Unlocking the Power of Analytics for Customer Insights

The provided content discusses the importance of analytics in businesses, highlighting the potential benefits of enabling skills and knowledge in customer analytics, updating skills and knowledge in tools of analytic software, and reflecting on potential outcomes. The text also addresses research limitations, issues of access and ethics, and references several studies and publications to support its arguments.

Running head: CUSTOMER ANALYTICS
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Table of Contents
Introduction......................................................................................................................................2
Research Objective......................................................................................................................3
Research Question........................................................................................................................3
Critical Evaluation of Literature......................................................................................................3
Research Methodology....................................................................................................................5
Elements of Research Design......................................................................................................5
Research Methodology....................................................................................................................5
Research Design...........................................................................................................................5
Methods of Data Collection.........................................................................................................6
Secondary Method for Data Collection.......................................................................................6
Primary Method for Data Collection...........................................................................................6
Procedure for Data Collection......................................................................................................6
Sampling Method.........................................................................................................................6
Sample Size..................................................................................................................................7
Research Limitations....................................................................................................................7
Issues of Access and Ethics.............................................................................................................7
Reflection on Potential Outcomes...................................................................................................8
Enabling Skills and Knowledge in Customer Analytics..............................................................8
Updating Skills and Knowledge in tools of Analytic Software...................................................8
Conclusion.......................................................................................................................................9
References and Bibliography.........................................................................................................10

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Introduction
The companies today are able to make the effective, timely and accurate decisions at every level:
strategic, tactical and operational for addressing the priorities and preferences of the customer in
largely competitive market. Moving upward, the companies in almost all the industries around
the world have initiated to use advanced customer analytics for analyzing their data which is
both unstructured and structured, which combines information over future actions that are
projected, present events and past circumstances (Adomavicius, et al., 2005). By incorporation of
the advanced customer analytics in the daily operations and activities, the organizations are able
to gain the control on the decisions which they are making to successfully achieve their
objectives and goals of the business. The customer analytics driven information data analysis
permit the enterprises for having an aggregate or a view of 360 degree of their customers and
operations. The insights which they earn from these analysis is used them for automating,
optimizing and directing their making of decisions. It gives the results where there is successful;
attainment of particular goals of the organization, whether they are linked to an enhancement in
the cross ell generation of revenue, a lowered service cost or production cost, a lowered behavior
which is fraudulent and an enhancement in response rates for promotional campaigns. Customer
analytics is defined as term which means application of different techniques of customer
analytics to data for answering the questions or resolving the problem. It cannot be considered as
a technology but a group of equipment or tool which is used with another tool for getting the
information, doing analysis of that information and predicting the results of the solutions of that
problems. Data mining and data integration are basis for the customer analytics. The more the
information can be integrated and gathered, the better recognition of pattern and identification
for relationship is analyzed (Apte, et al., 2003). Analysis by making use of statistics is yet

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another very significant tool for assessing the patterns and trends of data. The other techniques
that used for manipulating the data involves fuzzy logic which is used for dealing with
ambiguous or incomplete data as well as the neural network for anticipating the decisions ns
assist in the predictive analytics that helps for predicting the likely results.
Research Objective
 To get an insight into the Customer analytics, its advantages, implementations and
disadvantages.
 To assess the issues which are related to its efficient management and implementation
within broader context.
Research Question
The research question formulated for this research paper is to analyze the Customer analytics in
terms of its effective implementation.
Critical Evaluation of Literature
All through the history, the experts of marketing field have developed means to enhance their
ability to identify properly more sober differentiations among target market. The emergence of
internet technology have offered the marketers in the organizations a better tool to segment the
market and recognize their target as compared to earlier times. Proper segmentation is able to
disperse information. Segmentation is defined as compression of information. It is not useful as it
gives the information regarding significant attributes of customers (Germann, et al., 2014). The
emergence of social media and internet as well as latest opportunities for data collection allow a
look into the minds and heart of the customers so often as compared to any collection of data
before. The organizations have responsibility towards taking care of the customers by having
focus on the attention to keep and win the customers. The company affords the opportunity on its
own for taking a long term approach for customer retention and customer engagement. Coining

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of term called as “relationship marketing” discusses maintaining and building customer
relationships and brand loyalty in its customer base. This shift in marketing focus to more
contemporary relationship marketing must allow the organization for maintain the customer
retention all through long term which enhance the company bottom lines which achieves desired
economies of scale and develop a consistent and coherent image of the offerings (Abraham, et
al., 2016). The segments of target market remain loyal and faithful to organization as well as its
product line because company and customer have asserted a relationship all through efforts of
company to recognize the differences in users. It leads to high understanding of decision and
needs criteria of customers. In aggregate all the areas which a company use for segmenting the
potential base of customer namely positioning and targeting task, to lower costs and to foster the
adaptability to change in environment by the firm, it lies in positioning and targeting area in
which the social media data may become vitally significant (Marsella, et al., 2005). The key to
understand the importance of customer analytics involves recognizing usefulness and limitations
while arranging and planning a marketing strategy. The organizations should realize that users of
social media do not possess an association and link between purchase decisions and sites (Patel,
et al., 2014). Customers may be segmented by blood pressure, attitude, geography, product
interest, region, gender and age or any other aspect. The segmentations are justifiably good and
beneficial for any stated purpose. The company capacity for understanding the aspects it seeks
for quantifying a significant dimension of relationship is crucial for succeeding here. The
companies should crucially evaluate and assess their own systems of values, message and value
that are conveyed by the product offerings as well as the associations which are extended
towards customer base (Gopal, et al., 2016). The organization should assess itself empirically, by
collecting intrinsic data. The companies may utilize the data that is garnered by using its data

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pursuits of social media for developing a very precise model for segmentation, increase the
effectiveness of segmentation and performance of targeting as well as identify the opportunities
for achieving the goal that is underlying the development of relationships to use lowered
dimensions and aspects and perform the multi group modeling of structure equation. The
companies should become a better end user of data. They must learn to judge volume of the data
that is made available as well as initiate for determine the approaches to data for making sense as
per the newfound identity (Corrigan, et al., 2014).
Research Methodology
Elements of Research Design
It is defined as translation of requirements of customer objectives and survey into process that is
to be used. Additionally, to clearly state the objectives of Customer Analytics, the significant
elements of research design comprise: questionnaire design, selection of survey method, sample
design, type of customer survey and qualitative evaluation. Normally, the qualitative evaluation
flows initial statement and declaration of objectives of Customer analytics. Most commonly the
qualitative method entail deep interviews or the focus group that are conducted via various other
customer groups. The kind of survey of customer used to measure the satisfaction of customers
involve general transaction satisfaction tracking and customer satisfaction racking which is
assessed by the population being determined with respect to transaction and customers. Sample
design is defined as the population, the way to contact them and the real method of sampling to
be used (Sharda, et al., 2013).

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Research Methodology
Research Design
Exploratory research is conducted to define and clarify the kind of issues which does not provide
the decisive evidence and proof as well as subsequent research can also be expected. The main
objective of this research paper is to understand the concept of customer analytics and its impact
on growth of business. The results which are acquired and achieved through this study cannot be
classified into large population and thus the design of this research is known as exploratory
research.
Methods of Data Collection
For this reach paper, the qualitative and quantitative method to collect the data are used for
generating results which are convenient and easy to summarize, compare and generalize. The
data collection is done by making use of primary methods and secondary methods.
Justification for choosing them
Collection and techniques of data
Secondary Method for Data Collection
All the information has been gathered by single books, journals, articles, newspapers, research
reports, dissertations, research papers, and online journals.
Primary Method for Data Collection
Questionnaires will be generated online for this research survey for customers and business who
use customer analytics and will be directed to individual participant through mails.

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Procedure for Data Collection
Online questionnaires has been planned for the research for getting the data and information
from selected sample that has been planned to be generated by making use of Google Form.
Links that need to be generated will be loaded on social sites and will be sent to the contacts
through messengers.
Sampling Method
Around 100 employees from a multinational retail company has been chosen who are using the
software of Customer analytics (Reed, et al., 2014).
Sample Size
Size of Sample Vendor Consumer Total
Estimated 100 100 200
Research Limitations
Using online forms of questionnaires excludes every person who is not using internet. Thus, no
reliability is there to answer the questions correctly by respondents because of limited time and
resources. All the tools of the engagement for social media is not properly discovered.
Issues of Access and Ethics
For this research paper, there are many ethical and social considerations that are required to be
taken care of. The participants in this research paper must not be subjected to any harm of any
kind. The priority must be given to the respect of the dignity of participants in the research.
Complete consent must be taken from participants before the study. The security of privacy of
the participants in the research must be ensured (Bijmolt, et al., 2010). Anonymity of the
organizations and individuals who are participating in research paper must be ensured. Any

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exaggeration or deception regarding objectives and aims of research should be avoided. Any
kind of communication relating to research must be done with transparency and honesty and
misleading data and representation of the findings of primary data in a biased way should be
prevented. Respondent’s voluntary participation in research is significant. Using discriminatory,
offensive or any other type of language which is unacceptable must be avoided while
formulating the questionnaires. The researcher will acknowledge the work of the other authors
which is used in the part of this research dissertation while using APA or Harvard system of
referencing as per the Dissertation booklet (Feldman, et al., 2007). The researcher will maintain
the highest objectivity level while analyzing and discussing the issues all through the research.
Reflection on Potential Outcomes
Enabling Skills and Knowledge in Customer Analytics
The present gap of communication between business and analysts may efficiently be lowered by
proper encouragement of developing the related skills and business acumen of analysts.
Nowadays, many more effective and efficient tools and techniques of analytics are developed
(Froelich, et al., 2005). The industry analysts must constantly develop the awareness of trends of
industry, the approaches which ae successful and new knowledge and techniques regarding the
clever implementation of the techniques and tolls in the projects of analytics.
Updating Skills and Knowledge in tools of Analytic Software.
Analytics based software space is dynamic which involve new players who are excited to enter
the market. In reality, many a times, the intensity of advantage from the new latest software is a
great surprise. For instance, big organizations like ATO obtain high value by making use of
Open Source Software in projects that are specialized along with obtaining best return from the
high end analytics software that can perform multiple functions (Li, et al., 2004).

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Conclusion
Analytics is defined as tool which has evidenced potential for giving the valuable insights in
ways to help the organizations for addressing the improved growth of revenue, retention of
customer as well as other fundamental objectives. It possess a value which brings into light the
significant hidden factors which hold back the business from achieving the full potential as well
as allow the ways which are skillful to approach the overcoming of restraints which are imposed
by the factors mentioned (Lui, et al., 2007). Abandoning this kind of benefits is considered as
cost to not utilize the analytics strength as being the part of competitive artillery of the company.
Among significant and necessary situations for success of projects of analytics in company are
the proper data availability and quality, correct choice and selection of software for analytics and
awareness of organization for potential of analytics. This research study, outlines the approach
which is designed for allowing the optimum utilization and use of analytics in the context of
business. Therefore, giving careful attention towards prior design of this project trajectory may
allow and permit the business in all the industry to realize fully the benefits which the analytics
need to provide.

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References and Bibliography
Adomavicius, G. and Tuzhilin, A. (2005), “Towards the next generation of recommender
systems: a survey of the state-of-the-art and possible extensions”, IEEE Transactions of
Knowledge and Data Engineering, Vol. 17.
Apte, C.V., Hong, S.J., Natarajan, R., Pednault, E.P.D., Tipu, F.A. and Weiss, S.M. (2003),
“Data-intensive analytics for predictive modeling”, IBM Journal of Research & Development,
Vol. 47 No. 1, pp. 17-23.
Germann, F., Lilien, G. L., Fiedler, L., & Kraus, M. (2014). Do retailers benefit from deploying
customer analytics?. Journal of Retailing, 90(4), 587-593.
Abraham, P., Pradhan, M., Lakshminarayanan, S., & Iyer, G. (2016). Customer analytics at
bigbasket. com: product recommendation.
Gopal, R., Santhanam, J., & Muralidharn, S. M. (2016). U.S. Patent Application No. 15/078,774.
Corrigan, H. B., Craciun, G., & Powell, A. M. (2014). How does target know so much about its
customers? Utilizing customer analytics to make marketing decisions. Marketing Education
Review, 24(2), 159-166.
Sharda, R., Delen, D., & Turban, E. (2013). Business Intelligence: A managerial perspective on
analytics. Prentice Hall Press.
Reed, D. (2014). Customer analytics and insights in retail financial services. Journal of Direct,
Data and Digital Marketing Practice, 15(4), 361.
Bijmolt, T. H., Leeflang, P. S., Block, F., Eisenbeiss, M., Hardie, B. G., Lemmens, A., & Saffert,
P. (2010). Analytics for customer engagement. Journal of Service Research, 13(3), 341-356.

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Feldman, R. and Sanger, J. (2007), The Text Mining Handbook: Advanced Approaches in
Analyzing Unstructured Data, Cambridge University Press, New York, NY.
Froelich, J., Ananyan, S. and Olson, D.L. (2005), “Business intelligence through text mining”,
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HICSS.2007.268
Marsella, A., Stone, M. and Banks, M. (2005), “Making customer analytics work for you!”,
Journal of Targeting, Measurement and Analysis for Marketing, Vol. 13 No. 4, pp. 299-303.
Patel, A., Gheewala, H., & Nagla, L. (2014, March). Using social big media for customer
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