies (Leaders) were
segmented from the remaining
Figure 1: Marketing Leaders Outperform Followers 65% (Followers) using the 4
criteria below. The relative
performance of Leaders and
13.0% Followers is also shown:
Y-Y change in lifetime
customer value 2.3% √ Average uplift from a
marketing campaign - Leaders
6.7%, Followers 3.3%

Average response 7.1% √ Year-year change in the
rate average value of a sales
4.8%
transaction - Leaders 13%
Leaders increase, Followers 2% decline
Followers
√ Year-year change in customer
2.6% retention rate - Leaders 4%
Average opt-out rate
3.9% increase, Followers 1%
increase

√ Year-year change in operating
0% 2% 4% 6% 8% 10% 12% 14%
profit margin - Leaders 3%
Percentage, n=112 increase, Followers 0% change
Source: Aberdeen Group, December 2011

Although both Leaders and Followers use predictive analytics, leaders use
this technology to drive much greater business impact on sales and
This document is the result of primary research performed by Aberdeen Group. Aberdeen Group's methodologies provide for objective fact-based research and
represent the best analysis available at the time of publication. Unless otherwise noted, the entire contents of this publication are copyrighted by Aberdeen Group, Inc.
and may not be reproduced, distributed, archived, or transmitted in any form or by any means without prior written consent by Aberdeen Group, Inc.

Divide & Conquer: Using Predictive Analytics to Segment, Target & Optimize
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marketing problems. In addition to the three performance criteria shown in
Figure 1, Leaders also outperformed followers in four other metrics that Predictive Analytics Definition
were used to define the two groups (see sidebar). Predictive analytics creates a
sophisticated mathematical
Considered together, these metrics show the chain of events leading from model to use as the basis of
excellent marketing performance to improved business results. For predictions. Feeding this model
example: are potentially hundreds or
thousands of pieces of data
• The lower opt-out rates Leaders experience during marketing collected on each individual.
campaigns contribute to the higher customer retention enjoyed by This model gives marketers
those organizations. insight into the characteristics
• In turn, higher customer retention rates and larger growth in the and behaviors of buyers. The
richness of the data and the
average value of a sales transaction contribute to greater increases
rigor of the mathematical
in lifetime customer value, and higher growth in operating profit modeling distinguish predictive
margins. analytics from business
intelligence.
Competition for Capital
The macroeconomic climate of the last few years has fueled increased
competition for capital. As a result, a tougher competitive environment and
the competitive pressure to differentiate are the business pressures most
cited by survey respondents (Figure 2). At the same time, channels like
mobile and social media create new opportunities to interact with buyers,
but also new pressures to incorporate these channels into existing
marketing processes.

Figure 2: Pressures Driven by Competition for Capital

Tougher competitive
41%
environment

Competitive pressure
36%
to differentiate

Proliferation of sales /
29%
marketing channels

Need to improve
29%
productivity

Increasing cost of
28%
customer acquisition

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Percentage of respondents, n=112


Organizations adopted several strategies to address these challenges:

© 2012 Aberdeen Group. Telephone: 617 854 5200
www.aberdeen.com Fax: 617 723 7897

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• Customer segmentation and offer targeting. The strategy
most cited by respondents is to improve the targeting of marketing
offers (55%). Of course, this requires understanding the offers'
targets. It comes as no surprise that 29% of respondents are
building unique customer profiles and buyer personas (i.e.
segmentation) to support targeting.
• Customer Experience. 46% of respondents are adopting a
strategy to improve customer experience through a 360 degree
view of the customer. Whether the goal of this approach is to
increase customer retention, cross-sell, and up-sell, or to identify
new customer acquisition opportunities, a 360 degree view of the
customer can help companies address the pressures cited above.

Figure 3: On Target - Improved Targeting of Marketing is a Top
Strategy

Improve targeting of
55%
marketing offers

Obtain a 360º view of
45%
the customer

Build unique customer
29%
profiles and personas

0% 10% 20% 30% 40% 50% 60%


Data collected as part of Aberdeen's research Analytics for the CMO: How
Best-in-Class Marketers Use Customer Insights to Drive More Revenue
(September 2011) reveals that 24% of companies have adopted predictive
analytics for marketing, and an additional 33% of respondents plan to
implement this approach within the next 12 months. While predictive
analytics may not be required to execute the strategies listed above, this
study considers the strategies and capabilities of all companies using
predictive analytics. What emerges is a picture of how companies can most
effectively apply predictive analytics to achieve superior marketing results.


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An Offer You Can't Refuse Fast Facts
Companies have adopted a number of offer-related capabilities to improve Ease-of-use of a chosen
the targeting of marketing offers. While most survey respondents (57%) - solution is a key contributor to
both Leaders and Followers - are able to customize offers for a specific the superior performance of
Leaders.
market segment, Leaders differentiate themselves in their superior ability to
customize marketing offers for individuals. As indicated in Figure 4, Leaders √ Ease of integration with
are 70% higher than Followers (56% of Leaders v. 33% Followers) to existing applications or
provide individualized marketing offers. Leaders are also more likely to apply business processes. The
behavioral scoring to customer data (49% of Leaders v. 38% of Followers). insights generated from a
These capabilities are important not only because they help target offers, predictive model are rarely
but they're also differentiating in their own right. In a competitive used in isolation.
Commonly, the findings are
environment, companies that personalize marketing can be more effective in
incorporated into an existing
creating a perception of product differentiation among buyers. Leaders have business process or
demonstrated their ability to do this. As Figure 1 shows, Leaders have much application. 46% of
higher response rates and lower opt-out rates than Followers. Leaders can perform this
integration easily, only 26%
Figure 4: Leading Offer Management Capabilities of Followers can.
√ Business users are able
to use predictive tools
Ability to
provide offers 56% directly without
customized to
statistical experts. By
33%
individuals Leaders placing tools directly in the
hands of business managers,
Followers
organizations are more likely
Ability to apply to be able to manipulate and
behavior 49%
update models in step with
scoring to 38% the business need and
customer data
interpret results more
rapidly. 49% of Leaders
0% 10% 20% 30% 40% 50% 60% have such tools, only 38% of
Percentage of respondents, n=112 Followers possess them.

√ Library of different
Source: Aberdeen Group, December 2011 modeling templates. By
providing templates and
Informing Conversations examples as part of the
solution, organizations are
The value of predictive analytics extends beyond outbound offer likely to be able to deliver
management to include support for real-time decisions and interactions. value faster whenever a new
Thirty-two percent (32%) of Leaders use a predictive model to support model is required. 42% of
decisions in real-time, compared with 18% of Followers (Figure 5). Leaders Leaders have such libraries,
are also able to make proactive inbound offers in similar proportion (29% of only 30% of Followers do.
Leaders compared with 20% of Followers). With these capabilities,
companies can optimize web content with "next best action / offer" based
on customers history and / or online behavior (for example).


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Figure 5: Predictive Analytics Inform Real-Time Decisions

Customer-facing 50%
skills understood
and grown / hired 37%

Predictive model
used to drive 32%
decisions in real- 18%
time Leaders
Followers
Ability to make 29%
proactive in-
bound offers 20%

0% 10% 20% 30% 40% 50% 60%


Such real-time capabilities can be used to route incoming calls in a call
center to ensure each call is handled by the appropriate staff member.
Leaders excel here in their ability to use insights gained from predictive
analytics to enhance customer experience. Fifty percent (50%) of Leaders
have identified the critical skills required by customer-facing employees, and
nurture or hire for them accordingly, while only 37% of Followers do the
same. By understanding and growing the skills required by different roles or
tiers within a contact center, Leaders can maximize their gains from using
predictive models to route inbound calls. Aberdeen's prior research in this
area from September 2010 (Predictive Analytics – Driving Sales with Customer
Insights) offers a detailed example of this approach.

Fueling the Database of Intentions
Since data is the fuel of analytics, one would expect data-oriented
capabilities to be widely adopted among users of predictive analytics, and
indeed they are. Aberdeen's last benchmark report on this topic, from
September 2010 (Predictive Analytics – Driving Sales with Customer Insights),
found that top-performing organizations (the Best-in-Class) were more
likely to have access to a wider range of data than their more poorly
performing peers. The same holds true in Aberdeen's most recent survey.
Leaders outpace Followers in several categories. Figure 6 below illustrates
that a larger percentage Leaders have access to both customer behavior
data and transactional data. Access to this data allows the marketing
organization to build richer predictive models. Behavioral data is also key to
supporting real-time proactive management of offers (see above). Access to
transactional data is also vital to modeling aspects like basket size and
developing next best action scenarios. These data sources provide a more


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granular, adaptive predictive model and are essential for improving
predictive models - and hence improving business performance.

Figure 6: Data: The Fuel for Your Predictive Model

Access to
81%
customer
63% “To a large extent, the quality
behavior data
of the model is only as good as
Access to all the quality of the data that you
customer
78% have.”
71%
transactional data ~ CRM Manager,
European Utility
Access to
49%
internal
33%
unstructured data Leaders
Followers
Access to social 41%
media data 24%

0% 20% 40% 60% 80% 100%


Leaders are also more likely to have access to social media data, as well as
access to internal unstructured data, such as call center transcripts, emails,
instant messaging records and wikis. While we're in the early days of
exploiting social media feeds to gain predictive insight, this data can provide
intriguing and actionable insight based on sentiment, volume, and content.
Aberdeen's survey indicates that 55% of Leaders are using social network
analysis tools, compared with 36% of Followers (see Enablers below). It's
said that Google search data can be used to make fairly accurate predictions
about the outbreak and spread of flu based on search topics and volume. In
a similar way, social network feeds provide a public database of intentions
and sentiment. Aberdeen's data shows that marketing departments are
paying closer attention to social data as a source of predictive insight.

Enabling Technologies
In the course of our research, Aberdeen found a number of applications and
technologies that support, extend, and enhance the power of predictive
analytics (Figure 7).


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Figure 7: Applications to Enhance Predictive Analytics

71%
Customer segmentation
Fast Facts
tool 59%
The most common obstacles to
the adoption of predictive
Campaign management 63% analytics:
software 51%
√ Lack of understanding of the
benefits - 57%
Social media monitoring 39%
and analysis tools
√ Lack of appropriate skills -
27%
Leaders 45%
Followers
Marketing automation with
√ Data that could be used with
34%
embedded predictive predictive analytics is not
analytics 20% clean - 39%

0% 10% 20% 30% 40% 50% 60% 70% 80%
Percentage of respondents, n=-112


Overall, almost 2/3 of marketing organizations that took part in our survey
(63%) were using predictive analytics to aid in customer segmentation.
Grouping customers and potential customers according to shared
characteristics is foundational to sales and marketing. Some organizations do
this more formally than others, and some segment to a much finer level of Use Cases for Predictive
detail. It is clear that organizations with the best marketing performance Analytics in Marketing
(Leaders) are 20% more likely than Followers to use predictive analytics to
assist with segmentation. By applying predictive technologies to the task, √ Segmentation
marketers can gain more telling insights into the products and services that √ Cross Sell / Up Sell
are likely to appeal to each market segment. They will also be able to
forecast future revenue expectations and use this information to improve √ Offer Management
the targeting of marketing offers. √ Campaign Management
Both campaign management software and marketing automation software √ Channel Management
help bring rigor and consistency to the planning, management, and execution
of marketing campaigns and tactics. The use of campaign management √ Online advertising yield
software can provide senior management with a comprehensive view of management
marketing activities. It can ensure marketing plans are developed and √ Pricing & Forecasting
executed consistently. Finally, this class of software can provide a central
repository to capture all campaign performance-related information. This √ Loyalty & Customer
centralization and standardization can help marketers evaluate the Experience Management
effectiveness of campaigns, and determine their return on marketing
investment (ROM I). As noted earlier, Leaders are more likely to be able to
measure the effectiveness of individual marketing campaigns than Followers.
They also have a greater propensity to feed campaign results back into
predictive models, refining their performance over the long-term.
Thirty-four percent (34%) of Leaders also ensure predictive analytics
capabilities are embedded within their marketing automation solution. Only

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20% of Followers do the same. One perennial problem that dogs any
analytics project is "lift-and-shift"—the need to lift data from one
computerized system, and shift it to another before it can be analyzed. Such
processes are costly, time-consuming, and error-prone. Embracing a
marketing automation platform that allows analytics to be performed in situ
eliminates this barrier. As a result, there is a higher probability that analysis
can be performed in a timely fashion, more frequently, and with fresher
data. All these factors strengthen the impact of predictive analytics on
marketing performance.
Finally, Leaders are more likely than Followers to be tapping into the wealth
of social media data to direct their marketing campaigns, and are selecting
appropriate tools to do so.

Organizational Alignment is Imperative
The fastest, most accurate analytical engine on the planet is worthless unless
the insights it produces can be acted on in a timely way. For this to happen,
roles and responsibilities within the organization must be clear. Often,
organizational changes are needed to get the most from analytics. As Figure
8 shows, Leaders are more likely to make these changes than Followers.

Figure 8: Leaders More Likely to Have a Mandate to Act

Business
managers can 45% “Analysis was just the
take action on beginning, it helped us identify
predicted 30% where we needed to focus. But
Leaders
insights changes to the organization and
Followers
to business processes are
Able to align to necessary to benefit from the
execute on 45%
findings of the analysis.”
predicted 28%
outcomes ~ Derlin Mputu Kinsa,
Corporate Strategy and
0% 10% 20% 30% 40% 50% Business Intelligence Manager,
Wolters Kluwer


Using predictive analytics to support the sales and marketing function is
different from using predictive analytics to support strategic planning. When
sales and marketing initiatives are supported by this technology, insight from
the solution can be injected into customer interactions. That means that
employees lower down the management ranks must be empowered to act
on those insights. Leaders are 50% more likely than Followers to be willing
to delegate this decision-making.
Leaders are also more willing than Followers to reshape the organization to
make the most of analytic insight. As the case study below illustrates, when
predictive analytics is used to support a customer retention strategy,


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changes to operational processes will often be needed. Changing a process
changes the roles, responsibilities and reporting structure of the people
involved in that process. That can be a challenge for some organizations. For
example, a formal customer retention program is likely to require some or
all of the following:
• The formation of a "win-back" team that specializes in reacquiring
defecting customers.
• The formation of a team dedicated to managing high-value
customers.
• The creation of a virtual team that engages the customer at various
points in the lifecycle, rather than a solitary account manager being
the primary point of contact.
Each change can lead to bruised egos and office politics. Companies differ in
their willingness, ability, and expertise to make such detail changes.
However, Leaders are over 50% more likely to be able to do so than
Followers.
Case Study — Wolters Kluwer

Wolters Kluwer is a global provider of information, software, and
services that help professionals do their work more quickly and
efficiently. For 175 years Wolters Kluwer has provided professionals with
necessary information in the fields of legal, business, tax, accounting,
finance, audit, risk, compliance, and healthcare. The company has
approximately 19,000 employees in over 45 countries, and customers
in more than 147 countries. Annual revenues for 2010 were just over
€3.5 billion.
Wolters Kluwer has been using predictive analytics for the last three
years to drive improvements in customer retention. Three years ago, the
company decided to launch a customer retention program in Europe. In
devising and implementing this program, one of the major goals was that
it be fact-based. Initially, this project was tightly focused on understanding
and distilling key learnings. But, after demonstrating success in Europe,
the initiative has expanded in two ways. Firstly, the program grew to
include the United States. Secondly, the scope expanded to embrace the
entire customer lifecycle: improving the lead generation process,
improving the customer acquisition process and also customer
development - cross-selling and up-selling.
The program started with a straightforward analysis to understand which
segments of the customer base were most affected by high rates of
customer defection.
continued


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Case Study — Wolters Kluwer

This allowed Wolters Kluwer to focus its efforts and start to improve
some of its internal processes. In particular, analytics was used, both to
identify those customers, but also to identify key events that led to
customer defection. Once the root causes were identified, steps could be
taken to address these issues. For example, many cancellations occurred
at the seed phase during customer acquisition. Consequently, the
company focused on improving the targeting of customers so that
professionals were not offered products that were unsuitable for them.
Likewise, the customer onboarding process was fine-tuned to include a
thorough introduction to the product, and provide a series of touch
points during the introductory phase. “Analysis was just the beginning, it
helped us to identify where we needed to focus,” noted Derlin Mputu
Kinsa, Corporate Strategy and Business Intelligence Manager at Wolters
Kluwer. “But changes to the organization and to business processes are
necessary to benefit from the findings of the analysis.”
Once up and running with their products, Wolters Kluwer continues to
work proactively with the customer. Here, a different approach is used
based on the value of the customer, the products they have, and the way
they use those products. Based on the data they collect and the insights
they gain, the customer may be offered alternative products, or simply
additional training and workshops to help them realize the full value from
their existing subscription. Predictive analytics is also used to prevent
defecting customers. Before customers show signs of dissatisfaction, the
firm uses predictive analytics to identify them and to determine what
offer or action they should make to maximize the odds of keeping the
customer. In the longer term, analysis of canceled subscriptions is used to
shape product development and pricing strategy.
“This retention process has become a key part of our business. In one
division last year we improved retention by 1%. That may not sound like
much, but when the product portfolio is very large, it adds up to a lot of
money,” concluded Derlin Mputu Kinsa. “The success of the retention
program isn't just in building analytics. It's mainly due to reshaping
operations so that business managers can take advantage of the insights
from analytics at critical touch points with the customer.”

The Virtuous Cycle of Performance Management
Two themes emerge from the way Leaders adopt performance
management. The first is the importance of measuring marketing
performance in a granular way.


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Figure 9: The Performance Management Virtuous Cycle

Ability to track KPIs
associated with 84%
customer 57% Fast Facts
performance
Building and refreshing
Measure predictive models is a time
effectiveness of 59% consuming process:
individual marketing Leaders
49% √ After the business need has
campaigns Followers been identified, it takes a
total elapsed time of 37 days
Ability to
incorporate 54% on average to build a
campaign results
predictive model
33%
into models √ It takes an average of 10
days to refresh a predictive
0% 20% 40% 60% 80% 100% model when necessary


Leaders are 47% more likely than Followers (84% v. 57%) to have the ability
to track key performance indicators (KPIs) associated with customer
performance. Fifty-nine percent (59%) of Leaders also track the
effectiveness of individual marketing campaigns, compared with 49% of
Followers (Figure 9). While valuable as a means of tracking and trending
marketing performance over time, the ability to capture these metrics gives
marketing leaders critical data with which to refine segmentation and
marketing offers. In fact, 54% of Leaders say they have the ability to
incorporate campaign results into their statistical models, compared with
33% of Followers. This ability to incorporate results from previous
campaigns back into predictive models so their accuracy can be improved
and enhanced creates the potential for a virtuous cycle, where marketing
performance continues to improve over time.

Key Takeaways
Based on Aberdeen's research, marketing organizations currently using - or
considering using - predictive analytics should note:
• Leaders significantly outperform Followers in marketing
and related business performance. For example, the average
marketing response rate of Leaders is 67% higher than Followers,
and Leaders also grew their customer retention rate four times
faster than Followers. The ability of Leaders to gain deeper
understanding of customers and prospects, target marketing offers
more precisely, and deploy the skills at their disposal more wisely all
contribute to these significant differences in performance.


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• Followers are planning action to close the performance
gap. Followers are planning to invest in many of the capabilities
that have helped Leaders differentiate their marketing and business
performance. In the next 12 months 43% of Followers plan to add
the capability to customize offers for individuals (not just segments).
A similar number of Followers plan to improve their ability to
estimate lifetime customer value - an important factor in providing
the right offer to the right person at the right time.
• Unstructured data is growing in importance. Leaders are
more likely than Followers to be employing tools to analyze social
media already. However, both groups recognize the important of
social data. Both Leaders and Followers are planning to invest in
social network analysis tools within the next 12 months (25%
overall). However, Leaders distinguish themselves from Followers
when it comes to future investments in text mining and sentiment
analysis software. Thirty-seven percent (37%) of Leaders plan to
invest in this emerging technology over the next year, compared to
just 24% of Followers.
• Further automation can improve the performance and
impact of predictive models. Survey respondents report that
many areas concerned with the use of predictive models are labor
intensive. For example, 58% of all survey respondents report that
preparing the data to feed predictive analytics models is either all or
mostly a manual process, with little automation. Similarly, model
building is mostly or entirely a manual process at 55% of companies.
Manual processes are time consuming and error prone. By bringing
increased automation to bear on the predictive modeling cycle,
marketing departments can increase the rate at which they build,
refine and deploy predictive models. This should, in turn, let chief
marketing officers bring more precision to their inbound and
outbound marketing activities.
For more information on this or other research topics, please visit
www.aberdeen.com


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Related Research
Predictive Marketing for Sales and The Marketing Executive's Agenda for
Marketing: Seeing Around Corners; 2012: Uncovering the Hidden Sales Cycle;
January 2012 October 2011
Customer Experience Management: Analytics for the CMO: How Best-in-Class
Using the Power of Analytics to Optimize Marketers Use Customer Insights to Drive
Customer Delight; January 2012 More Revenue; September 2011
Sales Performance Management 2012: Future Integration Needs: Embracing
How the Best-in-Class Optimize the Front Complex Data; July 2011
Line to Grow the Bottom Line; Predictive Analytics – Driving Sales with
December 2011 Customer Insights; September 2010
Author: Trip Kucera, Senior Research Analyst, Marketing Effectiveness and
Strategy, (trip.kucera@aberdeen.com), David White, Senior Research Analyst,
Business Intelligence (david.white@aberdeen.com)

For more than tw o decades, Aberdeen's research has been helping corporations worldwide become Best-in-Class.
Hav ing benchmarked the performance of more than 644,000 companies, Aberdeen is uniquely positioned to provide
organizations w ith the facts that matter — the facts that enable companies to get ahead and driv e results. That's why
our research is relied on by more than 2.5 million readers in over 40 countries, 90% of the Fortune 1,000, and 93% of
the Technology 500.
As a Harte-Hanks Company, Aberdeen’s research provides insight and analysis to the Harte-Hanks community of
local, regional, national and international marketing executives. Combined, we help our customers leverage the power
of insight to deliv er innovative multichannel marketing programs that drive business-changing results. For additional
information, v isit Aberdeen http://www.aberdeen.com or call (617) 854-5200, or to learn more about Harte-Hanks, call
(800) 456-9748 or go to http://w ww.harte-hanks.com.
This document is the result of primary research performed by Aberdeen Group. Aberdeen Group's methodologies
prov ide for objective fact-based research and represent the best analysis available at the time of publication. Unless
otherw ise noted, the entire contents of this publication are copyrighted by Aberdeen Group, Inc. and may not be
reproduced, distributed, archived, or transmitted in any form or by any means without prior w ritten consent by
Aberdeen Group, Inc. (2012a)

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Featured Underwriters
This research report was made possible, in part, with the financial support
of our underwriters. These individuals and organizations share Aberdeen’s
vision of bringing fact based research to corporations worldwide at little or
no cost. Underwriters have no editorial or research rights, and the facts and
analysis of this report remain an exclusive production and product of
Aberdeen Group. Solution providers recognized as underwriters were
solicited after the fact and had no substantive influence on the direction of
this report. Their sponsorship has made it possible for Aberdeen Group to
make these findings available to readers at no charge.

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