ISTANBUL TECHNICAL UNIVERSITY - SOCIAL SCIENCES INSTITUTE

BIG DATA IN RETAIL

Business Administration and Technology Management

Consultant: Asst. Prof. Gülgün Karakutlu

February, 2016

Melda NALCI 417141026

Özgür ADIYAMAN 417151030

Kadir ŞAHİN 417151024

Abstract

Growing retail business faces different range of challenges including managing the

company’s valuable and vulnerable Big Data. The Big Data is being used in most of areas in

retail industry. It will be the fossil oil of next centuries and only the companies can process

this treasure are going to survive. This mass data creates both opportunities and challenges

for those who analyze it to gain a competitive advantage. In this paper, we are going to

examine the usage of big data in retail sector and its consequences.

Keywords: Big Data, Retail Industry, Data Analytics and Competitiveness

1. Introduction

There is no upper limit for modernization so the companies will continue to optimize

their production, workforce and consumption data as long as customers exist. People

distribute data when they are shopping, surfing on the Internet even speaking on the

phone then this mass data becomes to grow quickly.

We started to hear "big data" word as of 2010s. In recent days, most retail

professionals talk and discuss about it. Big data is often described as a collection of

large and complex data sets which are difficult to capture, store, manage and analyze

effectively using current database management softwares and concepts (Fan and

Bifet, 2013; Kaisler et al., 2013).

A footprint of data follows products because they are manufactured, stocked, shipped,

advertised, consumed and talked about by consumers; all of that can help forward

thinking retailers to increase their sales and operations performance. This requires a

retail analytics resolution capable of analysing large data sets populated by retail

sensors, enterprise resource planning systems, inventory control, social media, and

other sources.

In the retail industry, retailers can use big data analytics to gain new insights about

customer behavior and to improve their decision-making (Russom, 2011; Singh and

Singh, 2012). Analytic methods can be applied to improve retailers' operational

processes such as inventory management, price optimization, floor layout and product

range strategies (Manyika et al., 2011). With correct analytics platforms in place, big

data helps companies to recognize their target customers and they can take rational

actions with these information.

The next sections review the current literature on big data analytics in retail by

picturing the methodologies used, illustrating sample practices, finally closing with a

conclusion through discussion on findings of the study.

2. Literature Survey

Big data usage creates lots of business value in retail industry; such as inventory

management, learning customer purchase patterns, focusing right customers segment,

building core competitiveness, offering customized products, improving the

effectiveness of marketing campaigns in distributions and operations. Today, retailers

may know what customers want; but with Big Data, they will predict what customers

want (van Zanten, 2012).

In order to achieve such benefits, various data refine models have been proposed over

the years, such as;

• Product Pipeline Tracking: When inventory levels are out of sync with

demand, make recommendations to retail buyers to remedy the situation and

maximize return for the store.

• Market Basket Analysis: When an item goes on sale, let retailers know about

adjacent products that benefit from sales increase as well, then develop

appropriate marketing strategies.

• Social Media Analysis: Prior to products going viral on social media, suggest

retail buyers increase order size to be more responsive to shifting consumer

demands and avoid out-of-stocks.

We derive these data from different sources, like orders, sales, social media, surveys

and company’s own databases.

Shema-1 Data Sources

The value of big data analytics is the new insights that can be obtained from

analyzing big data sets in order to drive decision-making (Fan and Bifet, 2013;

Kaisler et al., 2013; Russom, 2011). Retailers can use these insights to optimize

processes along the value chain (Manyika et al., 2011; Mohamed et al., 2012)

What is more, we have a chance to observe all the electronic records of people’s or

groups’ activities by dataveillance; through usage of their credit cards, mobile phones,

email and the Internet by means of personal data systems in order to regulate or

govern their behavior. Dataveillance specifically indicates the ability of reorienting

individuals’ future behavior by means of four classes of actions: ‘recorded

observation’; ‘identification and tracking’; ‘analytical intervention’; and ‘behavioral

manipulation’. It is particularly focused on the third category; analytical intervention.

Shema-2 Dataveillance

Retail professionals use different analytics methods; such as forecasting, data mining

and clustering.

In a research observing forecasting method; Walmart used its last three years

historical data to forecast sales of the next 39 weeks. The strategy includes the

collection of huge data of sales and then it is transferred on HDFS (Hadoop

distributed file system) and map reduced is performed on this to arrey data. In the

map reduce method, data is focused rather than algorithms. The data processed by

map reduce becomes understandable format, but still too large to draw conclusions.

That’s why the Hive module is necessary to be done by loading the data sets. After

that, R-programming is used by the statisticians and data miners to develop the

statistical software and data analysis. Finally, the Holt winters algorithm is used to

predict the sales.

Shema-3 Map Reducing

In another research, clustering of retail customers is examined by means of Kohonen

networks. This method is chosen because it’s an ability to work on large data and to

find out the number of clusters which is the most important decision of a cluster

analysis. The aim of cluster analysis is to provide strategic information to the retailer

in order to help in its market segmentation and target market selection decisions while

discovering previously unknown critical customer attributes and their importance.

This study reveals the importance of customer targets and critical customer

specialties. It helps companies to choose right market and segment for sales.

Companies also need to get help from managerial support systems to analyze data

carefully with right model and multidimensional way. One of these managerial

support systems tools is data mining. It can provide superiority to companies by

revealing core information about their customers and business.

Shema-4 Data Mining

Williams-Sonoma has a database of 60 million households containing variables as

income, number of children and house value (Saniuta, Roman, Al.Pop, 2013). E-

mails’ contents are classified under senders’ age, gender, online activity and inventory

data, then processed in real time with the help of a software. Through the

implementation of this program, the conversion rate of e-Marketing campaign

responses was 30% (van Zanten et al, 2012).

On the other hand, using big data in retail have some challenges like implementation

costs and lack of structure. Refining massive data can be an expensive process and

companies need to use right method for optimization. They also need to explore

obvious use-cases in order to justify the implementation costs. Although most of

countries understand the importance of big data, for some countries usage is not

common, like South Africa. While research has been conducted, it is seen there is an

apparent lack of studies, regarding industry-specific usage in South Africa.

3. Conclusion We have entered an era of big data. Through the right analysis, this large volume of

data can shed light on shopper behaviours. But firstly, companies need to know how

they filter this mass data and which method they need to use to get the right answer.

Most of data projects come from basic business questions, such as; which customers

really like our brand, which product they are interested in, which line they use in the

store and how we can increase our sales and profit. Through big data analytics,

retailers transform tacit knowledge into explicit, thus they can learn more about their

customers, find right market for them, enhance satisfaction and loyalty,

correspondingly perform risk management and inventory management, increase sales

and company margin.

In this study, we found that different models are being used, such as market basket

analysis, social media analysis and invoice analysis. We also saw there are different

methods that professionals use, such as forecasting, clustering and data mining.

According to our findings, retailers who are processing their big data feel confident

during to take decisions because they have a chance to be game changer in the market

through taking advantage of market information value. However, some of challenges

like implementation costs and lack of structure must be solved before this potential

being realized fully. Without careful and detailed tests, the credibility of new data sets

is undermined by faulty conclusions from unverified sources. The target is to show

and implement methodologies for examining diverse sources to evaluate their

strengths and weaknesses. Wider point is that new data sets bring about new risks as

well as the new opportunities.

It is also a perfect use-case in the application of big data to reveal new earning money

opportunities. Location-based services merge customer engagement data with real-

time analytics to provide a more compelling customer experience via their mobile

devices. This technique can be used to maximize profitability and efficiency of the

organization.

The majority of publications around big data analytics are centered on technical

algorithms or systems development. Therefore, our paper’s primary focus has been on

analytics and oft-neglected dimensions of big data. Future researchs should consider

how these perceptions influence decision-making, and how this affects the future

usage of big data analytics in the retail industry. Although major innovations in

analytical techniques for big data have not yet taken place, one anticipates the

emerging technology in the near future. For instance, real-time analytics will likely

become a productive field of research because of the growth in location-aware social

media and mobile applications.

We believe that big data is either already delivering a competitive advantage for

leading retailers, or will be doing so in the next one to five years.

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