National Journal of Multidisciplinary Research and Development

ISSN: 2455-9040
Impact Factor: RJIF 5.22
www.nationaljournals.com
Volume 2; Issue 3; September 2017; Page No. 484-488

Role of big data in retail customer-centric marketing

1
Dr. Poonam Chauhan, 2 Aditya Mahajan, 3 Dhiraj Lohare
1
Assistant Professor, Department of Marketing, K.J Somaiya Institute of Management Studies and Research, Vidyavihar,
Mumbai, Maharashtra, India
2, 3
Student, K.J. Somaiya Institute of Management Studies and Research, Vidyanagar, Mumbai, Maharashtra, India

Abstract
All businesses today is inundated with data, be it transportation, healthcare, manufacturing, food or retail. They are struggling with
managing multiple big data sources. Combining these types of disparate data sources is the next evolution in business intelligence.
It should have ability to forecast and deliver predictive insights from large, varied and rapidly changing data sets. However,
retrieving meaningful message from the vast data is not easy. Most companies have built an effective data collection system, but
very few of them possess the capabilities to retrieve the powerful information from the large scale data even after applying some of
the most advanced commercial data analysis systems.
The retail industry has experienced some significant challenges and opportunities in recent years. One such challenge is
proliferation of digital media that assists makes customers test and compare products in one store and then buy them elsewhere,
this phenomenon is popularly known as ‘showrooming’. Maintaining customer acquisition, retention and loyalty is another
demanding task. As against this there is incredible opportunity due to penetration and usage of mobile and internet technology.
Global competition has forced retailers to engage in real time analysis of enormous data that is generated on a daily basis through
various consumer interactions. The use of big data analytics, and reporting can generate insights that will improve the profitability
for retailers.
The purpose of the paper is to provide overview of big data analytics in various stages of retail process - tracking emerging popular
products, foretelling of sales and future demand through predictive simulation, explore its application in Price optimization,
targeted promotions and overall enhancement in customer experience and suggest future developments in marketing, consumer
profiling and predictive analytics for retail business.

Keywords: customer experience, price optimization, consumer profiling, customer loyalty, predictive analytics

Introduction records, e-commerce transactions, social media, etc. There are

Retailers face a fragmenting audience as new channels emerge certain sectors that are early adopters of Big Data and
and fight for the attention of consumers. “Big Data” is Analytics and expect to be big beneficiaries of advancing
industry’s hottest buzzword (Waller and Fawcett, 2013) [18]. It solutions. Retailers have long collected massive amount of
is looked upon as a solution to many of the new age retail data that include point-of-sale transactional details,
challenges but, there is virtually no uniformity in defining Big demographic information and ever-increasing volumes of
Data, identifying its purpose, or establishing its role in retail unstructured data generated from blogs and social media. Now
management. Categorizing, assessing quality, and identifying retailers of all sizes are leveraging business analytics to
Big Data’s impact are very new to management in general predict what the customer prefers and how the customer
(George et al., 2014) [10]. behaves.
In retail industry massive data that is generated on a daily Customer Centricity seeks to illustrate the difference between
basis through various consumer interactions. Retailers are customer service and customer focus, most firms simply seek
exploring analytics to gain a unified picture of their customers and placate any customer, when, in fact, they should be
and operations across store or online channel and make seeking only the most profitable ones. (James E. Harris, 2012)
[11]
strategic decisions in hours versus weeks. Large volumes of . The goal of the Customer Centricity is to enable the entire
unstructured and structured data from a variety of sources organization to become closer to their customers by
contain valuable insights about customer behavior, which has establishing proven "best practices" designed to build
the ability to contribute to the growth of retail businesses. customer loyalty and retention. (Business/Technology Editors,
(Frost & Sullivan, 2014) [9]. 1999) [5].
Another characteristic of Big Data is that it contains structured
and unstructured data, with more emphasis on unstructured Research Questions
data such as data collected from social media, devices, sensors Based on preceding discussion following questions are offered
and such. Sources of Big data include web logs, CRM and for examination:
ERP systems, customer transactions, videos, audios, medical Q1.How does Big Data influence customer profiling?

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Q2.Which factors are contributing to Big Data application in improve conversion, developing a new offer or building a new
customer profiling? marketing campaign. It facilitates tracking emerging popular
Q3. Does Big Data in retail customer profiling facilitates Sales products, foretelling of sales and future demand through
forecast, Price optimization, and Targeted promotions? predictive simulation, explore its application in Price
Q4. Does Big Data enabled retail customer profiling enhances optimization, targeted promotions and overall enhancement in
customer experience and loyalty? customer experience. (Marketing Week, 2012) [2]. Most Fast
moving consumer goods (FMCG) companies use big data to
Methodology analyse product purchases and competition, A.C. Nielsen
The analysis is based on secondary sources of data primarily integrated TV and PC usage behaviour with the scanner data,
scholarly articles, news articles and survey reports and social thus widening the scope of consumer-level information.
media. Reports were referred for background study are ‘Big (Agarwal D, 2014). Much of the increase in data quality
Data: Relevance for Retailing’ from TATA Consultancy comes from better data compression, transformations, and
Services by Nathasamy S and Marimuthu R. ‘Harnessing the processing prior to analysis. Traditionally theory-agnostic
Power of Big Data: Big Opportunities for Retailer to Win predictive analytics tools are likely to have larger impact and
Customers’ from Infosys Ltd., by Srinivasan N and Nayar R, lesser bias if they are able to smartly combine theoretical
‘Seeking the Potential of Big Data’ from McKinsey Quarterly insights (akin to using subjective prior information in
by Bughin J and Livingstone J and Marwaha S. Bayesian analysis) with large troves of data. (Bradlow et al,
The researchers interviewed experts in the field for their 2017) [3].
insights on usage and implications of Big Data in making
retail customer centric. Retail customer profiling, customer experience and loyalty
Carbone and Haeckel, (1994) [6] defined Customer experiences
Review of Literature as the ‘takeaway’ impression formed by people's encounters
Big Data and Customer Profiling with products, services, and businesses – a perception
The era of big data emerged when cost of storing data fell produced when humans consolidate sensory information.
below the cost of deleting it. As customer interactions are We now have detailed digital records of what happens in
increasingly taking place in various digital platforms, where many different types of purchase environments. Data sources
all actions can be recorded, it becomes a rich source of data. include cameras in stores, mobile purchase activities on
According to Erevelles et al., (2015), “Consumers have branded apps, scanners at checkout, direct marketing purchase
become an incessant generator of both structured, responses online, online browsing and shopping carts, loyalty
transactional data as well as contemporary unstructured programs, 800 numbers and digital TV, among others. The
behavioral data”. The ability to track new customers and retailers and with their offers creates customer experiences. It
create linkages in transactions is important in retailing. As could be pleasant or unpleasant depending on customer
retailers become larger and more diverse, the type of data that perception and receptivity.
is managed becomes more complex. According to Bagdare S. and Jain R (2013) [1], “Contemporary
Analysis of consumer’s purchase pattern from each and every retailing engages the customers by carefully crafting and
transaction in a retail store is used for developing strategy for delivering experiential benefits to their shoppers. The
placement and promotion of products to improve customer dimensions of retail customer experience incorporate elements
satisfaction and sales revenue for the retailers. (Verma et al., of cognitive, emotional, sensorial and behavioural dimension
2015) [17]. to express customers' responses towards retail store
The short term goals in big data analytics market should aim operations.”
at building technology foundation and developing customer Customer’s satisfaction, loyalty and ultimately the firms
base for sustainable revenue generation. The medium term profitability depends upon the delivery of superior customer
goals should aim at supporting delivery schedules and experience. It is the central concern in retail management.
strengthening customer bond with high quality output. (Palem, (Kumar et al., 2013) [14].
G. 2014) [16]. Customer experience goes beyond the touch points, service
Retailers have to identify the moments when the customer is encounter and practices (Jüttner et al., 2013) [13]. It embraces
most receptive to their influence, it could be his home or when pre and post purchase experiences, as well as past service
he is commuting for work or on the way to a mall or browsing engagements, and their influence on future experience
through window displays at a store. Companies have to focus expectations. (Zomerdijk, L.G. and Voss, C.A., 2010) [9].
on relating with their customers in situations where their Retailers frequently concentrate on improving customer
communication can be most relevant. (Joseph L. Gagnon, loyalty through customer orientated sales strategies, and
Julian J. Chu, 2005) [12]. research by and large reflects that customer satisfaction is
In retail markets – knowledge of customer demography vis-à- precursor to customer loyalty (Mittal and Kamakura, 2001)
[15]
vis purchase patterns and trends can be utilised for planning . The profitability is enhanced due to increased loyalty
and executing more relevant and targeted customer which in turn is impacted by increased satisfaction. (Cronin,
communications and promotions as well as improving supply J.J. Jr, Brady, M.K. and Hult, G.T.M., 2000) [7].
chain effectiveness. Researcher proposes following model based on their
Data-based decision-making unleashes creativity in trying to secondary findings:

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Fig 1: Big Data impact on Customer Profiling

Expert Views Choosing right technology, Collection of data and correlating

Saikat Chaudhary (Vice President -Retail Analytics it, Processing and transforming it properly, Identification of
Delivery at Accenture) shared that the same customer at different platforms.
“Big Data makes a marked difference in creating customer It helps to customise a specific offering best suited for the
profile as compared to the past. A typical marketer brings data customer's needs, simply speaking, consumer profiling leads
from all platforms to create a complete profile. An example of to targeted promotion which in turn leads to price optimisation
how customer profile is created from big data is given below. and this in turn goes as an input to demand forecast
 You can analyse purchase behaviour in terms of purchase From transactional profiling, you know a customer has a
history, credit and return history available.- reference price of $10 for a product.so you optimise price for
TRANSACTION PROFILE that product using elasticity calculations and that goes as an
 Hit/page view/Click path/duration of stay - WEB input to your multivariate forecasting. So these are related.
BEHAVIOR
 SMS/Geolocation analysis- MOBILE ACTIVITY Naimesh Tungare (Assistant VP at Trent-TATA Group)
 open rates/spam complaints/bounce- EMAIL BEHAVIOR He shared the for Big data analytics is growing both in online
 Age/Sex/location/other info-DEMOGRAPHICS and offline areas. There are minimum four people dedicated
 Followers/Posts/Influencers etc- SOCIAL MEDIA for Big Data Analytics for one particular store. Continuous
 Subscriptions/language- PERSONALIZATION data is collected from online and offline mediums like
surveys, reviews, comments, ratings, salesperson interaction,
You then proceed to understanding the data and mining it calls etc. The interrelationship can be explained by following
properly, Applying data science properly to get real insights, figure:

Fig 2

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Managerial Implications decision making. This would lead to enhanced customer

Retail gets the privilege for direct customer interactions, but experience which in turn yields higher conversion rates and
they also generate massive amounts of data. This information loyalty. Though initial impetus has been on cost saving, it
holds the potential to drive real frontline differentiation, if needs to become more holistic. It should aim to deliver value
retailers have the right tools and approaches to make the most to all stakeholders.
of this unique asset.
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