This document provides an analysis of the unmet analytics needs of retail companies. It conducted interviews with retail industry professionals to understand the tools currently used and key metrics tracked. While many retailers use tools like SAS and Oracle Endeca for reporting and dashboards, there are gaps in areas like predictive analytics, social media analysis, and seamless integration. The document recommends solutions like predictive intelligence, dynamic customer segmentation, and ROI analysis to help retailers better understand customer behavior and improve supply chain management.

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UNMET ANALYTICS NEEDS OF RETAIL COMPANIES
-
PRESENT SCENARIO AND WAY FORWARD
By
Sachin Serigar
Under the Guidance of
Romila Chopra
SCHOOL of INSPIRED LEADERSHIP
GURGAON

2. 2
CERTIFICATE
This is to certify that Mr. Sachin Serigar has successfully completed the project titled
“Unmet analytics needs of retail companies”, in ORKASH Services Private Limited. It is an
independent research work done under my supervision during 20th August 2015 to 17th September
2015. It is being submitted to the School of Inspired Leadership in partial fulfillment for the award
of the program completion Certificate.
Rahul Raj Romila Chopra
Senior Analyst VP – Marketing & Admissions
Company Stamp Institute Stamp

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ACKNOWLEDGEMENT
I would like to express my gratitude to all those who gave me the possibility to complete this
research project. I am deeply indebted to my supervisor Romila Chopra, Renu Misra and Rahul
Raj (Project Guide from ORKASH) for their help, stimulating suggestions and continuous
encouragement helped me throughout my research during the post graduate program.
I would like to extend my sincere and heartfelt obligation towards all the people who have helped
me in this endeavor. Without their active guidance, help, co-operation and encouragement, I would
not have made headway in the project.
With immense gratitude I would like to thank Neetika Batra, Program Chair (BLP) for her full
support and guidance and for providing me this opportunity.
I extend my gratitude to SCHOOL Of INSPIRED LEADERSHIP for giving me this opportunity
to learn about a new area of research and to understand my own capability in the research field.
Finally it is all to the almighty who has always blessed me and guided me to the right tracks.
Thanking You,
SACHIN SERIGAR
BLPG059

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TABLE OF CONTENTS
1. Brief Company Profile……………………………………………………………..5
2. Abstract……………………………………………………………………………..6
3. Introduction………………………………………………………………………...7
4. Research Methodology……………………………………………………………..8
5. Analysis………………………………………………………………………….…..9
6. Recommendations………………………………………………………………….12
7. Limitation…………………………………………………………………………...13
8. Conclusion…………………………………………………………………………..14
9. Bibliography………………………………………………………………………...15
10. Appendix…………………………………………………………………………….17

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BRIEF COMPANY PROFILE
ORKASH is a high-end technology services and management consulting company which
deals majorly in areas like Market, Competitive, and Consumer & Political Intelligence, Business
Assurance and Operational Risk Management and Defense & Homeland Securities.
Various services provided by ORKASH are as follows:
 Business Assurance
 Operational Risk Management
 Market & Competitive Intelligence
 Homeland Security
 Security Consulting
 Public Policy
 Political Consulting
 Social Media Intelligence
 Smart City Initiatives
 Investigation Solutions
ORKASH works with the senior executives of its client companies to enable them to
respond to new opportunities, competitive forces, and risks quickly and efficiently. Their client-
base is diverse, consisting primarily of large and mid-size internationally active businesses and
financial institutions, including many of the Fortune 1000 companies.
Companies working in Big Data Analytics, Data Visualization, Social CRM, Business
Assurance, Market Assessment Studies, Consumer behavior, Market behavior, Product and &
Brand intelligence, Social Media intelligence would prove as a strong competitors for ORKASH.

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ABSTRACT
The research basically focusses on analyzing and zeroing the unmet needs of the retail
industry with respect to the analytical tools. The population selected was basically from the retail
industry and the sample was selected at random who were interviewed and their excerpts were
used to carry this research intensively. After interviewing and analyzing it has been captured that
few unmet needs which needs to be captured are prediction of human behavior, micro
segmentation of the market, adopting right pricing strategy, introducing the concept of
“Thanksgiving” and at the end stressing on ROI analysis.
The research also stressed on various ways of predicting human behavior, out of which one
method of Likelihood function was finally been determined for accurate prediction of human/
customer behavior.

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INTRODUCTION
Problem Definition:
 To understand current scenario of retail industry
 To understand how the market is segmented by solution providers
 To find the gaps and where ORKASH analytics solution can fit in
Objective of the Study:
 Determine the awareness of analytics solutions available for retail sector among the retail
companies in India.
o Data gathering and integration tools used
o Key metrics available to the retailers
o Determine the awareness and extent of use of Big Data solutions
 Segment the retails analytics industry on the basis of solutions already available (such as
dashboards, reporting tools, predictive analytics, social media analytics, etc.)
 Determine the unmet needs of analytics in terms of
o GIS analytics solutions
o OSINT and Social data integration
o Multi- channel integration
Two key variables to be concentrated here were the analytical tools used by the retail
industry and the extent of use of Big Data in the retail industry.
Retail industry has been growing at the ever increasing pace with industry like Fashion &
Apparels, Clothing’s, Food & beverages, Electronic Goods, Consumer Goods, Luxury items, etc.
And with them is the consumer base growing exponentially. To trace the performances of both the
retail industry and the increasing consumer base, and also to find a correlation between them, the
data has to be stored on a continuous basis.
Data which is generated in terabytes, petabytes cannot be processed or analyzed based on
the tools available in the market. And this we are talking about “Structured Data”. How about
“Unstructured Data” that is been generated continuously through various mediums like Social
Media, News Feed, etc. To trace all these data and do analytics on them, BIG DATA comes handy
and that is what it is meant for.
Analysis of the retail industry with regards to the amount of data and their interpretations
has been growing stronger, but to what to extent this data is useful and how fruitful they can be if
analyzed regularly and correctly. This research basically focusses on the same.

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RESEARCH METHODOLOGY
Tools Used:
The research conducted was qualitative in nature. Primary source of collection of data was
from questionnaires and interviews. Interviews were conducted of the people working from the
Retail industry. Secondary source of collection of data was through desktop research.
Questionnaires used were open ended and majorly focused on deriving information about
the retail industry and their influence. Interviews conducted were semi- structured or unstructured
depending on the flow and comfort of the interviewee.
Keeping the confidentiality of the interviewee in the mind, their company’s name and their
name has not been mentioned or recorded anywhere. They have been numbered as Interview No.
Desktop research formed the major part of collecting information from the retail industry,
tools being used by them and what are the gaps which could be tapped by the company.
Sampling Technique:
Sampling technique used was Snowball Non – Probability sampling technique. The
population was targeted as Retail Industry. Samples were selected at random from the retail
industry and asked them for details about their market and other retail market related data.
Sample size was 18, out of which 10 got ready for getting interviewed.

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ANALYSIS
Tools Used
The research was qualitative in nature, hence no statistical tools were used.
Interpretation with results
After initial research and after talking various industry person from retail sector
on analytical tools available in the market, several tools like HDPOS, Tally Shoper 9, Oracle
Endeca, SAS, MS Excel, GoFrugal, WSxM solutions, Ginesys came into picture. Out of which
SAS, Demandware and Endeca came out to be used widely for its advantageous applications and
wide client base.
Key metrics available for the retail sector were customer retention, incremental sales, cost
of goods sold, customer satisfaction, point of sales, average purchase sales, sales per square foot.
Whereas for the retailers and store managers key metrics available were footfalls, employee
management, inventory management, conversion percentage, point of sales and average basket
size.
As per the NASSCOM report, 70% of the large volumes of data are unstructured data. And
about 90% of the data in the world today has been created in the last two years alone. There are
many companies already operating in this domain of Big Data like Meltwater, Crayon data, Fractal,
Guavus, Germin8, Dataswift, etc. But there are some concerns related to this big data and those
are less availability skill sets, low level of technology awareness, lack of management sponsorship
and increasing data complexity.
After collection of data, exploring and filtering the data followed by integration and
measuring the data gives us the final data for analysis. For retail market, segmentation like
customer intelligence, managing campaign, lifecycle marketer, managing the loyalty, managing
the store, customer satisfaction, e-commerce, social connect and professional services.
Solutions already available are market analysis, dashboards, sentiment analysis, seamless
integration, predictive data analysis, news analysis, social media analysis and due – diligence.
Find below the segmentation:

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Image 1: Segmentation – Competitors
From the segmentation of all the competitors, it is evident that out of all the segmentation
due diligence, seamless integration, sentiment analysis and news analysis are done my less number
of the competitors. (From Image 1)
Whereas in case of segmentation of all the clients, from the diagram below, it is evident
that due diligence and sentiment analysis are been carried out by less number of clients. And when
it comes to news analysis, only one company does it. (From Image 2)
7
11
17
18
8
11
8
3
0 5 10 15 20
News Analysis
Social Media
Market Analysis
Dashboards
Sentiment Analysis
Predictive Data Analysis
Integration
Due diligence

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Image 2: Segmentation – Clients
After analyzing the above graph, the areas of improvement were risk management and competitive
analysis in case of market analysis, data from the news, aggregators, social media for sentiment
analysis and also concentrating on recommendations and personalization requests and lastly
seamless integration with the existing model.
Basic steps for calculating or predicting the human behavior was estimating the probability
that customer will choose to purchase a particular product and another one was to estimate the
probability that a customer will make a purchase at a particular time. The joint probability function
was devised to calculate by multiplying the probabilities of which and when to get the estimation,
but this contained the sampling error. The concept of human connection was conceptualized with
probability cube, but it contained the sampling error. To remove this error, another method of
predicting human behavior, was penned down named the Likelihood function by Donald
McFadden.
1
18
27
26
9
25
10
0 10 20 30
News Analysis
Social Media
Market Analysis
Dashboards
Sentiment Analysis
Predictive Data Analysis
Due diligence

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RECOMMENDATIONS
The analysis chain requires an optimistic answers to questions arising from Business
Intelligence, explanatory analytics, predictive analytics, optimization and simulations. To cover
the gaps in retail industry like risk management, recommendations, data from RSS, news,
competitive analysis, attending on personalization requests, seamless integration, bifurcation of
loyalty points, etc. needs to be tapped by creating a dashboards with appropriate data, right
information to right people, Omni-channel campaign execution, predictive intelligence, analyzing
response, following with right actions, real time measurement, segment customers dynamically,
comprehensive reporting of loyal points both online and offline, providing a 360 view of
customers, measuring the success of the campaign execution.
Working towards community management and introducing the concept of human
connection is far more important if the retail industry is concentrating on long term goals rather
than achieving short term financial goals. Predicting exact human behavior with right predictions
shall help in accurate prediction of human behavior and shall help in developing more accurate
results when it comes to predictive analysis by using Bayesian Estimation to reduce the sampling
error.
In case of customers, concentrating on micro –segmentation, plotting of zone marketing,
response modelling and ROI analysis for customer acquisition. Cross – selling, campaign selling,
profitability analysis, loyalty analysis, cohort analysis and credit line improvement for customer
management. Selective retention, churn prediction, plotting customer life cycle, win back for
customer retention.
When it comes to supply chain management, a system to reduce the “Bullwhip effect”
which would prove useful to both the retailers and the suppliers.
Hence talking about the gaps overall where ORKASH can apply logistics and tap the market in
the following, by exploring human connection and human behavior, tapping the customer life cycle
from acquisition to retention, tapping the supply chain, tapping the pricing strategy, introducing
the concept of “on-the-spot-sales” and also extrapolating the concept of “Thanksgiving”.
The final recommendation would be if the data sources would be from CRM, products,
loyalty cards, human connection and social media. Then the analysis could be done to get the
information like best mode of communication, Human behavior, supply chain efficiency and
competitive intelligence. The final value proposition would be areas like Pricing, Risk
management, Micro-segmentation and ROI analysis.

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LIMITATIONS
Understanding the existing market was easier, but getting in touch with the industrial
personnel was a bit of challenge. Some were easily available and even showed keen interest in
taking up the interviews, but some were equally reluctant and in doing so.
Halo effect was observed for most of the answers were the individuals preferred playing
safe while answering the questions. Because of which drawing an immediate conclusion was
difficult and zeroing on any particular point would also have led to central tendency effect.
While working on some particular data with regards to segmentation of the analytics tool
market, it was difficult to get the data with respect to the segmentation of all the analytics tools in
the retail market. For instance, data like due – diligence, risk management, social media analytics,
and news analytics – the data available through the websites or the sample tools were not distinct.
Getting the details about the ways of predicting human behavior was a bit of challenge as there
were no concrete details available regarding the same. Also the ways of the database formed by
the team of TDS with respect to their Human connection campaign of #thankyou.

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CONCLUSION
Unmet needs of the retail industry in case of analytical tools which can be implemented by
collecting the data from the data sources like Customer Relation Management (CRM), best
products, loyalty cards, human connection, social media and open source intelligence. From the
data collected from the above data sources, analysis can be done on the following such as best
mode of communication, human behavior, increasing supply chain efficiency and competitive
intelligence.
From the above analysis obtained, we can go ahead with value propositions such as pricing
strategy, risk management as well as the competitive analysis, micro-segmentation of the market
as well as the customers to target them accordingly and the result of all the above to increase and
maintain the ROI analysis.

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BIBLIOGRAPHY
Nitin Atroley & Rajat Wahey (2014) Indian Retail – The next growth story. Retrieved from
https://www.kpmg.com/IN/en/IssuesAndInsights/ArticlesPublications/Documents/BBG-
Retail.pdf
Indian Brand Equity Foundation (August 2015). Retail Industry. Retrieved from
http://www.ibef.org/industry/retail-india.aspx
Ben Carter – Whitney (November 27th, 2014). The Wild World of Omni channel Retail. Retrieved
from http://www.nextopia.com/blog/2014/11/the-wild-world-of-omnichannel-retail/
Jasmine Desai (May 8th, 2013). Big Data: Inside in-memory analytics. Retrieved from
http://archivecomputer.financialexpress.com/features/1292-big-data-inside-in-memory-analytics
ET-Retail White paper (March 2015). E-Tailing Market in India. Retrieved from
http://retail.economictimes.indiatimes.com/etanalytics/reports/E-commerce/E-Tailing-Market-in-
India/81
ET-Retail Social Analytics (September 16th, 2015). Compare FMCG Segment by People talking
about on FB. Retrieved from http://retail.economictimes.indiatimes.com/social-
analytics/FMCG/Dabur/464
Jeff Kelly (February 12th, 2015). Big Data Market Size and Vendor Revenues. Retrieved from
http://wikibon.org/wiki/v/Big_Data_Market_Size_and_Vendor_Revenues
NCR RealPOS™ Dynakey. Retrieved from http://www.ncr.com/retail/department-specialty-
retail/assisted-service-hardware/realpos-peripherals/realpos-dynakey
George Lawrie (July 31st, 2015). The Forrester Wave™: Point Of Service, Q3 2015. The 10
Providers That Matter Most And How They Stack Up. Retrieved from
http://www.forrester.com/pimages/rws/reprints/document/116461/oid/1-VN8IH6
I-Retail. Polaris ESL. Retrieved from http://www.polarisesl.com/downloads/Brochure- i-
Retail.pdf
Gary Vaynerchuk (July 28, 2014). Go Big on Community Management! Retrieved from
http://www.slideshare.net/vaynerchuk/140722-human-sideoncm
Dorian Stone and Joel Maynes (February 2014). For customer loyalty, only the best will do.
Retrieved from http://www.mckinseyonmarketingandsales.com/for-customer-loyalty-only-the-
best-will-do
David Edelman (October 2014). The Big Data blunder: Missing personal connections. Retrieved
from http://www.mckinseyonmarketingandsales.com/the-big-data-blunder-missing-personal-
connections

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Sheldon Lyn, Maggie Lu, and Ryan Murphy (May 2015). Three steps to unlock growth with
smarter pricing. Retrieved from http://www.mckinseyonmarketingandsales.com/three-steps-to-
unlock-growth-with-smarter-pricing
Jayne Eastman (April 2015). Why your marketing planning process is broken, and what to do
about it. Retrieved from http://www.mckinseyonmarketingandsales.com/why-your-marketing-
process-is-broken-and-what-to-do-about-it
Maher Masri, Dianne Esber, Hugo Sarrazin, and Marc Singer (July 2015). Social care in the world
of "now". Retrieved from http://www.mckinseyonmarketingandsales.com/social-care-in-the-
world-of-now
Emily Bailey, Nate Srinivas and Barry Fischer (June 6th,2013). Want to predict human behaviour?
Use these 6 lessons based on data from 10 million households. Retrieved from
http://blog.opower.com/2013/06/want-to-predict-human-behavior-use-these-6-lessons-based-on-
data-from-10-million-households/
Albert-László Barabási (February 23, 2010). Human behaviour is 93 percent predictable, research
shows. Retrieved from http://phys.org/news/2010-02-human-behavior-percent.html
Amit Shah (September 8th, 2015). Can Big Data Improve Customer Satisfaction? Retrieved from
https://www.linkedin.com/pulse/can-big-data-improve-customer-satisfaction-amit-shah?trk=hp-
feed-article-title-channel-add
Andrea Tosin (June 28th, 2013). Predicting the Unpredictable – Human Behaviours and Beyond.
Retrieved from http://mpe.dimacs.rutgers.edu/2013/06/28/predicting-the-unpredictable-human-
behaviors-and-beyond/
Seung Ho Lee (2009). Integrated Human decision behaviour modelling under an extended belief-
desire-intention framework. Retrieved from
http://arizona.openrepository.com/arizona/bitstream/10150/193788/1/azu_etd_10606_sip1_m.pd
f
La Monica Everett-Haynes (May 28, 2008). Award-Winning Researchers Work to Predict Human
Behaviour and Emotions. Retrieved from http://uaatwork.arizona.edu/lqp/award-winning-
researchers-work-predict-human-behavior-and-emotions
Theresa from Jackson, NJ. (October 3, 2014). Statistics in Human Behaviour
https://www.wyzant.com/resources/answers/52263/statistics_in_human_behavior

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APPENDIX
Structured Questionnaire:
1. What are the analytics tool used by your company?
2. How do you keep a track of range of categories of products?
a. How do you measure the frequency or size of the products?
b. What is the optimal stock level you keep of products and how do you trace them?
3. How do you decide the best range of the products in your stock?
a. How do you price the brand portfolios without affecting the market shares?
b. On what basis do you plan the promotions of the products?
c. According to you, what are the most suitable promotional mechanism?
d. How do you trace the responses of the new products and promotions on different
customers?
4. How do you distinguish your most profitable consumers?
a. How do you trace their needs?
b. What are the strategies used to increase their basket size?
c. Do you go ahead with cross - selling opportunities? On what basis?
5. How do you deal with the recommendations and personalization requests from the
customers?
6. Are you planning to open any new stores in the area?
a. On what basis is your decision on selecting the right location for new store
development? And what would be the format of the store?
b. How would you trace the performance of each store on various parameters?
c. How are you planning for forecasting of sales and demands?
d. How are you measuring your workforce optimization?
Unstructured Questionnaire:
1. Which sector do you work in?
2. What kind of customers your sector deal with?
3. What kind of platform you use to store your data?
4. What medium do use to find the analysis or to interpret the data?
5. Does your department handle the same or is it been outsourced?
6. How frequently do you/ your department use that platform for fetching the data?
7. What are the tools/ techniques used for analyzing the bulk of data?
8. How comfortable you are using the tools/ techniques?
9. What are your expectations from the tools/ techniques you use for your analysis?
10. What are the other areas where you expect your tool to be useful?
11. How satisfied are you with the analysis/ interpretation given by the techniques used by
you?
12. To what extent do you think if more valuable data added to your existing analysis will
help?
13. What additional values do you think should be added to your existing technology/
technique used?

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THANK YOU!