A Case Study of Nakumatt Holdings Limited

STRATEGIC ROLE OF BIG DATA ANALYTICS ON THE COMPETITIVE ADVANTAGE OF SUPERMARKET CHAINS: A CASE STUDY OF NAKUMATT HOLDINGS LIMITED

KENNETH PATRICK OGWANG

UNITED STATES INTERNATIONAL UNIVERSITY – AFRICA

FALL 2016 STRATEGIC ROLE OF BIG DATA ANALYTICS ON THE COMPETITIVE ADVANTAGE OF SUPERMARKET CHAINS: A CASE STUDY OF NAKUMATT HOLDINGS LIMITED

KENNETH PATRICK OGWANG

A Research Project Report Submitted to the Chandaria School of Business in Partial Fulfilment of the Requirement for the Degree of Masters in Business Administration (MBA)

UNITED STATES INTERNATIONAL UNIVERSITY – AFRICA

FALL 2016 STUDENT’S DECLARATION

I, the undersigned, declare this my original work and has not been submitted to any other college, institution or university other than United States University in Nairobi for academic credit.

Signed ______Date: ______

Kenneth Patrick Ogwang (645395)

This project report has been presented for examination with my approval as the appointed supervisor.

Signed ______Date: ______

Dr. Paul Katuse

Signed: ______Date: ______

Dean Chandaria School of Business

ALL RIGHTS RESERVED. Any unauthorized reprint or use of this research report is prohibited. No part of study may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system without express written permission from the author and the university.

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ABSTRACT

The main aim of this study was to establish the role of Big Data Analytics (BDA) on the competitive advantage of supermarket chains in Kenya. A case study was done on Nakumatt Holdings Limited. The study was guided by three research questions and these are; how BDA of customer trends and patterns impact differentiation within Nakumatt, how use of Nakumatt loyalty cards increase customer repetitive buying and lastly how use of Big Data Analytics creates cost leadership over Nakumatt’s competitors.

This research focussed on the population of Nakumatt managers, employees and customers in Nairobi. In this study, the sampling frame involved the management staff at Nakumatt as well as the customers that are loyalty card holders from all the 23 branches in Nairobi. Random sampling was used for customers as they exited the branches. Ultimately, we spoke to every 5th customer exiting the supermarket after completion of an interview.

This study adopted the Probability based technique for sampling. Under this, stratified random sampling Technique was adopted for the collection of data about the population from the entire population. The Strata was the Senior Management, Employees in Operations and the Nakumatt Loyalty Card Holders. The data collected through this method gave the researcher the opportunity for an intensive study about the problem area. To obtain the minimum population sample for this study, the researcher used the rule of thumb and therefore targetting all the 23 branches in Nairobi, and the top management at Nakumatt in charge of operations department.

Questionnaires were used to collect data for this research and collected data was analysed using Statistical Package for Social Sciences (SPSS) program and analysis presented in tables, and figures in chapter four to give a clear picture of the research findings.

ACKNOWLEDGEMENT

The submission of this work is the culmination of a long hard fought journey, the idea of which was borne many years ago. Following on the saying that ‘A journey of a thousand miles starts with one step”, I am thankful to many people for having enabled me make that first step. I would not have been able to achieve this success without the splendid support of a number of important persons, who in their own way, directly and indirectly contributed to it. First and foremost I sincerely thank God Almighty who gave me the grace and strength to persevere this far. I would like to thank my supervisor for inspiring me every step of the way. His tireless and selfless effort in advising, correcting and mentoring me gave the much needed push to keep me focused on my goal. I thank all my friends, classmates and office colleagues who encouraged and inspired me to the very end with their timely advice and invaluable support. I acknowledge the USIU administration for the opportunity to further my education; to them I will ever be indebted. To my family, yours has been a sacrifice that only God will be able to repay. For the encouragement and creation of an enabling environment, without your support I would not have been able to make it. Special thanks to my Dad and Mum (both deceased) who laid a firm foundation for me and taught me to pursue big dreams.

TABLE OF CONTENTS STUDENT’S DECLARATION ...... ii COPYRIGHT ...... iii ABSTRACT ...... iv ACKNOWLEDGEMENT ...... v LIST OF TABLES ...... viii LIST OF FIGURES ...... ix LIST OF ABBREVIATIONS ...... x

CHAPTER ONE ...... 1 1.0 INTRODUCTION...... 1 1.1 Background of the Problem ...... 1 1.2 Statement of the Problem ...... 5 1.3 Purpose of the Study ...... 7 1.4 Research Questions ...... 7 1.5 Significance of the Study ...... 7 1.6 Scope of the Study ...... 8 1.7 Definition of Terms...... 8 1.8 Chapter Summary ...... 9

CHAPTER TWO ...... 10 2.0 LITERATURE REVIEW ...... 10 2.1 Introduction ...... 10 2.2 Use of Customer Trends data to create differentiation in Nakumatt ...... 10 2.3 Use of loyalty cards in driving repetitive purchase amongst Supermarket Customers 13 2.4 Use of BDA in Creating Cost Leadership within Supermarket Chains ...... 17 2.5 Chapter Summary ...... 20

CHAPTER THREE ...... 21 3.0 RESEARCH METHODOLOGY ...... 21 3.1 Introduction ...... 21 3.2 Research Design...... 21 3.3 Population and Sampling Design ...... 22

3.4 Data Collection Methods ...... 24 3.5 Research Procedures ...... 25 3.6 Data Analysis Methods ...... 26 3.7 Chapter Summary ...... 27

CHAPTER FOUR ...... 28 4.0 RESULTS AND FINDINGS ...... 28 4.1 Introduction ...... 28 4.2 Demographic Information ...... 28 4.3 To What Extent Does Big Data Analytics Of Customer Trends And Patterns Impact Differentiation Within Nakumatt? ...... 31 4.4 How Does Use Of Nakumatt’s Loyalty Cards Increase Customer Repetitive Buying? ...... 35 4.5 How does use of Big Data Analytics create cost leadership over Nakumatt’s competitors? ...... 41 4.6 Chapter Summary ...... 44

CHAPTER FIVE ...... 45 5.0 DISCUSSION, CONCLUSION AND RECOMMENDATIONS ...... 45 5.1 Introduction ...... 45 5.2 Summary of Findings ...... 45 5.3 Discussion ...... 47 5.4 Conclusions ...... 51 5.5 Recommendations ...... 52

REFERENCES: ...... 54 APPENDICES ...... 57 APPENDIX 1: LETTER OF INTRODUCTION ...... 57 APPENDIX II: RESEARCH QUESTIONNAIRE ...... 58 APPENDIXIII: ANOVA ...... 64

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LIST OF TABLES

Table 3.1: Listing of Nakumatt branches in Nairobi County...... 23 Table 3.2: Population size ...... 24 Table 3.3: Sample Distribution Table Using Rule of Thumb ...... 24 Table 4.1: Gender...... 29 Table 4.2: Level of Education ...... 30 Table 4.3: Ease of getting products at Nakumatt ...... 32 Table 4.4: Satisfaction with the quality of products at Nakumatt ...... 33 Table 4.5: Satisfaction with variety of products at Nakumatt ...... 33 Table 4.6: Satisfaction with ease of getting products at Nakumatt ...... 34 Table 4.7: Satisfaction with quality of goods at Nakumatt ...... 34 Table 4.8: Satisfaction with variety of products at Nakumatt ...... 35 Table 4.9: Descriptive Statistics ...... 37 Table 4.10: Regular use of Loyalty Card ...... 38 Table 4.11: Ease of Redeeming Loyalty Points ...... 38 Table 4.12: Frequent Shopper at Nakumatt ...... 39 Table 4.13: Regular use of Loyalty Card ...... 39 Table 4.14 Ease of redeeming loyalty points ...... 40 Table 4.15 Frequency of Shopping ...... 40 Table 4.16: Satisfaction with the Pricing of Good at Nakumatt ...... 41 Table 4.17 Use of Big Data Analytics to create cost leadership at Nakumatt ...... 42 Table 4.18: Satisfaction with Pricing of goods at Nakumatt ...... 43 Table 4.19: Correlation between Increased Shoppers Vs Increased Loyalty Card Numbers ………………………………………………………………………………..43

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LIST OF FIGURES Figure 2.1: The four Big Data strategies that a firm may employ in utilizing Big Data ... 11 Figure 2.2: The knowledge Required By Data Scientists ...... 13 Figure 2.3: Evolution of the Consumer Behaviour ...... 15 Figure 2.4: Porter’s Generic Strategies ...... 17 Figure 4.1: Gender ...... 29 Figure 4.2: Age Group ...... 30 Figure 4.3: Sales Data ...... 31 Figure 4.4: Loyalty Data ...... 32 Figure 4.5: Length with Loyalty Card ...... 36

LIST OF ABBREVIATIONS

BDA - Big Data Analytics

CRM - Customer Relationship Management

CHAPTER ONE

1.0 INTRODUCTION

1.1 Background of the Problem

Ittman (2016) reveals that one of the major trends over the last number of years that has affected companies in every sector of the economy has been the realisation for the need to move towards more accurate, data-driven insights to achieve effective decision making. With increased and more sophisticated developments in IT, more powerful computers and the abundance of personal electronic devices, the world has moved into the ‘big data’ era. Data now has the power to help businesses succeed; but this can only be achieved through appropriate and proper analysis, through the use of what is called ‘analytics’ of these big volumes of data (Davenport, 2016). The impact or potential impact of this has been (or could be) widespread in many different sectors across the business including Supply Chain, Information Technology, Human Resource as well as Sales and Marketing.

Kaoutar et al (2014) estimated that by the year twenty twenty (2020), forty three (43) trillion gigabytes of data will have been generated. This will be one thousand (1000) times more than what we currently have. This data is mostly unstructured but represents an immense potential source of information that can be utilized to unlock the competitive edge of an organization if exploited. This data is what is referred to as “Big Data”. “Big data” are data sets that are too big to be handled using the existing database management tools and are emerging in many important applications, such as Internet search, business informatics, social networks, social media, genomics, and meteorology (Prachi et al, 2013).

Marr (2013) a best-selling author and key note speaker, explains that the history of Big Data as a term may be brief, but many of the foundations it is built on were laid long ago. Big Data evolved over period of time from C 18,000 BCE where there were examples of humans storing and analysing data using sticks. A case in point is the Ishango stick in present day Uganda (Court, 2015). Data then grew in leaps and bounds, traversing the information explosion era in 1940s, the start of large data centres in the 1960s and the discovery of the World Wide Web in the early 1990s into the modern era Big Data phenomenon.

Press (2013) indicates that the turning point on Big Data came with the introduction of Internet and the coming into the market of improved and more powerful Personal Computers and mobile phones. He had estimated that by the year 2002, data on the internet would overlap voice data. The internet and improved accessibility of the Internet on Personal Computers and mobile phones spurned the use of social media such as Facebook, Whatsapp, Instagram and other web platforms that have played a big role in information growth explosion currently being experienced. (SAS Analytics Suite of Software, 2013)

Vairavel (2016) describes Fast Moving consumer goods (FMCGs) as products that are oversubscribed quickly and at a low price. These embrace goods like soft drinks, toiletries, over the counter medicine, toys, vegetables etc. He noticed that ever changing tastes of customer plays a significant role. A customer today may prefer coffee and tomorrow prefers tea. Young kids out of campus could prefer sleek portable phones in one year and then look out for bigger, more powerful phones in the next year. Vairavel also mentions that to reach the business pinnacle, one needs to have radical data concerning the merchandise, its competitors and also the dynamic trends within the environment. Richy et al (2014) summarises this by mentioning that the FMCG business has used the ability of big data to realize success and speed up their businesses in numerous aspects.

FMCGS always have to innovate in order to meet the customer requirements however frequent that happens. Rijmenam (2016) gives an example of Amazon’s use of Big Data Analytics to illustrate this. He mentions that Amazon has an unrivalled bank of data on online consumer purchasing behaviour that it can mine from its one hundred and fifty two (152) million customer accounts. Since many years, Amazon uses that data to build a recommender systems that suggest products to people who visit Amazon.com. Already in 2003 they used item-item similarity methods from collaborative filtering, which was at that time state-of-the-art. Gorman (2012) adds onto this and mentions that since then Amazon has evolved and improved its recommender engine and today and has mastered this to perfection. He adds that they use customer click-stream data and historical purchase data of all the 152 million customers and each user is shown customized results on customized web pages. He concludes that this customer touch point makes it easier for customers to return to the site for return purchase and in many instances could lead to a

2 customer purchasing what they didn’t intend to purchase before and hence improving on sales.

Vairavel (2016) urges that Big data helps FMCG companies to be plenty of responsive and responsible towards her customers. By combining consumer data with purchase data, retailers can section their customers in fine detail. They will in addition target their customers with made-to-order promoting and targeting. Davenport (2016) notices that the current challenges featured by the FMCG business include: optimizing the pay, speedy new product innovation, rising sales and promoting effectiveness, integration of varied knowledge supply, analysis of varied situations and visibility of supply chains. Keeping in mind the complexities, FMCG industries want better tools which will facilitate in designing, management, measuring and improve the operational and money performance of the corporate.

Davenport then concludes by re-iterating the point that Business Intelligence and big data analytics incorporate a positive impact on the FMCG industries such as through the printing of distinctive codes on packs that unlocks the large potential big data holds for FMCG brands. As a customer registers and enters codes, it permits brands to gather top quality activity and individual purchase knowledge during a single read. Big data facilitates a lot of intelligent promotions that is key to driving sales. Davenport and Harris (2013) reiterate that Big Data and Business Intelligence helps FMCG companies to identify the most profitable customers and optimize the pricing structure. They add that it is also used to analyse inventory turns to improve sales and predicts customer buying patterns.

Johnson (2012) states that there are number of FMCGs within Africa that are embedding the use of Big Data Analytics. These stretch from Telecommunication companies like Safaricom in Kenya that used Big Data Analytics to determine best price points for the different calling patterns, Soft Beverage Industries like Coca- Cola Beverages as well as Alcohol Industry like East African Breweries that is using Big Data Analytics from its Production floor to optimize production and reduce on downtimes.

The other sector within the FMCGs that is actively embracing Big Data Analytics is the Supermarket chains. Ochieng (2015) reveals that the Kenyan supermarket sector is composed of four main domestic retail chains: Uchumi, Nakumatt, Tusker Mattresses, and the Ukwala Group. However, he adds that there has been increasing number of

3 foreign entries into this space in the form of the French Supermarket firm Carrefour as well as Game. Although this is the case, the market concentration has kept on increasing and several independent supermarkets have also come up. Nairobi, with a population of more than 5 million has many supermarket chains due the growing trend of self service and desire for convenient shopping (Kenyaweb, 2015). Urban centres opportunities have also driven investment in the super market chain with Carrefour setting up in traditionally high class neighbourhoods such as Two Rivers in Runda and The Hub in Karen (Johnson, 2012).

With the intense competition beginning to be felt within the sector, Big Data is an area of opportunity that could help drive the competitive advantage to success as revealed by Gorman (2012). He adds that Big Data use and Analytics is entrenched in most of the medium to large scale supermarket chains but the effectiveness of use of the information derived from the analytics is what remains to be explored. While most retail outlets tend to cherish competition and encourage growth, it is surprising that two third of these firms drop out of the growth curve of the product lifecycle. Gorman also adds that a significant fraction of these progress to maturity and stagnate shortly before crashing down. Most of these firms face this trend because retail business is volatile and there is also increasing competition in major markets due to inadequate contingency planning and incompatible growth retail models (Agarwal & Audretsch, 2012). They add that frequent changes in consumer trends and short business cycles are also some of the challenges in the retail supply chain requiring agile models.

Nakumatt Supermarket Holdings will be the focus of this research proposal. According to (Who We Are, 2016) a publication by Nakumatt, Nakumatt Holdings Limited is a Kenyan supermarket chain owned by Atul Shah. At the end of 2015, Nakumatt had over sixty five (65) stores and spread throughout East Africa in Kenya, Uganda, Tanzania and Rwanda with active plans to expand to the rest of Africa. It employed over five thousand five hundred (5500) people and the financial results indicated a turnover in excess of USD 450m by end of 2015. Johnson (2012) in describing the competitive advantage of Nakumatt, mentions that Nakumatt’s store formats range from supermarkets to hypermarkets that display world-class shopping floor layouts and amenities. The Nakumatt branches offer a range of over fifty thousand (50,000) quality products as well as shopping and entertainment centres for the whole family in most of their hypermarkets (Nakumatt Brochure, 2015). They have also introduced a smart card and a gift card. The

Nakumatt smart card enables customers to accumulate points with their purchases which claim rewards and prizes. The Nakumatt gift voucher is available in any denomination, the value of which the shopper is required to redeem at once.

Johnson (2012) stated that Big Data Analytics is being used in Africa by many institutions as part of the Corporate Strategy to create competitive edge including the regional grocery store, Nakumatt that is already utilising predictive analytics to determine the buying habits of its customers across East Africa, to better target them with products they desire. The loyalty card that earns these customers points as they shop, also stores and shares information on purchasing trends allowing Nakumatt to target and personalise the offers they make.

1.2 Statement of the Problem

Gormin (2012) states that there are four types of big data Business Information that really aid business. These four types are not static but keep on expanding and changing as the business needs evolve. The first type is prescriptive and reveals what actions should be taken. It is the most valuable kind of analysis and usually results in rules and recommendations for next steps. The second type is predictive and is an analysis of likely scenarios of what might happen. The deliverables are usually a predictive forecast. The third is diagnostic and looks at past performance to determine what happened and why. The result of the analysis is often an analytic dashboard. Watson et al (2012) summarizes with the last which is descriptive which as the name suggests describes what is happening now based on incoming data. Watson et al add that to mine the analytics, you typically use a real-time dashboard and/or email reports.

However, big data, like any other commodity, only has real value when it’s refined: in this case combined with enhanced analytics to provide insights that help identify opportunities or develop solutions (Johnson, 2012). Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway (Moore, 2013). Moore continues that therefore Big Data by itself cannot be useful to an organization, it is only when it is utilized to draw out information that an organization benefits. This is what is referred to as “Big Data analytics”.

However it is important to determine how easy it is for companies to mine data and obtain analytics. McKinsey report (2011) predicted a sixty percent (60%) margin increase for

5 retail companies who were able to harvest the power of big data. However, despite the numerous big data benefits that are well documented, McAfee and Brynjolfsson (2012) posed a question on how many organizations across the globe are putting it to use and in which way. They demonstrated that BDA is increasingly emerging as a new technology that increases overall efficiency of management and better decision-making. When compared to traditional analytics system, BDA is able to enhance the productivity and performance of organizations in real-time. Many studies on adoption of BDA have focused on telecommunication (Oghuma, 2013), employment trends, 2012 – 2016 in the UK (Randall et al, 2011) and how companies gain by assimilating Big Data Analytics in full scale and in decision making and for operations (Aggrawal, 2013).

Research from IBM conducted by IDG Connect (2013) on the move towards big data projects across core African markets in Kenya and Nigeria revealed that infrastructure readiness was already very high, yet skills still seem fairly low. The research revealed that for smaller organizations with 100 – 499 employees only a small percentage of 40% were looking to fully outsource big data projects. The research also found out that there was lack of local information on Big Data which certainly needed to be addressed for the technology to be implemented effectively. Another research by Mckinsey (2012) found out that the majority of companies in Africa have barely considered the implication of social media, e-mail and multimedia on the marketing of their products and how they can use the data to communicate with their consumers. Studies on Big Data Analytics in Kenya and specifically on Supermarkets are limited. In spite of these, supermarkets accumulate a lot of data from different sources.

The paradox however is whether with all the accumulation of data that is at their disposal, the supermarkets take advantage of these to improve on internal processes. Previous research would suggest that this big haul of data is not being mined to the extent that is necessary to create a paradigm shift in terms of a Supermarket’s competitive advantage (Deloitte & Material Handling Institute [MHI], 2014). The previous researches that have done have been focused on the benefits of Big Data in use within FMCGs (Ochieng, 2015). However, how these benefits translate into competitive advantage in terms of Cost leadership, differentiation or focus is what is left to be explored.

This study therefore was aimed at filling this knowledge gap by endeavoring to understand how effectively Big Data Analytics is used in Supermarkets and how that

6 relates to a competitive advantage. The research study was aimed at answering the following research questions: a) To what extent has BDA helped Nakumatt create differentiation? b) Has Data Analytics such as predictive forecasting increased customer loyalty through repeated buying? and lastly c) How has use of Big Data Analytics created a competitive advantage over Nakumatt’s competitors?

1.3 Purpose of the Study

The purpose of this study was to investigate the strategic role of Big Data Analytics (BDA) on the competitive advantage of Supermarket chains with the case study being Nakumatt Holdings Limited.

1.4 Research Questions

The study was guided by the following research questions:-

1.4.1. To what extent does Big Data Analytics of customer trends and patterns impact differentiation within Nakumatt?

1.4.2. How does use of Nakumatt loyalty cards increase customer repetitive buying?

1.4.3. How does use of Big Data Analytics create cost leadership over Nakumatt’s competitors?

1.5 Significance of the Study

1.5.1 Company Leadership

This study provides data that assists organizational leaders make informed decisions when implementing Big Data strategies by supplying information on the concerns and satisfaction required for Big Data implementation. The project implementation team and the Nakumatt management will be able to identify how best to use the data at their disposal to make them more competitively placed to challenge her competitors. This study will also be of assistance to the employees because it will be used as a reference during the implementation of other projects in the organization.

1.5.2 Supermarket Chains

This research is also important to Nakumatt Holdings and other Supermarkets because it provides clues on the importance of using Analytics to boost company performance. Big Data use is still a grey area for many companies and this study will help open that up.

1.5.3 Academicians and Future Researchers

This research will contribute to the scant information available on the strategic impact of Big Data in creating competitive advantage in firms. Most the previous research done in Kenya on Big Data has centred more on the use and benefits of BDA within the firms. This therefore will provide extra source of secondary information that can be used by Academicians and Researchers alike.

1.6 Scope of the Study

The study will focus on Nakumatt Supermarket Chain and specifically in Nairobi County. The study will focus on the management team within Nakumatt that is the leadership team as well as lower level management who are in charge of day to day operations. The third category of focus will be Nakumatt customers and this will be sampled from the different Nakumatt branches in Nairobi. The study will be longitudinal over a period of 5 years looking at the trends and performance of Nakumatt holdings Limited.

1.7 Definition of Terms

1.7.1 Big Data

Prachi et al (2013) define Big Data as extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. It usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.

1.7.2 Competitive Advantage

Competitive advantage is a business concept describing attributes that allow an organization to outperform its competitors. These could include access to natural resources, highly skilled personnel, geographic location, and high entry barrier (Porter,

1998). He defined the two types of competitive advantage an organization can achieve relative to its rivals: lower cost or differentiation.

1.7.3 Supermarket Chains

According to Krupnik (2013), a supermarket is a large form of the traditional grocery store, is a self-service shop offering a wide variety of food and household products, organized into aisles. Krupnik notes that it is larger and has a wider selection than a traditional grocery store, but is smaller and more limited in the range of merchandise than a hypermarket or big-box market.

1.8 Chapter Summary

This chapter discusses the background information of Big Data and how that metamorphoses into analytics. The general objective, specific objectives, significance of the study, purpose of the study and definition of key terms used in this research are also discussed. The next chapter will review relevant literature for this research guided by the specific objectives highlighted in chapter one. Chapter three will focus on the research methodology that will be used to guide this study explaining the population under study, sample size and data collection method that will be used to collect from for this research.

CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 Introduction

This chapter discusses literature review of Big Data analytics and its creation of competitive advantage in corporate firms with a focus on Nakumatt Holdings Limited guided by the specific objectives highlighted in chapter one. The objectives include: - to analyse the Big Data strategies used by firms, to examine key success factors in Big Data implementation and to identify if utilization of Big Data Analytics in Nakumatt Holdings has created a competitive edge over her competitors.

2.2 Use of Customer Trends data to create differentiation in Nakumatt

This section will discuss Literature Review on the use of BDA to understand the customer needs and trends and create unique offering from Nakumatt.

2.2.1 Types of Big Data:-

Prescriptive Analysis is really valuable, but largely not used. According to Gartner (2014), thirteen (13) percent of organizations are using predictive but only 3 percent are using prescriptive analytics. Where big data analytics in general sheds light on a subject, prescriptive analytics gives a laser-like focus to answer specific questions. For example, in the health care industry, you can better manage the patient population by using prescriptive analytics to measure the number of patients who are clinically obese, then add filters for factors like diabetes and LDL cholesterol levels to determine where to focus treatment. The same prescriptive model can be applied to almost any industry target group or problem.

Predictive Analytics uses big data to identify past patterns to predict the future (Marr, 2016). For example, some companies are using predictive analytics for sales lead scoring. Some companies have gone one step further use predictive analytics for the entire sales process, analyzing lead source, number of communications, types of communications, social media, documents, CRM data, etc. Properly tuned predictive analytics can be used to support sales, marketing, or for other types of complex forecasts.

Diagnostic Analytics is used for discovery or to determine why something happened (Al Sakran, 2015). For example, for a social media marketing campaign, you can use descriptive analytics to assess the number of posts, mentions, followers, fans, page views, reviews, pins, etc. There can be thousands of online mentions that can be distilled into a single view to see what worked in your past campaigns and what didn’t.

Al Sakran (2015) adds that Descriptive Analytics is also known as data mining and is at the bottom of the big data value chain, but can be valuable for uncovering patterns that offer insight. A simple example of descriptive analytics would be assessing credit risk; using past financial performance to predict a customer’s likely financial performance. Descriptive analytics can be useful in the sales cycle, for example, to categorize customers by their likely product preferences and sales cycle.

2.2.2 Strategies a Firm Uses to Utilize Big Data

Salvatore et al. (2013) redefined the framework of capturing Big Data into 4 quadrants that are illustrated below:-

Figure 2.1:The four Big Data strategies that a firm may employ in utilizing Big Data Hoffman (2013) indicates that performance management involves understanding the meaning of big data in company databases using pre-determined queries and multidimensional analysis. The data used for this analysis are transactional, for example, years of customer purchasing activity, and inventory levels and turnover. He added that

11 managers ought to ask questions such as which are the most profitable customer segments and get answers in real-time that can be used to help make short-term business decisions and longer term plans.

Data exploration involves making use of heavy statistics to experiment and get answers to questions that managers might not have thought of previously (Ularu, 2012). The approach leverages predictive modelling techniques to predict user behaviour based on their previous business transactions and preferences. Chieng, Chang and Storey (2012) suggest that cluster analysis can be used to segment customers into groups based on similar attributes that may not have been on analysts’ radar screens. Chieng et al add that once these groups are discovered, managers can perform targeted actions such as customizing marketing messages, upgrading service, and cross/up-selling to each unique group.

Bologa (2013) adds to the framework to state that Social Analytics measures the vast amount of non-transactional data that exists at present. Bologa continues that much of this data exist on social media platforms, such as conversations and reviews on Facebook, Twitter, and Telegram. Davenport (2016) also delves into this to indicate that social analytics measures three broad categories: awareness, engagement, and word-of-mouth or reach. Davenport continues to state that awareness looks at the exposure or mentions of social content and often involves metrics such as the number of video views and the number of followers or community members. He finalises by mentioning that engagement measures the level of activity and interaction among platform members, such as the frequency of user-generated content.

Ittman (2015) summarises the framework to explain that Decision science involves experiments and analysis of non-transactional data, such as consumer-generated product ideas and product reviews, to improve the decision-making process. He clarifies that unlike social analysers that focus on social analytics to measure known objectives, decision scientists explore social big data as a way to conduct “field research” and to test hypotheses. Gorman (2012) gave an example of crowdsourcing, including idea generation and polling, that enables companies to pose questions to the community about its products and brands. Sathi (2012) summarises this by mentioning that decision scientists, in conjunction with community feedback, determine the value, validity, feasibility and fit of these ideas and eventually report on if/how they plan to put these ideas in action.

This section looked at the ways BDA builds on the information obtained from the customer needs, shopping patterns and trends to help companies react in an appropriate manner by redefining their products to solely suit the customer needs. This aligns with the theory of Differentiation where an organisation has unique offerings that endear the customer to it.

2.3 Use of loyalty cards in driving repetitive purchase amongst Supermarket Customers

This section will look at the literature review on how the use of loyalty cards as an incentive scheme will help drive sales in any organisation or firm. Grossman and Siegel (2014) conceptualized a model for Big Data Analytics based on an organizational framework that seeks to integrate analytics, business knowledge, and information technology as reflected in the figure below. The 3 work have to work in tandem for there to be any realization of success.

Figure 2.2: The knowledge Required by Data Scientists Grossman and Siegel further mention that the framework is based on four main questions: It looks at whether the organization views data and analytics as a key function of the organization, similar to the way that finance, information technology, sales and marketing, product development, etc. are viewed as functions of the organization. It is important that analytics is perceived as valuable to the business units in order for it to be integrated into operations. Siegel (2013) adds that second question is on whether there is

13 a critical mass of data scientists. Siegel explains that without a critical mass of data scientists, there is insufficient domain knowledge to address all the problems of interest. Samuelson (2013) builds onto this with a third question that is whether there are data scientists with sufficiently deep knowledge of the business unit domains. He adds that without such knowledge, it would be difficult to build models that bring value to the business unit. Deep knowledge and complex business problems tend to spawn specialization. Waller (2013) dives into the last question that makes the framework and queries whether there is an adequate analytics governance structure. Waller explains that a governance structure helps stakeholders make decisions that prioritize big data opportunities, obtain the required data, deploy analytical models, and support measurement of the business impact of the models.

Christina (2014) states that the purchasing decision process began to be studied about 300 years ago by Nicholas Bernoulli (in 1783 he introduced the terms of expected utility and marginal utility in the economic theory), followed by John von Neumann and Oskar Morgenstern (they introduced the terms of risk and uncertainty, and in 1944 they published a fundamental article for microeconomics “Theory of Games and Economic Behavior”). They created a mathematical model in order to determine the utility gained after a consumer activity, people being considered pure rational beings (consumers tried only to satisfy self-interest). Hirschowitz (2013) stated that “no matter how sophisticated a company's ability to generate customer insight, it will deliver little value without the processes in place that exploit this understanding to build stronger customer relationships.” Today, the best processes that can create a complex and complete image of what consumers buy, and can also understand why they buy a certain product or service, is Big Data. In an interview for KDnuggets, Schitka (2015), who works on the SAP Big Data Solution Marketing team, said: “Big Data is an opportunity to re-imagine our world, to track new signals that were once impossible, to change the way we experience our communities, our places of work and our personal lives.” So Big Data is the perfect instrument to study today’s consumer behavior. Regarding Big Data, studies reflect that after 2017, this technique of data analysis will be a competitive necessity, so companies need to have started to adapt to the trends in order to survive in the dynamic and digitalized markets.

Figure 2.3: Evolution of the Consumer Behaviour Christina (2014) builds the relationship between consumer behaviour and loyalty. By understanding the consumer, their patterns, needs and wants, an institution is able to tailor a personal experience for the consumer. In doing so, loyalty is then embedded within the consumer as their needs are captured adequately.

Aside of the need for organizational capabilities that has been adequately captured above, (Kumar and Dasl, 2014) introduce the aspect of requisite technology such as Apache Hadoop as the best new approach to unstructured data analytics. Kumar and Dasl explain that Hadoop is an open-source framework that uses a simple programming model to enable distributed processing of large data sets on clusters of computers. Mckinnon (2013) contribute by saying the complete technology stack includes common utilities, a distributed file system, analytics and data storage platforms and an application layer that manages distributed processing, parallel computation, workflow and configuration management. Mckinnon also offers more support by mentioning that in addition to offering high availability, Hadoop is more cost efficient for handling large unstructured data sets than conventional approaches, and it offers massive scalability and speed. The importance of using the right technology in Big Data Analytics cannot be overemphasized as the wrong solution may be akin to “pouring water down the drain” in terms of finance and resources to be used (Cooke, 2013).

The Strategic role of Big Data Analytics in organisations has been expressly explored. However, with the amount of data all around us and growing exponentially, the rate of uptake of Big Data analytics is still low especially in Africa. The potential is clear cut but the practice is not entrenched (Johnson, 2012). It is of interest is in understanding the reasons why Big Data is still not widely used in African organisations apart from a few firms. Acharya (2011) mentioned the following as Key Success Factors in the successful take off in the use of Big Data Analytics as a strategic tool in the business.

2.3.1 Technology and Getting Relevant Data:

Brown et al. (2013) explains that data is all around us and that organisations need to be able to pick on what scope of data will be relevant for its use. Brown et al add that there is need for a fine balance between collecting an inadequate amount of data that does not provide proper analytical value and capturing all kinds of data that puts a strain on organizational storage facilities. In terms of technology, Mckinnon (2013) mentions that the cost of buying software like Hadoop that adequately captures and is able to do analytics, comes at pricey cost for smaller organisations. For companies unable to take advantage of cloud storage, implementing Storage Area Network capable of handling big data is prohibitive.

2.3.2 Skill Levels To Analyse and Manage Big Data Projects

Big Data Analytics requires experienced analysts able to analyse and decipher data to churn out the requisite information (Baesens, 2014). These skills are still scarce and organisations need to build the capability required. The capability to understand the sheer amount and volume of data that is Big Data is at a higher level that what is currently done in organisations (Coles, 2013). Organisations need therefore, he adds, to invest in up skilling of her staff in order to be able to utilise the potential that is Big Data. Siegel (2013) adds that without adequate investment in proper skills and training, any other expense by the company in investing in technology may be a waste as this will not be utilised fully.

2.3.3 Adequate Business Support

Sathi (2012) laments that Big Data is still a new phenomenon and that some organisations are still not sold onto the idea of investing in large unknown unstructured information. Grossman and Siegel (2014) stressed that the organizations should view data and

16 analytics as a key function of the organization, similar to the way that that finance, information technology, sales and marketing, product development are viewed as functions of the organization. This he advises, will help build the profile of the Big Data team and ensure it’s a respected function with the right level of resourcing provided.

2.4 Use of BDA in Creating Cost Leadership within Supermarket Chains

This section will discuss literature review on how BDA can lead to cost leadership within an organization as part of competitive advantage.

2.4.1 Competitive Advantage

Michael Porter (1998) states that competition is at the core of success or failure of firms and that Competitive Strategy is the search for a favourable competitive position in an Industry. He stresses that Competitive Strategy aims at establishing a profitable and sustainable position against the forces that determine Industry. He also described that there are three generic strategies for achieving competitive advantage and these are: Cost Leadership, differentiation and focus.

Figure 2.4: Porter’s Generic Strategies Kessinger and Pieper (2013) state that these generic strategies when put into practice bring about Competitve advantage that is measured in several ways:- How a firm gains sustainable cost advantage? How it differentiates itself from the her competitors? And how the firm chooses a segment so that competitive advantage grows out of a focus

17 strategy. Kessinger and Pieper add that Competitive Advantage therefore grows fundamentally out of the value a firm is able to create for its buyers. It can take the form of lower prices than competitors or provision of unique benefits that more than offset a premium price.

Davenport (2013) states that the drive towards Competitive Advantage therfore starts with understanding of the consumer and consumer behaviour. This is where Big Data Analytics comes into play. It provides information to the firm that was not previously available. This information can then be used by the firm to come up with unique strategies that provide for unique benefits to the customer that may not be easily discernible.

2.4.2 Examples of Big Data Analytics in Practice

2.4.2.1 Negotiation:

Al-Sakran (2014) undertook a study to explore the opportunities of using big data and business analytics for negotiation, where big data analytics can be used to create new opportunities for bidding. He stated that by using big data analytics sellers may learn to predict the buyers’ negotiation strategy and therefore adopt optimal tactics to pursue results that are to their best interests. His study showed that Negotiation as part of an intricate buying process can be shortened by use of Big Data Analytics. Using big data analytics, the seller agent will be able to predict the price a customer has in mind and find out what‘s included in other companies‘ offers in order to negotiate from a position of strength. Agents can do their research within a given price range and estimate the profit a business will gain. Krupnik (2013) built on this and mentioned that the Seller-Agent accurately predicts profitability based on different variables and that these variables included original price, available quantity, delivery time and other attributes. He concluded that based on that data, Sales Agents derive the best initial asking price and the walk-away price on the spot in order to maximize profit.

2.4.2.2 Supply Chain Management

Ittmann (2015) tried to capture the importance of Big Data in the Supply Chain Management within an organization. He mentioned in a survey conducted in 2013 by (Deloitte and Material Handling Institute [MHI], 2014), supply chain executives were questioned about ‘innovations that drive supply chains’. The aim was to obtain the views

18 of executives on emerging supply chain trends that could dramatically impact supply chains of the future. The two top strategic priorities for supply chain executives that emerged from the survey were supply chain analytics and multichannel fulfilment (Deloitte & MHI).

2.4.2.3 Insurance

Bologa (2013) dived deeper into how Big Data Analytics has been embedded into the Strategic Plans of the Romanian Health ministry and used in cutting down insurance fraud within the public health insurance system. He mentioned that the main issue in fraud detection was the fact that the collections of data was impossible to be processed by a human brain and that certain trends that were suspicious were not being picked up by the controllers. However, introducing Big Data Analytics and techniques can help curb fraud and reduce on the financial drain that Insurance deals on the public purse of government (Court, 2015).

2.4.2.4 Health Care

Belle et al (2011) noted that the rapidly expanding field of big data analytics in the Americas had started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyse, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics had been recently applied towards aiding the process of care delivery and disease exploration. Belle et al also noted that the Ministry of Health in Saudi Arabia put in place a solution that provided for public health professionals across the Kingdom with a secure, easy-to-use application to collect, share and analyse health information critical in managing public health outbreaks such as SARS, influenza or any other communicable diseases. In turn, as has been experienced in many other governments, it allows for far better communication among public health professionals and tools when responding to epidemic-prone and emerging disease threats, helping minimise the impact on people’s health and on provincial and national economies (Watson et al, 2012).

2.4.3 Big Data in Africa

(Johnson, 2012) stated that Big Data Analytics is being used in Africa by many institutions as part of the Corporate Strategy to create competitive edge. Some of these

19 are:- Regional grocery store, Nakumatt, that is already utilising predictive analytics to determine the buying habits of its customers across East Africa, to better target them with products they desire. (Ochieng, 2015) states that the loyalty card that earns these customers points as they shop, also stores and shares information on purchasing trends allowing Nakumatt to target and personalise the offers they make. He also explored trends in Kenya’s capital, Nairobi, where there are efforts to reduce congestion and augment public transport services using Big Data and analytics. Together with IBM, the city has been considering an approach that will collect and analyse data from the Kenyan capital’s transport grid to predict and identify delays; automatically reroute transport to optimal pathways; and notify commuters via live SMS and mobile app updates – all based on a similar approach developed by IBM for Singapore’s transport network. (IBM Analytics Journal, 2016).

This section looked at the ability of Big Data Analytics to spur the competitive advantage within the Industry in terms of building a cost leadership edge of an organisation over the other. Cost leadership is built over a period of time and involves using the most efficient, cost effective model to ensure that the firm enjoys healthy product margins over her competitors. This was well explored in the study done on Nakumatt Holdings Limited in trying to understand how Nakumatt establishes its competitive advantage over other super market chains based on its cost leadership model.

2.5 Chapter Summary

This chapter analyses literature review of Big Data analytics and its creation of competitive advantage in corporate firms with a focus on Nakumatt Holdings Limited guided by the specific objective highlighted in chapter one. The objectives include; analysing the Big Data strategies used by firms, to examine key success factors in Big Data implementation and use and to identify if utilization of Big Data Analytics in Nakumatt Holdings has created a competitive edge over her competitors.

CHAPTER THREE

3.0 RESEARCH METHODOLOGY

3.1 Introduction

This chapter discusses the research methodology for this study including the research design, population and sampling design which contains the; sampling frame, sampling technique and sample size. The research methodology also includes; data collection methods, research procedures and the data analysis methods. The chapter then concludes with chapter summary.

3.2 Research Design

This research adopted descriptive research design and specifically, the correlational research study. According to Cooper and Schindler (2014), descriptive research design is a scientific method that involves observing and describing behaviour of a subject without influencing it in any way. This design attempts to describe a subject by creating a profile of a group problem, people or events, through collection of data and the tabulation of frequencies on research valuables and the research answers questions of who, what, when, where or how much. Descriptive research design contributes to high response rate; low refusal rate and less time consuming (Cooper & Schindler, 2014). Correlational design was chosen as this study will aim to determine the link between the Big Data elements such as use customer loyalty cards and predictive analysis of customer trends and patterns to provide the competitive edge in Nakumatt in the form of differentiation, increased sales as well as cost leadership. The research focused on all the branches of Nakumatt in Kenya as per the time the research was carried out. There was a lot of focus on getting information from the Head Quarters as that was where many of the senior managers are based. In that aspect too, some of the questions that were earmarked for middle level employees and floor shop staff were answered by the Senior Management as per the policy of Nakumatt. These were captured as qualitative feedback. This study was guided by numbers of loyalty card holders, rate of reviews of Big Data as the independent variables with repeat purchase and sales as the dependent variables.

3.3 Population and Sampling Design

3.3.1 Population

Population is an aggregate or totality of all the objects, subjects or members that conform to a set of specifications (Polit & Hungler, 2013). The targeted population in sthis study are the managers, employees and customers of the Nakumatt supermarkets in Nairobi.

3.3.2 Sampling Design

3.3.2.1 Sampling Frame

A sampling frame is a list of all the elements of a complete and correct list of a population from which a sample is drawn from (Coopers & Schindler, 2014). In this study, the sampling frame involved all the management staff and employees at Nakumatt in the 23 branches as well as the customers that are in the system as loyalty card holders. The sampling frame was obtained from the human resource office of Nakumatt head office in Nairobi, along Mombasa road.

This is provided in the next page.

Table 3.1: Listing of Nakumatt branches in Nairobi County.

Nakumatt Branch Location Along Uhuru Highway,Opp. Nyayo Stadium 1 Nakumatt Mega Nairobi

2 Nakumatt Ukay Mwanzi Rd. / Ring Rd., Westlands Nairobi

3 Nakumatt Lifestyle Monrovia St. Nairobi

4 Nakumatt Embakasi Old Airport Road, Embakasi Nairobi

5 Nakumatt Village The Village Market, Gigiri Nairobi

6 Nakumatt Prestige Prestige Plaza, Along Ngong Road Nairobi

7 Nakumatt Highridge Highridge Shopping Centre Nairobi

8 Nakumatt South C Mugoya Estate/ Off. Popo Road Nairobi

9 Nakumatt Karen Karen Shopping Centre Nairobi

10 Nakumatt Junction Dagoretti Corner Nairobi

11 Nakumatt Ronald Ngala Along Ronald Ngala Nairobi

12 Nakumatt City Hall Along Wabere street Nairobi

13 Nakumatt Moi Avenue Along Moi avenue Nairobi

14 Nakumatt Haille Selasie Along Haile Selasie Nairobi

15 Nakumatt Express Wendani, Thika Road Nairobi

16 Nakumatt Galleria Galleria Mall along Langata Rd. Nairobi

17 Nakumatt Ridgeways Along Kiambu Road Nairobi

18 Nakumatt Thika Road TRM mall, Opp kasarani sports centre Nairobi

19 Nakumatt Lavington Off Wayaki way/ Westlands Nairobi

20 Nakumatt Shujaa Shujaa mall ,Spine road

21 Nakumatt Lunga Lunga lunga Mall Nairobi

22 Nakumatt Garden City Garden City Mall, Thika Road Nairobi

23 Nakumatt Westgate Westlands Nairobi

Nakumatt: About Us (2016). Retrieved from http://www.nakumatt.co.ke/?page_id=3205

This study targeted all the Twenty Three (23) branches in Nairobi including the employees therein as well as her customers.

Table 3.2: Population Size

Target Population

1 Nakumatt Branches 23

2 Nakumatt Managers 69

3 Nakumatt Employees 2311

4 Nakumatt Customers 5784

Nakumatt: About Us (2016). Retrieved from http://www.nakumatt.co.ke/?page_id=3205

3.3.2.2 Sampling Technique

3.3.2.3 Sample Size

Cooper and Schindler (2014) indicate that a sample size is a smaller set of the larger population. To obtain the minimum population sample for this study, the researcher will use the rule of thumb.

Table 3.3: Sample Distribution Table Using Rule of Thumb

Target Population Sample Size

1 Nakumatt Branches 23 23

2 Nakumatt Managers 69 69

3 Nakumatt Employees 2311 400

4 Nakumatt Customers 5784 400

3.4 Data Collection Methods

This research used both primary and secondary data. This study used questionnaires to collect primary data. Malhotra (2013) states that questionnaires are an important data collection tool because they provide an effective and efficient way of gathering

24 information within a very short time. The questionnaires helped code and analyse data easily for this research. The questionnaires contained closed and open ended questions. Closed ended questions ensure that the respondents are restricted to certain categories in their responses while open ended questions provide an insight of new ideas. The questionnaires are designed on the basis of the specific objectives and are standardized, valid and reliable for testing purposes for this research.

The questionnaire was divided into four (4) parts. First was on general information provided to all participants and this looked at background information about the respondent such as Age, Religion and Education. The second part looked at Use of BDA in assessing customer trends and patterns. This chapter focused on the employees in trying to understand whether Big Data is used within the organisation to align the firm to customer needs. The third part focussed on the customers and gathered information on loyalty cards and how they impact repeat purchases. The last part focussed on the senior managers and focussed on obtaining data on the use of BDA to assist management in establishing a cost leadership model in Nakumatt.

The questionnaires and corresponding checklist developed by the researcher. This aided in the data collection process as it was structured to assist in answering the research problem. Permission to conduct the study was obtained by writing an official letter for permission to the Human Resources manager at Nakumatt. This was backed by a physical visit to the site at Nakumatt Headquarters along Mombasa road.

3.5 Research Procedures

Before the questionnaires were administered to the respondents, the researcher conducted a pilot test on the questionnaires which was administered to 2 employees of Nakumatt who were not be part of the final data collection process. The pilot test is carried out to ensure that the questionnaires are complete, precise, accurate and clear. This is important to ensure reliability of the data collection instrument (Hussey and Hussey, 2007). After the pilot study, no amendments needed to be done, thus it was the final questionnaire used for collection of data. The researcher then explained the purpose of the research and sought permission from the Human Resource manager at Nakumatt to carry out the research. This was done through an introduction letter seeking to carry out the research in the company as well as a physical visit to the Headquarters. Upon provision of the go

25 ahead approval, the questionnaires was then administered to the respondents during working hours with the help of a qualified research assistant. The customers and employees in operation were encouraged to fill in the questionnaires on the spot as it is difficult to follow up with them at a later date.

3.6 Data Analysis Methods

Cooper and Schindler (2014) indicate that Data analysis includes both qualitative and quantitative techniques. They explain that Qualitative technique refers to any kind of research that produces findings not arrived at by means of statistical procedures or other means of quantification. This research approach is often expressed as personal value judgments from which it is difficult to draw any collective general conclusions. Saunders, Lewis and Thornhill (2012) mention that Qualitative research seeks insight through a less structured and more flexible approach whereas quantitative research generally involves the collection of data from large numbers of respondents with the aim of presenting the findings to a large population. They continue that the aim is to generalize about a specific population based on the results of a representative sample of that population. The research findings may then be subjected to mathematical or statistical manipulation to produce a broad representative of data to the total population and forecasts of future events under different conditions (McDaniel & Gates, 2013).

The collected data was coded and analysed using the descriptive statistics, specifically mean and standard deviation to describe each variable under study. Factor analysis was used in measuring the variability of the variables that were observed and correlated. The data was then be analysed using Statistical Package for Social Sciences (SPSS) program. Descriptive Statistics is presented using tables, and figures to give a clear picture of the research findings at a glance. Inferential Statistics such as correlations, standard deviations are also presented using graphs to give a conclusion on the relationship between the independent and dependent variables.

3.7 Chapter Summary

This chapter highlights the research methodology that was used in this study highlighting keenly the various methods and procedures the researcher adopted in conducting the study in order to answer the research questions raised in the first chapter. This research adopted probability based sampling and use descriptive research design to conduct Stratified Sampling from amongst the population. Chapter four will present the findings of this study.

CHAPTER FOUR

4.0 RESULTS AND FINDINGS

4.1 Introduction

This chapter presents the fundamental study findings and interpretations based on field- data accessed from the target study participants. The presented findings constitute a basis towards drawing the study conclusions and recommendations.

4.2 Demographic Information

4.2.1 Response Rate

The study sought requisite data from an aggregate of 400 Nakumatt Customers and 46 Nakumatt staff spread across Nairobi County. However, for staff, we had to use both qualitative and quantitative methodology because of regulations governing research in the company. We conducted an In-depth interview with the Head of consumer department, the person in charge of the Analysis Team and incorporated quantitative research questions to enable us achieve our objectives. By the end of data collection period, a total of 383 Customer questionnaires had been duly completed and considered for inclusion in the subsequent data processing and analysis phases. This completion translated to a summation of 95.7% response rate, which met the Bell (2003) benchmark of at least 60% response rate.

4.2.2 Gender, Age and Level of Education

The study was pure random, and research data indicated that consumers are not generally differentiated by gender.

Figure 4.1: Gender As illustrated in Figure 4.1 above and Table 4.1 below, of the surveyed respondents, there were 52.7% female shoppers against 47.3% male shoppers.

Table 4.1: Gender

Gender

Frequency Valid Percent

Valid Male 181 47.3

Female 202 52.7

Total 383 100.0

Figure 4.2: Age Group A significant proportion of surveyed respondents (44.09%) were between the age of 21- 30 years, followed by age group 31-40 at 41.94% and 11.83% and 2.15% age groups 41- 50 and 50+ respectively.

Table 4.2: Level of Education

Highest Level of Education

Frequency Valid Percent

Valid PHD 8 2.2

Masters 45 11.8

Undergraduate/ Diploma 214 55.9

High school 103 26.9

Primary 12 3.2

Total 383 100.0

Table 4.2 shows that Majority of the surveyed respondents have a college diploma or undergraduate degree, 2.2% studied up to PHD level, 11.8% have attained a Master’s degree, 26.9% and 3.2% have only studied up to high school and primary school level.

4.3 To What Extent Does Big Data Analytics Of Customer Trends And Patterns Impact Differentiation Within Nakumatt? 4.3.1 Introduction

Purposive sampling was used to select the respondent to talk to. Purposive sampling, one of the most common sampling strategies in qualitative research, groups participants according to preselected criteria relevant to a particular research question (for instance we selected the head of the Big Data Analytics team at Nakumatt to talk to.) Purposive sample sizes are often determined on the basis of theoretical saturation (the point in data collection when new data no longer bring additional insights to the research questions). In this case all the analysis is done at head office and results cascaded down to the branch managers.

4.3.2 Types of Big Data Analytics used at Nakumatt

Nakumatt Holdings has approximately 5000 employees countrywide. The key big data at Nakumatt is Sales Data and Loyalty Data running on an Oracle System. The Summary is as illustrated on the figures below; Sales Data and Loyalty Data.

Data: Sales Data Which Items are selling more and which items How items are selling on the shelf. are not moving?

Classifcation: Category and branch

Point of Sale (POS) data Shelf Data

Frequency: Reviewed fortnighly

Helps firm rationalize on the items they are This data also helps the organization with stocking; what to stock more or less or which deciding when and how to introduce new manufactures need to come pucsh their products. products.

Figure 4.3: Sales Data

Data: Loyalty What suggestions customers are giving in regard Which combination of Items Customers are to the which items are stocked and how they are buying arranged in the stores

Classifcation: Category and branch

POS data Feedback Data

Frequency: Reviewed fortnighly Helps firm rationalize on the items they are This data also helps the organization with stocking; what to stock more or less or which deciding when and how to introduce new manufactures need to come push their products. products.

Figure 4.4: Loyalty Data 4.3.3 Research Findings

Table 4. 3: Ease of getting products at Nakumatt

Over the last 2 years, I have been satisfied with my experience in the ease of getting what I want at Nakumatt as compared to other similar Super markets * Length with Loyalty card Cross tabulation

Count

Length with Loyalty card Total

0–6months 7–12months 1–3 4–6 6+ years years years

Satisfaction Strongly 4 0 0 0 0 4 with my Disagree experience in the ease Disagree 4 33 12 0 0 49 of getting Uncertain 4 12 33 0 0 49 what I want at Nakumatt Agree 16 41 54 4 12 127

Strongly 0 33 95 16 8 152 Agree

Total 28 119 194 20 20 381

Table 4.4: Satisfaction with the quality of products at Nakumatt

Over the last 2 years, I have been satisfied with my experience with the quality of the products at Nakumatt as compared to other similar Super markets * Length with Loyalty card Cross tabulation

Length with Loyalty card Total

0–6months 7–12 1–3 4–6 6+ months years years years

Satisfaction Strongly 0 0 4 0 0 4 with the quality Disagree of the products at Nakumatt as Disagree 4 21 4 0 0 29 compared to Uncertain 8 12 16 0 0 36 other similar Super markets Agree 8 49 107 12 12 188

Strongly 8 37 62 8 8 123 Agree

Total 28 119 193 20 20 380

From the cross tabulations and overtime, customers are more satisfied with the quality of products at Nakumatt stores and the ease getting what they want when shopping.

Table 4.5: Satisfaction with variety of products at Nakumatt

Over the last 2 years, I have been satisfied with my experience with the variety of products at Nakumatt as compared to other similar Super markets * Length with Loyalty card Cross tabulation

Length with Loyalty card Total

0 – 6 7–12 1–3 4–6 6+ months months years years years

Satisfaction Disagree 4 29 8 0 0 41 with the variety of Uncertain 8 21 29 0 0 58 products at Agree 8 41 95 8 8 160 Nakumatt Strongly Agree 8 29 62 12 12 123

Total 28 120 194 20 20 382

From the cross tabulations and overtime, customers are more satisfied with the variety of products at Nakumatt stores and the ease getting what they want when shopping.

4.3.4 Frequency Distribution Tables

Table 4.6: Satisfaction with ease of getting products at Nakumatt

Over the last 2 years, I have been satisfied with my experience in the ease of getting what I want at Nakumatt as compared to other similar Super markets

Frequency Percent Valid Percent Cumulative Percent

Valid Strongly Disagree 4 1.1 1.1 1.1

Disagree 49 12.9 12.9 14.0

Uncertain 49 12.9 12.9 26.9

Agree 128 33.3 33.3 60.2

Strongly Agree 152 39.8 39.8 100.0

Total 383 100.0 100.0

Table 4.7: Satisfaction with quality of goods at Nakumatt

Over the last 2 years, I have been satisfied with my experience with the quality of the products at Nakumatt as compared to other similar Super markets

Cumulative Frequency Percent Valid Percent Percent

Valid Strongly Disagree 4 1.1 1.1 1.1

Disagree 29 7.5 7.5 8.6

Uncertain 37 9.7 9.7 18.3

Agree 189 49.5 49.5 67.7

Strongly Agree 124 32.3 32.3 100.0

Total 383 100.0 100.0

Table 4.8: Satisfaction with variety of products at Nakumatt

Over the last 2 years, I have been satisfied with my experience with the variety of products at Nakumatt as compared to other similar Super markets

Frequency Percent Valid Percent Cumulative Percent

Valid Disagree 41 10.8 10.8 10.8

Uncertain 58 15.1 15.1 25.8

Agree 161 41.9 41.9 67.7

Strongly Agree 124 32.3 32.3 100.0

Total 383 100.0 100.0

Nakumatt holdings; the company uses Big Data analytics on a large scale. Data collected include: - Loyalty Card, Mobile phone data and sales data on a large scale and Social media data on a small scale basis.

The data is reviewed on average fortnightly and helps the firm in Inventory management that is helping the firm stock more or less of the products that are moving or stagnating. The data collected also helps management in stocking new products that are needed by the customer. This data helps Nakumatt execute its differentiation strategy as it focuses on a broad range of products that are unique to the market and makes them conveniently available.

4.4 How Does Use Of Nakumatt’s Loyalty Cards Increase Customer Repetitive Buying?

4.4.1 Introduction

According to PR Loyalty Solutions (2011) customer loyalty is both attitudinal and behavioural tendency to favour one brand over all others whether due to satisfaction with the products or service, its convenience or performance or simply familiarity and comfort with the brand. Similarly, Collin Shaw and Ryan Hamilton (2014) describe customer loyalty as the result of consistently positive emotional experience, physical attribute- based satisfaction and perceived value of an experience which includes the products or

35 services. In the study, customer loyalty was measured using indicators such as ownership of loyalty card, usage frequency, length of usage, and satisfaction level of various parameters.

4.4.2 Research Findings

Figure 4.5: Length with Loyalty Card Slightly more than half (50.54%) of the surveyed respondents have owned the Nakumatt Loyalty card for between 1-3 years showing the importance customer peg to loyalty programs.

Table 4.9: Descriptive Statistics

Descriptive Statistics Std. N Minimum Maximum Mean Deviation Variance Skewness Regular use Loyalty Card when purchasing goods at Nakumatt 383 2 5 3.8925 0.89875 0.808 -0.503 Ease of redeeming my loyalty points 383 2 5 3.8925 0.93404 0.872 -0.502 Ease of using loyalty card for payments at any other point of sale 383 1 5 3.6667 1.18688 1.409 -0.771 Challenges while loading card with cash 383 1 5 3.8387 0.97723 0.955 -0.508 Satisfaction with ease of getting what I want at Nakumatt 383 1 5 3.9785 1.06879 1.142 -0.809 Satisfaction with experience on the pricing of goods at Nakumatt 383 1 5 3.6774 1.08065 1.168 -0.516 Satisfaction with the quality of the products at Nakumatt 383 1 5 4.043 0.90415 0.817 -1.053 Satisfaction with the variety of products at Nakumatt 383 2 5 3.957 0.95065 0.904 -0.671 Frequent shopper at Nakumatt 383 1 5 4.2796 0.96697 0.935 -1.304

The table presents the summarized ordinal measures on a 5-point scale. From the study, most consumers regularly use their loyalty card when purchasing goods at Nakumatt, with a mean of (3.8925) and least deviation of (0.89875), as seen from the negative skewness of the attribute, (-0.503) most consumers rated the attribute above the mean rating. This implies that most consumers are always using their loyalty cards for shopping. Consumers also perceive Nakumatt to have the best quality of goods as seen from the high ratings of the attribute with a mean of 4.0430 and a standard deviation of 0.90415. Redemption of points cited as easy with a mean of 3.8925 and standard deviation of 0.93404 implying the effective use of Big Data Analytics as consumers do not experience any problems while redeeming the points.

Cross Tabulations: Customer Loyalty by length with Loyalty Card. a. Number of valid cases is different from the total count in the cross tabulation table because the cell counts have been rounded.

Table 4.10: Regular use of Loyalty Card

I regularly use my Loyalty Card to purchase goods at Nakumatt * Length with Loyalty card Cross tabulation

Length with Loyalty card Total

0–6 7–12 1–3 4–6 6+ months months years years years

I regularly Disagree 8 8 16 0 0 32 use my Loyalty Uncertain 12 33 33 0 0 78 Card to Agree 8 66 78 8 8 168 purchase goods at Strongly 0 12 66 12 12 102 Nakumatt Agree

Total 28 119 193 20 20 380

Table 4.11: Ease of Redeeming Loyalty Points

I find it easy redeeming my loyalty points * Length with Loyalty card Cross tabulation

Length with Loyalty card Total

0–6 7–12 1–3 4–6 6+ months months years years years

I find it easy Disagree 4 16 16 0 0 36 redeeming my loyalty points Uncertain 8 25 41 4 0 78 Agree 12 58 74 8 4 156

Strongly 4 21 62 8 16 111 Agree

Total 28 120 193 20 20 381

Table 4.12: Frequent Shopper at Nakumatt

Over the last 2 years, I can categorise myself as a frequent shopper at Nakumatt (at least once a month) * Length with Loyalty card Cross tabulation

Length with Loyalty card Total

0–6 7–12 1–3 4–6 6+ months months years years years

I can categorise Strongly 0 4 0 0 0 4 myself as a Disagree frequent shopper at Disagree 4 21 0 0 0 25 Nakumatt (at Uncertain 4 8 29 0 0 41 least once a month) Agree 8 33 54 8 0 103

Strongly 12 54 111 12 21 210 Agree

Total 28 120 194 20 21 383

From the cross tabulations, customers who have had their loyalty cards longer seem to agree more with the measured attributes showing the importance customers peg loyalty cards.

Frequency Distribution Tables

Table 4.13: Regular use of Loyalty Card

I regularly use my Loyalty Card to purchase goods at Nakumatt

Cumulative Frequency Percent Valid Percent Percent

Valid Disagree 33 8.6 8.6 8.6

Uncertain 78 20.4 20.4 29.0

Agree 169 44.1 44.1 73.1

Strongly Agree 103 26.9 26.9 100.0

Total 383 100.0 100.0

Table 4.14 Ease of redeeming loyalty points

I find it easy redeeming my loyalty points

Frequency Percent Valid Percent Cumulative Percent

Valid Disagree 37 9.7 9.7 9.7

Uncertain 78 20.4 20.4 30.1

Agree 156 40.9 40.9 71.0

Strongly Agree 111 29.0 29.0 100.0

Total 383 100.0 100.0

Table 4.15 Frequency of Shopping

Over the last 2 years, I can categorise myself as a frequent shopper at Nakumatt (at least once a month)

Frequency Percent Valid Percent Cumulative Percent

Valid Strongly Disagree 4 1.1 1.1 1.1

Disagree 25 6.5 6.5 7.5

Uncertain 41 10.8 10.8 18.3

Agree 103 26.9 26.9 45.2

Strongly Agree 210 54.8 54.8 100.0

Total 383 100.0 100.0

One Way ANOVA on Customer loyalty attributes by how long they have had the loyalty card

From the Analysis of variance results (reference to Appendix V: One Way ANOVA on Customer loyalty attributes by how long they have had the loyalty card) , the length the customer has had the Card has no significant effect (0.697) on their decision to use the Card as a payment card at other point of sales. There’s however a significant effect on

40 the effect the length of having the card has on other measured parameters as indicated on the table.

4.5 How does use of Big Data Analytics create cost leadership over Nakumatt’s competitors?

4.5.1 Introduction

We used purposive sampling to select the respondent to talk to. Purposive sampling groups participants according to preselected criteria relevant to a particular research question (for instance we selected the head of the Big Data Analytics team at Nakumatt to talk to.) Purposive sample sizes are often determined on the basis of theoretical saturation (the point in data collection when new data no longer bring additional insights to the research questions). In this case all the directive comes from the Head of Consumer and results are used by the same person and cascaded down to the branch managers.

4.5.2 Research Findings

Table 4.16: Satisfaction with the Pricing of Good at Nakumatt

Over the last 2 years, I have been satisfied with my experience on the pricing of goods at Nakumatt as compared to other similar Super markets

Frequency Percent Valid Percent Cumulative Percent

Valid Strongly Disagree 8 2.2 2.2 2.2

Disagree 62 16.1 16.1 18.3

Uncertain 70 18.3 18.3 36.6

Agree 148 38.7 38.7 75.3

Strongly Agree 95 24.7 24.7 100.0

Total 383 100.0 100.0

Table 4.17: Use of Big Data Analytics to create cost leadership at Nakumatt

Strongly Strongly Agree Agree Undecided Disagree Strongly Disagree Relative advantage Sales : Using big data analytics has led to increased sales in this branch

Relative advantage Customer Trends : Using big data analytics has enabled me understand my customers better this branch

Compatibility: The firm has the right infrastructures, technical skills and IT platform to implement big data analytics technology

Complexity : It is easy to implement and use big data analytics technology

Triability: Information obtained from analyzing customer feedback/ patterns has been used by management to effect changes

Observability: Many firms/organizations are already using big data analytics to drive their business processes

Top management supports implementation of big data analytics as a business innovation for competitive advantage by availing resources

Table 4.18: Satisfaction with Pricing of goods at Nakumatt

Over the last 2 years, I have been satisfied with my experience on the pricing of goods at Nakumatt as compared to other similar Super markets * Length with Loyalty card Cross tabulation

Length with Loyalty card Total

0–6 7–12 1–3 4–6 6+ months months years years years

Satisfaction Strongly 4 4 0 0 0 8 with my Disagree experience on the Disagree 4 29 21 4 4 62 pricing of Uncertain 4 33 29 4 0 70 goods at Nakumatt Agree 12 33 86 4 12 147

Strongly 4 21 58 8 4 95 Agree

Total 28 120 194 20 20 382

Table 4.19: Correlation between Increased Shoppers Vs Increased Loyalty Card Numbers

% Increase in loyalty card holders % Increase in number of shoppers

8% 12%

Over time, there has been a gradual increase in the number of loyalty card holders at Nakumatt stores. This has led to subsequent increase in the number of shoppers at the stores. Nakumatt management also points to an equally a strong correlation between increased sales and increased loyalty card numbers at Nakumatt branches

Increase in Sales at Nakumatt driven by increased loyalty card holders has helped Nakumatt take advantage of scale to reduce on its per capita costs. Therefore, this gives Nakumatt advantage in terms of higher margins per product sold as compared to other competitors. Most customers (64%) are satisfied with the pricing of goods at Nakumatt, thus driving the repeat purchases.

4.6 Chapter Summary

This chapter discusses the research findings of the role of Big Data analytics at Nakumatt holding and how this has helped them to understand customer needs & trend analysis and create differentiation, how use of loyalty cards has driven customer repetitive buying and how they have used the analytics to create cost leadership for Nakumatt Holdings.

CHAPTER FIVE

5.0 DISCUSSION, CONCLUSION AND RECOMMENDATIONS

5.1 Introduction

This chapter presents the study’s summary of findings on thematic areas, conclusions, recommendations, and suggestions for further studies. The summary of findings is based on each and every indicator used in the study while the conclusions and recommendations 0are based on the generalized views under each objective area.

5.2 Summary of Findings

Thematically, the study was intended to analyse the role of Big Data Analytics (BDA) on the competitive advantage of Nakumatt Supermarket in Nairobi County. Specifically, the focus was placed on customer needs and trends, customer loyalty and use of big data in creating cost leadership for Nakumatt Managers. The findings for each of these are as summarized hereunder;

From the study, we can conclusively say that Nakumatt Holdings uses Big Data Analytics on a large scale, and this analysis is done at the head office and results cascaded downwards, that is to the branches through the branch managers. Key Big Data at Nakumatt are Loyalty data and Sales Data and is done on a large scale. The company also collects social media data though is done on a fairly low scale. The data collected is reviewed at least fortnightly, still at the head office, by a team of analysts working in the operations department under the Head of Consumer Department and findings used to inform business decisions in the company like introduction of new items, or getting rid of dead stock. It is from this analysis that Nakumatt has been able to position itself a notch higher than its competitors, thus creating differentiation by introducing a variety of products, this is from the analysis of customer feedback and acting on it. This has driven customer satisfaction as seen from very high ratings of the attributes. The company has also been able to arrange products systematically, with some having combinations from customer feedback. This has made it easy for customers to access products at the stores thus improving the customer experience at Nakumatt stores which in return has given Nakumatt the upper edge of differentiation from their competitors. And all this is because of Big Data Analytics, where there were able to analyse trends and patterns.

The findings from the study indicate that despite the fact that all the surveyed respondents have a loyalty card, there were divergent views on the measured attributes regards repetitive buying driven by use of loyalty cards. Customers clearly use their loyalty cards regularly while shopping, the attribute had a fairly high mean of (3.8925) and the least standard deviation (0.89). Moreover, the study found consistency that the consumers who have had their loyalty card for 1-3 years were likely to give a positive rating on the surveyed parameters as shown on the cross tabulations between the attributes and the length of having the loyalty card. Majority of the customers state that Nakumatt has good quality products compared to other similar supermarkets with a mean of 4.04 and standard deviation of 0.904. There is also high agreement among respondents that they find redeeming loyalty points relatively easy (mean 3.8925 and standard deviation 0.934). With a wider standard deviation though at (1.187), the extent of customers using their loyalty card at other POS averaged at 3.667 score which was marginally below the mean mark (3.914). This positive customer experience at Nakumatt stores that is ease of getting what they want and redeeming loyalty points, satisfaction with measured attributes that has been made possible by the analysis of Big Data at the company and also the regular use of loyalty card when shopping has driven repetitive buying at Nakumatt stores

With respect to creating cost leadership, the findings show that Big data analytics has had a relative advantage on the sales at Nakumatt as the management make decisions based on the data collected and analysed and this has improved the Holdings’ understanding of customer trends and customers as a whole. This data also helps them decide on when and how to introduce a new product and make decisions on slow moving items. Nakumatt Holdings management is focussed on embracing technology to achieve its strategic goal. Part of technology is use of Big Data Analytics and this has been made possible by the management investing in the right infrastructure, technical skills and IT platforms to implement Big Data Analytics technology and on overall, the company finds it relatively easy to implement and use Big Data Analytics supported by top management although they feel they have not reached scale yet. Growth has been realized in the last two years in the number of frequent shoppers and loyalty card holders with an average of 12% increase in the number of frequent shoppers at Nakumatt branches and an 8% increase in the number of loyalty card holders. Most customers (64%) are satisfied with the pricing of products at Nakumatt thus helping Nakumatt achieve economies of scale. This has

46 enabled Nakumatt to achieve cost leadership over its competitors as Nakumatt pricing is relatively higher than her peers but the sales are also higher compared to her peers.

5.3 Discussion

5.3.1 To What Extent Does Big Data Analytics of Customer Trends And Patterns Impact Differentiation Within Nakumatt

The findings indicate that Nakumatt has been able to position itself a notch higher than its competitors, thus creating differentiation by introducing a variety of products as they analyse customer feedback and act on it. This has driven customer satisfaction as seen from very high ratings of the attributes. The company has also been able to arrange products systematically, with some having combinations from customer feedback. This has made it easy for customers to access products at the stores thus improving the customer experience at Nakumatt stores which in return has given Nakumatt the upper edge of differentiation from their competitors.

From the literature review of Big Data Analytics, Prescriptive Analysis is really valuable, but largely not used. According to Gartner (2014), thirteen (13) percent of organizations are using predictive analysis but only 3 percent are using prescriptive analytics. Predictive Analytics uses big data to identify past patterns to predict the future (Marr, 2016). For example, some companies are using predictive analytics for sales lead scoring. Some companies have gone one step further use predictive analytics for the entire sales process, analyzing lead source, number of communications, types of communications, social media, documents, CRM data, etc. Properly tuned predictive analytics can be used to support sales, marketing, or for other types of complex forecasts.

Our findings indicate that Nakumatt Holdings are part of the 13% that are using predictive analysis to analyze trends and patterns of customers thus being able to anticipate the customer needs. The Company uses Big Data Analytics on a large scale; key Big Data being Loyalty data and Sales Data. Loyalty, Mobile and Sales data are collected on a large scale. Social media data is also collected albeit on a fairly low scale.

Data is reviewed at least fortnightly and findings used to inform business decisions in the company like introduction of new items, or getting rid of dead stock. Nakumatt has also been been able to introduce a variety of products that have driven customer satisfaction.

Nakumatt has also been able to arrange products systematically, with some having combinations from customer feedback. This has made it easy for customers to access products at the stores thus improving the customer experience at Nakumatt stores which in return has given Nakumatt the upper edge of differentiation from their competitors. This is in line with the existing literature on the Big Data analytics, that states that properly tuned predictive analytics can be used to support sales, marketing, and for other complex forecasts.

On a small scale though, Nakumatt Holdings also uses prescriptive analysis that gives a laser-like focus to answer specific questions. For example, at Nakumatt, they are able to “prescribe” the needs of some specific customers’; case in point, the management mentioned about introduction of a specific kind of socks, which was driven by the customer combinations when shopping, and some of the questions raised by customers. Nakumatt therefore went ahead to introduce this specific item mainly for a particular target group.

From the Nakumatt Holdings case study though, they have not fully embraced Diagnostic Analytics that is used for discovery i.e. to determine why something happened (Al Sakran, 2015), could be because they are not really focused on the social media marketing campaign, that can adopt descriptive analytics to assess the number of posts, mentions, followers, fans, page views, reviews, pins, etc. There can be thousands of online mentions that can be distilled into a single view to see what worked in the past campaigns and what didn’t. Thus this is an area that the company can focus on to also target their customers who express their concerns on social media.

5.3.2 How Does Use of Nakumatt’s Loyalty Cards Increase Customer Repetitive Buying?

From the findings, customers clearly use their loyalty cards regularly while shopping; the attribute had a fairly high mean of (3.8925) and the least standard deviation (0.89). Moreover, the study also found consistency that the consumers who have had their loyalty card for 1-3 years were likely to give a positive rating on the surveyed parameters.

Grossman and Siegel (2014) conceptualized a model for Big Data Analytics based on an organizational framework that seeks to integrate analytics, business knowledge, and

48 information technology as reflected in the figure below. The 3 work have to work in tandem for there to be any realization of success.

Ochieng, (2015), states that the loyalty card that earns these customers points as they shop, also stores and shares information on purchasing trends allowing Nakumatt to target and personalise the offers they make, this was validated by the study as findings indicate that Nakumatt collects loyalty card data on a large scale and the data is analysed to understand the trends i.e. customer spent, the combinations of items bought, frequency, amount spent et al.

Brown et al. (2013) explains that data is all around us and that organisations need to be able to pick on what scope of data will be relevant for its use. Brown et al add that there is need for a fine balance between collecting an inadequate amount of data that does not provide proper analytical value and capturing all kinds of data that puts a strain on organizational storage facilities. Nakumatt have been able of prioritize on the type of data to pick for analysis such that they do not get overwhelmed with so much data that is not actionable.

Big Data Analytics requires experienced analysts able to analyse and decipher data to churn out the requisite information (Baesens, 2014). These skills are still scarce and organisations need to build the capability required. Nakumatt however feels they are well equipped both resource wise and infrastructure and technology wise to implement the Big Data Analytics. Customers regularly use their loyalty cards while shopping with high mean of (3.8925) and the least standard deviation (0.89) and have not had any challenges with the card. Moreover, the study found consistency that the consumers who have had their loyalty card for 1-3 years were likely to give a positive rating on the surveyed parameters as shown on the cross tabulations between the attributes and the length of having the loyalty card. This positive experience has driven the customers’ repetitive purchase. Customers claims Nakumatt has good quality products compared to other similar supermarkets with a mean of 4.04 and standard deviation of 0.904. There is also high agreement among respondents that they find redeeming loyalty points relatively easy (mean 3.8925 and standard deviation 0.934).

The positive customer experience at Nakumatt stores that is ease of getting what they want and redeeming loyalty points, satisfaction with measured attributes and the regular use of loyalty card when shopping that has primarily been made possible because of the

49 analytics and taking into consideration the customer feedback and expectations has driven repetitive buying at Nakumatt stores.

5.3.3 How does use of Big Data Analytics create cost leadership over Nakumatt’s competitors?

The findings from the study show that Big Data Analytics has led to a relative advantage on the sales at Nakumatt even when the pricing at Nakumatt is relatively higher than her peers. This has led to cost leadership and improved profitability for Nakumatt. The data collected helps management decide on when and how to introduce a new product and make decisions on slow moving items.

This objective sought to answer the ability of Big Data Analytics to spur the competitive advantage within the Industry in terms of building a cost leadership edge of an organisation over the other. Cost leadership is built over a period of time and involves using the most efficient, cost effective model to ensure that the firm enjoys healthy product margins over her competitors. Michael Porter (1998) states that competition is at the core of success or failure of firms and that Competitive Strategy is the search for a favourable competitive position in an Industry. Kessinger and Pieper (2013) state that generic strategies when put into practice bring about competitve advantage that is measured in several ways:- How a firm gains sustainable cost advantage? How it differentiates itself from the her competitors? And how the firm chooses a segment so that competitive advantage grows out of a focus strategy. Kessinger and Pieper add that Competitive Advantage therefore grows fundamentally out of the value a firm is able to create for its buyers. It can take the form of lower prices than competitors or provision of unique benefits that more than offset a premium price.

Nakumatt Holdings management has embraced the use of Big Data Analytics through having the right infrastructure, technical skills and IT platforms to implement it. This has helped Nakumatt gain competitive advantage over competitors by being able to action on the findings of the analysis by understanding of customer trends and customers as a whole. Big data analytics has also had a relative advantage on the sales at Nakumatt as the management make decisions based on the data collected and analysed and has improved the company’s offer to their customers. This data also helps them decide on when and how to introduce a new product and make decisions on slow moving items.

Similarly, Davenport (2013) states that the drive towards competitive advantage starts with understanding of the consumer and consumer behaviour, through use of Big Data Analytics that provides information to the firm that was not previously available. This information can then be used by the firm to come up with unique strategies that provide for unique benefits to the customer that may not be easily discernible. Nakumatt has been able to action on the results that has driven satisfaction amongst most (64%) of its consumers with the pricing of products at Nakumatt thus helping Nakumatt achieve economies of scale. Some growth has also been realized in the last two years in the number of frequent shoppers and loyalty card holders with an average of 12% increase in the number of frequent shoppers at Nakumatt branches and an 8% increase in the number of loyalty card holders.

5.4 Conclusions

5.4.1 To What Extent Does Big Data Analytics of Customer Trends and Patterns Impact Differentiation within Nakumatt?

Use of big data has enabled Nakumatt holdings to have a clear differentiation amongst its competitors by anticipating the customer needs and addressing them in time before the customer sees the need to switch to competitor supermarkets thus helping them retain most of their customer base and earn new ones. They have been able to drive satisfaction amongst their customers thus clearly winning over competition.

5.4.2 How Does Use Of Nakumatt Loyalty Cards Increase Customer Repetitive Buying?

Loyalty cards incentivizes customers as seen as with the high rate of repeat purchases as consumers are motivated by the loyalty points they earn, customers are motivated by the fact that the more the shop at Nakumatt stores, the more they earn the points and therefore the more the returns. This has driven regular use of the loyalty cards in Nakumatt Supermarkets. The ease with which they redeem the loyalty points has also encouraged consumers to continue shopping at Nakumatt.

5.4.3 How Does Use of Big Data Analytics Create Cost Leadership Over Nakumatt’s Competitors?

Nakumatt has the capacity and resources and willingness to implement use of Big Data Analytics. This is pegged on the fact that Nakumatt has been able to use findings from the data analysed to improve customer experience in the branches. Another key indicator is the fact that customers do not have any challenges when using their loyalty cards at the outlets indicating a proper system on which the card operates.

The use of big data analytics has also enabled Nakumatt holdings to understand their customers better thus giving them better offerings. There is a strong positive correlation between the increase in number of loyalty cards and increase in sales hence giving Nakumatt economies of scale and better margins as compared to other competitors.

5.5 Recommendations

5.5.1.1 To what extent does Big Data Analytics of customer trends and patterns impact differentiation within Nakumatt?

Nakumatt should create an action log track for end to end implementation and review the data collected regularly for the company to be able to optimize on Big Data Analytics.

Nakumatt needs to focus on the use of Diagnostic and descriptive statistics to now go further into understanding why and how some customers behave or do what they do. This is more important on the analysis of social media data that has become key in terms of customer feedback point. Most customers express their dissatisfaction on social media therefore Nakumatt should also focus on collecting more social media data of their customers to be able to understand them more and curb any negative publicity that could impact negatively on the brand of the company.

5.5.1.2 How Does Use Of Nakumatt Loyalty Cards Increase Customer Repetitive Buying?

More effort should be put to analysis of customer data, i.e. loyalty data to be analysed more often so that the company is able to deliver real time solutions to customer expectations. The company should also educate customers on the other uses of loyalty card like a payment card, this will give customers convenience and grow the uptake.

5.5.1.3 How does use of Big Data Analytics create cost leadership over Nakumatt’s competitors?

Nakumatt should come up with newer and proactive approaches to using the available data efficiently in order to optimize on the available data for data development and also continue to proactively work ahead of technological innovations so as to proactively deal with the changing dynamics in the big data analytics.

The company can also focus on using Big Data for performance management that involves understanding the meaning of big data in a company databases using pre- determined queries and multidimensional analysis Hoffman (2013) by use of transactional data like years of customer purchasing activity, and inventory levels and turnover by asking questions such as which are the most profitable customer segments and get answers in real-time that can be used to help make short-term business decisions and longer term plans.

5.5.2 Recommendations for Further Research

The study was aimed at identifying the strategic role of Big Data Analytics (BDA) on the competitive advantage of supermarket chains, with particular focus on Nakumatt Holdings. Although, the research collected data and used Statistical interpretation to conclude on the research problem, in order to have a more objective and reliable generalization, the scope should have been widened to include other Super market chains in Kenya such as Uchumi and Tuskys. This is to enable a thorough understanding of the role if any that BDA plays in the running of these other organisation. This would have given a control that could have been used to justify or reject the results of these findings. It is therefore a recommendation that to corroborate these findings, a similar study needs to be done in other Supermarkets.

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APPENDICES

APPENDIX 1: LETTER OF INTRODUCTION

Dear Sir/Madam;

REQUEST TO COLLECT DATA

I am a Master’s student at United States International University-Africa completing my degree of Masters in Business Administration (MBA)-Strategic Management. I am required to conduct research on a topic of my choice that will contribute positively to the body of knowledge and the industry as a whole which led me to choose The Impact of Big Data Analytics on the Competitive Advantage of Supermarket Chains: A case study of Nakumatt Holdings Limited.

It is to this effect am seeking assistance from you in completing this questionnaire. Please note that any information you give will be treated with confidentiality and at no instance will it be used for any other purpose other than for this research.

Thank you very much for taking time off your busy schedule. Do not hesitate to contact me if you have any questions.

Regards;

Kenneth Patrick Ogwang

APPENDIX II: RESEARCH QUESTIONNAIRE

PART 1: GENERAL INFORMATION (Please Tick appropriately for each question and Specify where necessary).

1. Gender

Gender Male 1 Female 2

2. Age group:

Age (Years) 21 – 30 1 31 – 40 2 41- 50 3 50 + 4

3. What is your highest form of Education

Highest Level of Education PHD 1 Masters 2 Undergraduate 3 High school 4 Primary 5 None 6

PART 2: EXTENT BIG DATA ANALYTICS OF CUSTOMER TRENDS AND PATTERNS IMPACT DIFFERENTIATION

EMPLOYEES

This section covers the firm’s usage of Big Data Analytics

4. What is your position at NAKUMATT?

Level at Nakumatt Management (Divisional Heads) 1 Middle level (Heads of department and Section heads) 2 Lower level (Management trainee and Supervisors 3 Subordinate (Administration and clerks) 4 Others (Specify) 5

5. How long have you worked at NAKUMATT?

Length at Nakumatt Less than a year 1 Between 1 -2 years 2 3 -4 years 3 5 years and above 4

6. How long has this branch been in operation?

Age of the branch

Less than one year 1

1 – 5 years 2

6 – 10 years 3

11 – 15 years 4

16 – 20 years 5

Over 20 years 6

7. Please specify the size of your branch in terms of employees

Number of Employees 1 – 50 employees 1

51 – 100 employees 2

101 – 150 employees 3

150+ employees 4

8. I’m going to read to you some statements regarding big data analytics, please tell me the extent to which you agree or disagree with the statements on a scale of 1 – 5 where; 1-Strongly Disagree, 2-Disagree, 3-Uncertain, 4-Agree, 5-Strongly Agree

Customer Needs and Trends

Strongly Strongly Disagree Disagree Uncertain Agree Strongly Agree a) My branch uses Big Data analytics 1 2 3 4 5

b) My branch uses Big Data Analytics in a large 1 2 3 4 5 scale

c) I collect loyalty card data from my customers 1 2 3 4 5

d) I collect social media data from my 1 2 3 4 5 customers

e) I collect Mobile phone data from my 1 2 3 4 5 customers

f) The data collected is reviewed on a frequent 1 2 3 4 5 basis say monthly

g) An Action log is prepared based on the data 1 2 3 4 5 collected and tracked for end to end implementation

PART 3: USE OF LOYALTY CARDS TO DRIVE REPEAT PURCHASE AMONGST NAKUMATT CUSTOMERS

CUSTOMERS This section covers the customer’s experience while shopping at Nakumatt

9. Do you have a loyalty card?

Loyalty Card Yes 1 No 2 (Close if No)

10. If yes, how long have you had your loyalty card ? Length with Loyalty card 0 – 6 months 1 7 – 12 months 2 1 – 3 years 3 4 – 6 years 4 6 + years 5

11. I’m going to read to you some statements regarding the use of your loyalty card and general experience at Nakumatt, please tell me the extent to which you agree or disagree with the statements on a scale of 1 – 5 where; 1-Strongly Disagree, 2-Disagree, 3-Uncertain, 4-Agree, 5-Strongly Agree

Customer Loyalty

Strongly Strongly Disagree Disagree Uncertain Agree Strongly Agree a) I regularly use my Loyalty Card to purchase 1 2 3 4 5 goods at Nakumatt

b) I find it easy redeeming my loyalty points 1 2 3 4 5

c) I can easily use my loyalty card for 1 2 3 4 5 payments at any other point of sale apart from Nakumatt stores

d) I don’t get any challenges while loading my 1 2 3 4 5 card with cash

e) Over the last 2 years, I have been satisfied 1 2 3 4 5 with my experience in the ease of getting what I want at Nakumatt as compared to other similar Super markets

f) Over the last 2 years, I have been satisfied 1 2 3 4 5 with my experience on the pricing of goods at Nakumatt as compared to other similar Super markets

g) Over the last 2 years, I have been satisfied 1 2 3 4 5 with my experience with the quality of the products at Nakumatt as compared to other similar Super markets

h) Over the last 2 years, I have been satisfied 1 2 3 4 5 with my experience with the variety of products at Nakumatt as compared to other similar Super markets

i) Over the last 2 years, I can categorise 1 2 3 4 5 myself as a frequent shopper at Nakumatt (at least once a month)

PART 4: USE OF BDA IN CREATING COST LEADERSHIP FOR NAKUMATT MANAGERS

This section covers the firm’s usage of Big Data Analytics

12. In your estimate over the last 2 years, what is the % increase of frequent shoppers in this Nakumatt branch?

% increase of frequent shoppers 0 -20 % 1 21 – 40 % 2 41 – 60 % 3 61 – 80 % 4 81 – 100% 5 More than 100% 6 6. By your estimate over the last 2 years, what is the % increase of loyalty card holders in this Nakumatt branch?

% increase loyalty card holders 0 -20 % 1 21 – 40 % 2 41 – 60 % 3 61 – 80 % 4 81 – 100% 5 More than 100% 6

13. The following statements relate to the factors impacting on the adoption of big data analytics and the use of Big Data Analytics to create a competitive Advantage for Nakumatt. Please tell me the extent to which you agree with each statement as having influenced your adoption of big data analytics by your branch.

Strongly Strongly Agree Agree Undecided Disagree Strongly Disagree Relative advantage Sales : Using big data analytics has 1 2 3 4 5 led to increased sales in this branch

Relative advantage Customer Trends : Using big data 1 2 3 4 5 analytics has enabled me understand my customers better this branch

Compatibility: The firm has the right infrastructures, 1 2 3 4 5 technical skills and IT platform to implement big data analytics technology

Complexity : It is easy to implement and use big data 1 2 3 4 5 analytics technology

Triability: Information obtained from analyzing 1 2 3 4 5 customer feedback/ patterns has been used by management to effect changes

Observability: Many firms/organizations are already 1 2 3 4 5 using big data analytics to drive their business processes

Top management supports implementation of big data 1 2 3 4 5 analytics as a business innovation for competitive advantage by availing resources

Thank You for Your Participation

APPENDIXIII: ANOVA Table 4.14 One Way ANOVA on Customer loyalty attributes by how long they have had the loyalty card

One Way Analysis of Variance on Customer Loyalty attributes by length of the loyalty card Sum of Df Mean F Sig. Conclusions Squares Square Use of Between 50.728 4 12.682 18.5 .000 The length the Loyalty Groups 93 customer has had Card to the loyalty card purchase has a significant goods at effect on the Nakumatt measured attribute

Within 257.833 378 .682 Groups Total 308.561 382 Ease of Between 27.160 4 6.790 8.38 .000 The length the redeeming Groups 5 customer has had loyalty the loyalty card points has a significant effect on the measured attribute

Within 306.110 378 .810 Groups Total 333.271 382 Ease of Between 3.128 4 .782 .552 .697 The length the using loyalty Groups customer has had card for the loyalty card payments at has NO significant any other effect on the point of sale measured attribute

Within 534.994 378 1.415 Groups Total 538.122 382 Challenges Between 14.107 4 3.527 3.80 .005 The length the while Groups 1 customer has had loading my the loyalty card card with has a significant cash effect on the measured attribute

Within 350.693 378 .928 Groups Total 364.800 382 Satisfaction Between 61.756 4 15.439 15.5 .000 The length the with the ease Groups 79 customer has had

64 of getting the loyalty card what I want has a significant at Nakumatt effect on the measured attribute

Within 374.604 378 .991 Groups Total 436.361 382 Satisfaction Between 34.094 4 8.523 7.82 .000 The length the with the Groups 0 customer has had pricing of the loyalty card goods at has a significant Nakumatt effect on the measured attribute

Within 412.009 378 1.090 Groups Total 446.103 382 Satisfaction Between 13.661 4 3.415 4.32 .002 The length the with the Groups 3 customer has had quality of the loyalty card the products has a significant at Nakumatt effect on the measured attribute

Within 298.620 378 .790 Groups Total 312.281 382 Satisfaction Between 38.322 4 9.580 11.8 .000 The length the with the Groups 00 customer has had variety of the loyalty card products at has a significant Nakumatt effect on the measured attribute

Within 306.905 378 .812 Groups Total 345.227 382 I can Between 33.687 4 8.422 9.84 .000 The length the categorise Groups 1 customer has had myself as a the loyalty card frequent has a significant shopper at effect on the Nakumatt measured attribute

Within 323.497 378 .856 Groups Total 357.183 382