1. Running Head: ANALYTICS AND SEGMENTATION 1
Analytics and Segmentation and
The Increasing Importance of Social Media Data
Bernard J. Karlowicz Jr.
Wilkes University
Author Note
Bernard J. Karlowicz Jr., College of Graduate and Professional Studies, Wilkes
University.
Correspondence concerning this article should be addressed to Bernard J.
Karlowicz Jr. C/O Kathleen Houlihan, College of Graduate and Professional Studies,
Wilkes University, Wilkes-Barre, PA 18766.
Contact: bjk002@yahoo.com

2. ANALYTICS AND SEGMENTATION 2
Abstract
This paper explores the rise of complex database lists as a result of the internet and social
networks and how this new information, along with new techniques in analytical data mining, is
making an impact on the marketing discipline, changing forever the ways in which companies
collect, analyze, segment, and act (target) on new data. This paper leverages the information
from several published articles which themselves explore unique aspects of the advent of social
data as a new information source, as well as the association between analytics and market
segmentation and targeting. Foedermayr and Diamantopoulos (2008), explore the effectiveness
of segmentation from the perspective of international companies, offering insights into their
evolving business practices as companies accelerate their investments into analytics as a means
to adapt to change, reduce costs, and capitalize on new opportunities. Jedidi, Jagpal, and
Desarbo (1997), dive deep into the mathematical constructs and equation models that underlies
the latest efforts to aggregate social data and allow for the treating of heterogeneity in social
data. Dibb and Simkin (2000) is a useful article exploring internal relationships in corporate
culture, the need to evaluate, question, and change established marketing orthodoxy, and the
sensibility of engaging external agencies for segmentation process enhancements and strategic
modifications and data access enhancements. Sheth-Voss and Carreras (2010), discuss some of
the latest aspects of information theory applied to current data mining and market segmentation
practices, highlighting the struggle to bridge perception/reality conflicts in business today.
Roberts (2008) again discusses the opportunities and challenges associated with the collection,
analysis, dissemination, and practical application of segmentation data through the corporate
lens. Remaining articles are used primarily for a specific quote or thought promotion so as to
lend credence to the conclusions of this paper.

3. ANALYTICS AND SEGMENTATION 3
Analytics and Segmentation and
The Increasing Importance of Social Media Data
Throughout history, marketing experts have sought means of enhancing their ability to
more properly identify ever more subtle differentiations between target markets. The onset of
the internet, and in particular social media, have provided marketing organizations a means by
which to more thoroughly segment markets and identify targets than ever before. “Good
segmentations convey information. In our view, segmentation is information compression.
Segmentation is not useful unless it conveys information about important customer attributes.
Ideally the converse is also true; observable customer attributes convey information about
segment membership.” (Sheth-Voss et al., 2010). This basic premise underlies the foundations
of this paper, whereby the advent of social media and the newly available data collection
opportunities as a result, allows a glimpse into the hearts and minds of the customer more so than
any data collections prior.
But why should an organization care about the hearts and minds of its potential customers? The
answer to such a question is obvious. By focusing attention on the “winning and keeping” of
customers, an organization affords itself the opportunity to take a longer term approach to
customer engagement, and customer retention (Dibb and Simkin, 2000). The coining of the term
“relationship marketing”, first coined by Berry in 1983 (Dibb and Simkin, 2000), discusses the
maintenance of customer relationships and building brand loyalty among its customer base. This
fundamental shift in marketing focus, from mere transactional selling, to the more forward
thinking relationship marketing, should allow an organization to maintain its customer retention
over the long term, enhancing company bottom lines, achieving the much desired economies of

4. ANALYTICS AND SEGMENTATION 4
scale, and developing “a coherent and consistent image of their offerings” where targeted market
segments are more likely to remain loyal to the company and its product line as the customer and
company have forged a relationship through the company’s efforts in “recognizing users’
differences, leading to an increased understanding of customer needs and decision criteria”
(Foedermayr et al., 2008). This basic tenant of evolving marketing practice is where the advent
of social media data becomes so significant. In all, of the four areas companies use to segment
their potential customer base, namely targeting and positioning tasks, in reducing costs and in
fostering a firm’s adaptability to environmental changes, it is in the targeting and positioning
area where social media data will become so vitally important (Foedermayr et al., 2008).
Key in understanding the significance of social media data is to recognize its limitations and
usefulness when contriving a marketing strategy. First and foremost, companies need to
recognize that “social media users do not have a strong association between these sites and
purchase decisions. They see them as being more about personal connection, so finding ways to
embrace that powerful function is key” (Smart actions for tough times, 2009). Customers can be
divided by age, gender, region, product interest, geography, attitudes, blood pressure or any other
dimension. Segmentations “exist” as soon as we define them. But not all segmentations are
equally good for a given purpose (Sheth and Carreras, 2010). The capacity of a company to
understand the dimensions it seeks, in order to fully quantify a key relationship dimension is
critical to success here. Companies must critically evaluate their own value system, the value
and message conveyed by their product offerings, and the “relationship” that can be extended to
the customer base. The company must evaluate itself empirically, gathering internal data and
asking questions on that data in terms of what relationships it can and is willing to offer its

5. ANALYTICS AND SEGMENTATION 5
potential customer. From this position, companies can begin to mine the data presented through
the rise of social media in an attempt to ascertain where appropriate targets and segments reside.
Companies can utilize data garnered through its social media data pursuits to develop more
precise segmentation models, increase segmentation effectiveness and targeting performance,
and identify opportunities to achieve the underlying goal of developing relationships with their
customers. Market programs can be designed to more effectively pursue niche market
opportunities revealed through the mining of social data and application of Information Theory.
A Market researcher can “use a variety of data-reduction methods (e.g. principal components or
factor analysis) to filter the aggregate data by purging measurement error, form clusters
(segments) using the reduced dimensions, and then perform multi-group structural equation
modeling” (Jedidi et al., 1997). Essentially, companies need to become better consumers of data.
They need to learn how to judge the volumes of data being made available, and begin to
determine what approaches to the data make sense given their newfound identity. When
companies begin to ask questions such as “What if we treat these variables as ordinal rather than
nominal?” “What if we include current product shares as attributes?” “What if we reduce the
attitudinal questions via factor analysis and use the factors in the segmentation analysis?” (Sheth
and Carreras, 2010), companies are on the beginnings of developing a marketing strategy aligned
with their, and their customer’s interests. Enveloping the utilization of entropy, surprise, and
latent class analysis within the scope of analyzing social data via algorithmic processes such as
K-Means testing, Bayes information criterion, and Total Mutual Information, formulate the
beginnings of a novel approach, based on Information Theory, to approach customers by way of
relationship forging. This co-development lends itself to the establishment of a relationship
between the company and the consumer unlike efforts of the past where simple management

6. ANALYTICS AND SEGMENTATION 6
insight or simplistic correlation analysis once owned the road in terms of segmentation
evaluation and understanding (Sheth and Carreras, 2010). “Smart firms make above-market
returns on their investments in customer information, analyzing that information and then
building business strategies around what they have learned.” (Smart actions for tough times,
2009).
So, in understanding the need for relationship building with the consumer, along with
new insight into the need to develop more robust segmentation and targeting strategies based on
solid, well understood Information Theory principles, companies need to realize the point, and
turn their attention to the collection of this vital information available via social media. “If you
can so segment the market that you really understand who is buying and what their motivation is,
you can make offers that are most relevant.” (Hopkins, 2009). Hopkins postulates that
companies must turn to improving their internal data collection with the aim of improving its
quality, and thus allow an adjoining of this internal data with data collected externally, via
external sources such as rating bureaus, social media sites, etc… The leveraging of online
marketing channels in conjunction with external data providers and web analytics service will
provide a means by which the company can make better segmentation and targeting decisions
with the aim of forging relationships with their best customers. “The bottom line is that in a
challenging economic landscape, marketers must focus on data intelligence in order to succeed.
FLaving an in-depth understanding of your existing customers is the first step in learning more
about your prospects, which ultimately leads to better segmentation, better targeting and better
campaign performance.” (Hopkins, 2009).

7. ANALYTICS AND SEGMENTATION 7
The final issue that needs to be addressed is the manner in which companies collect data from
such external partners as social media sites, bureaus, external data providers, etc… As was so
poignantly addressed in the article ‘I Know all about you’, “The data does need to be bandied
in a compliant way within all privacy laws and codes of conduct.” (Roberts, 2008). Compliance
concerns, SOX compliance, and other regulatory concerns need to be addressed when collecting
and managing customer data. Companies need to take care to properly handle such information,
and utilize it accordingly. It is my contention that companies should consider leveraging outside
experts and data collection facilities to provided cleansed and scrubbed data, pre-prepared for
company consumption, eliminating the risk of data non-compliance. “Another issue worthy of
future research concerns the role of external experts (i.e. market research agencies, advertising
companies, consultancies, etc.) in supporting firms with their international segmentation
decisions. Although our exploratory study revealed that external experts are frequently involved
in segmentation decisions, little is known as to what services they offer to the firms that employ
them or the benefits in terms of improved segmentation effectiveness that are obtained by using
external expertise.” (Foedermayr et al., 2008).
In light of this new understanding of the role of social data, the means to evaluate it, and
the impact and insights such efforts bring to the company, it is imperative to understand the
pitfalls companies face in developing a marketing plan with this newfound wealth of knowledge
from the consumer. Dibb and Simkin provide a wonderfully articulate list of common pitfalls
(impediments) companies most often face in this regard. The table below is a well researched
list of said impediments that stand in the way of companies and their means of developing
relationships. Though the purpose of the research is to draw insight into internal relationship

8. ANALYTICS AND SEGMENTATION 8
forging within a company, the same attributes and rules apply when attempting to build external
relationships, hence my inclusion of this table here in this paper:
Dibb and Simkin's Observed Barriers Hindering Marketing Planning
1. Poor Grasp of the Marketing Concept
2. Little or No Marketing Analyses Undertaken
3. Strategy Determined in Isolation of Analysis or Formulation of Tactical
Programmes
4. Blinkered View of the External Environment
5. Poor and Inadequate Marketing Intelligence
6. Little Internal Sharing of Marketing Intelligence
7. Inadequate Understanding & Support from Senior Management
8. Poor Internal Communications in Marketing, Between Functions/Tiers
9. Planning Activity Fades Out
10. Planning and Personnel Overtaken by Events
11. Lack of Confidence/Conviction
12. Little Opportunity for Lateral Thinking
Figure 1 - (Dibb and Simkin, 2000)
Companies must evolve to save themselves from the trend of obsolescence through
generalization. Niche markets and niche products will dominate the landscape of company
success in the coming years. Those companies best prepared to dominate those niches,
demonstrating themselves as offering best of breed product lines in subtle markets, along with a
means to truly engage the consumer in relationship building, will find success in the new
tomorrow.

9. ANALYTICS AND SEGMENTATION 9
References
Dibb, S., & Simkin, L. (2000). Pre-Empting Implementation Barriers: Foundations, Processes
and Actions-The Need for Internal Relationships. Journal of Marketing Management,
16(5), 483-503. Retrieved from EBSCOhost.
Foedermayr, E. K., & Diamantopoulos, A. (2008). Exploring the Construct of Segmentation
Effectiveness: Insights from International Companies and Experts. Journal of Strategic
Marketing, 16(2), 129-156. doi:10.1080/09652540801981579
Hopkins, D. (2009). Finding the hidden value of lists and databases. B to B, 94(2), 24. Retrieved
from EBSCOhost.
Jedidi, K., Jagpal, H. S., & Desarbo, W. S. (1997). Finite-mixture structural equation models for
response-based segmentation and unobserved.. Marketing Science, 16(1), 39. Retrieved
from EBSCOhost.
Smart actions for tough times. (2009). Marketing Management, 18(4), 3. Retrieved from
EBSCOhost.
Roberts, J. (2008). I Know All About You. NZ Marketing Magazine, 27(1), 28-31. Retrieved
from EBSCOhost.
Sheth-Voss, P., & Carreras, I. E. (2010). HOW INFORMATIVE IS YOUR SEGMENTATION?.
Marketing Research, 22(4), 9-13. Retrieved from EBSCOhost.