Research Background

How Big Data play its role in product innovation?

Objectives:

Relation between Big Data(ECRM) and Customer’s behaviour

Role of Big Data in finding Customers Behaviour

Role of Big data in product customisation

Role of Big Data in identifying the demand of customers

Role of Big data in making innovative products and services

Role of Big data in achieving Competitive Advantage

Big Data is that term for the data sets, which are very large as well as complex that the applications of traditional data processing are not very adequate (Zikopoulos and Eaton 2014). On the other hand, product innovation is the introduction of a service or good that is completely new or has remarkably intended uses or improved characteristics (Manyika et al., 2015). Moreover, the innovation of a process is also known as the implementation of a significantly improved or new delivery method or production (Russom 2012). The development or innovation of a particular product is very risky for the manufacturing organization (Zikopoulos and Eaton 2013).

Thus, the researcher is going to interpret as well as demonstrate the role of the Big Data in the product development or product innovation with the help of thorough research in this study. Hence, the significance of the ECRM in product innovation in terms of big data is also aimed to be discussed in this reserach. The researcher is going to make a deepened analysis with the help of a proper literature review and the data analysis made based on the literature review.

The previous trend’s history in the IT innovation as well as investment and its influence on the productivity and completeness suggest strongly that Big Data can have the same potential, namely the capability of transforming people’s lives (Srinivasa and Bhatnagar 2012). The Big Data concept has been around for years. Numerous companies have now understood that if the organizations capture all the information or the data that stream into their businesses, thus they can also make an application of the analytics as well as get significant value from it (Chen, Chiang and Storey 2012). The Big Data mainly helps the companies harness their data as well as use it for identifying new scopes. That leads to the smarter business moves in turn, happier customers, higher profits as well as more efficient operations (Rahm 2016). In this case, Big Data and Customer Behaviour both are interrelated with each other. Therefore, this research has focused on the implementation of this particular relationship. On the other hand, Big Data Analytics play a significant role on ECRM as well as Business Transformation. Therefore, this research has also focused on the impacts of the big data on Business transformation and ECRM. Thus, the principle statement is to find out the impact of the big data on the product innovation.

Research Rationale

At the time of the typical process of product development that begins with the strategizing the design, testing as well as the validation of the product and ends finally with the product being phased out; huge data volume is manufactured (Chen and Chen 2014). Nowadays, the big data usage is becoming an important approach for the leading organizations for outperforming their peers. The new entrants alike as well as the established competitors in most of the industries would leverage the strategies those are drive by data for innovating, competing as well as capturing value (Taylor and Hunter 2013). Big Data would also help for creating new opportunities of growth as well as completely new organization categories like those that analyse and aggregate the industry data point (Marais and Pienaar-Marais 2016). Therefore, in case of product innovation, the big data can play a significant role. On the other hand, the big data can also be significantly applied to the product life cycle of a particular organization. Thus, in order to establish a more clear view regarding the utilities of the big data and ECRM in the product innovation, a thorough and a precise research must be conducted. In other words, this research has been conducted in regards to establish or show evidences to prove the importance as well as essence of Big Data in every ground of business operations especially product innovation. Therefore, this research is going to be conducted in order to accomplish the fact demonstrated above.

This research is mainly aimed to portray the relation between the behaviour of the customers of an organization as well as the Big Data with the help of the proper implementation of the concept of Electronic Customer Relationship Management. On the other hand, the major aim of this particular research is also to illustrate the significant role of the Big Data in the innovation of a particular product, which is going to be launched by a certain enterprise.

There are few major objectives of this particular research on the importance of Big Data in product innovation. These are as follows:

To portray the relation between Big Data (ECRM) and Customer’s behaviour

To implement the role of Big Data in finding Customers Behaviour

To state the role of Big data in product customisation

To identify the role of Big Data in identifying the demand of customers

To establish the role of Big data in making innovative products and services

Research Aim

To recognize the role of Big data in achieving Competitive Advantage

There are some major research questions those are significant and very important to conduct this research. These are as follows:

What is the relationship between the customer’s behaviour and the Big Data (ECRM)

How does Big Data play its role in finding Customers Behaviour?

How does Big Data play its role in product customisation?

How does Big Data play its role in identifying the demand of customers?

How does Big Data play its role in making innovative products and services?

Big Data has huge implications in the IT services or in the product innovation. However, this research has to implement a thorough study in order to specify how the big data impacts the product innovation by illustrating the impact of big data on the Electronic Customer Relationship Management and the Business Transformation of a business industry.

Figure 1: Research Outline

Chapter 1: Introduction – Chapter 1 deals with the establishment of the major purpose to conduct this research. This chapter incorporates the research hypotheses, research objectives that help to continue the study in a technical way. On the other hand, it demonstrates thoroughly the rationale of selecting the subject along with a short description background.

Chapter 2: Literature Review – This chapter includes a detailed explanation of relevant and major concepts, theories and models based on the selected subject of research under consideration.

Chapter 3 Research Methodology: This chapter concerns on the selection of the most suitable and appropriate approach of research for conducting the study. The researcher selects the most suitable research design and techniques for this purpose to yield relevant and useful details that represent the impact of the Big Data on Product Innovation.

Chapter 4 Data Analysis and Interpretations: the gathered data in this chapter is to be analyzed properly in terms of deriving meaningful information that provides a clear picture and fulfils the major objectives of this study.

Chapter 5 Conclusion and Recommendations:  This section concentrates on the conclusion of the study with the entire view of the subject based on the outcomes. Additionally, this chapter incorporates determining and evaluating the success level of the topic and demonstrates the relation with the objectives of this research.

This study interprets as well as demonstrates the role of the Big Data in the product development or product innovation with the help of thorough research. Hence, the significance of the ECRM in product innovation in terms of big data is also aimed to be discussed. Therefore, this chapter has mainly tried to outline the aims, objectives as well as the problems and the research questions of this research with a proper explanation of the research background and the rationale for this research as well.

Research Objectives

Introduction

This particular chapter in this research mainly deals with the literature review on the topic of this research that is the impact of Big Data on the product innovation. Therefore, this chapter has shed light on the discussion on big data analytics. On the other hand, this chapter also states the characteristics of the Big Data Analytics. Therefore, the Big Data storage and management and the ECRM are aimed to be discussed in this particular chapter. Apart from that, the product innovation has been discussed in this research along with the brief discussion of its advantages. At the end of this chapter, the impact of Big Data on the product innovation as well as on ECRM has been discussed in this chapter.

Big Data has been recently applied to datasets that has developed so large that these are becoming awkward in order to work with the usage of the systems of the “conventional database management” (Cameron 2015). They are the data sets, whose size is more than the capability of the commonly utilized storage systems as well as software tools for capturing, storing, managing and process as well the data within the elapsed and tolerable time (Zikopoulos and Eaton 2014). The sizes of the big data are increasing currently and ranging currently from a few dozen TB or tera-Bytes to numerous PB or peta-bytes of data in the single data set. Some challenges are consequently related to the big data incorporate visualization, analytics, sharing, search, storage and capture (Manyika et al., 2015). Companies, in today’s world are more exploring the huge bulk of highly informative data so as for discovering the facts, they did not have any idea before.

Therefore, the big data analytics is the field where the advanced techniques of analytics can be applied on the huge datasets (Russom 2012). The analytics relating to the data leverages samples as well as can reveal the business changes. Nevertheless, the larger the data set, the much harder it becomes for managing (Katal, Wazid and Goudar 2013).

“Big data” is the dataset whose “timeliness”, “diversity”, “distribution” or “scale” require the utility of the new tools of analytics and technology and the architectures as well in regards to enable the views those can easily unlock the value sources of new business (Zikopoulos and Eaton 2013). There are three major features those can characterize the big data such as the velocity, variety as well as volume or the three V’s (LaValle et al., 2014). The data volume is its size as well as how it is enormous. On the other hand, the velocity is referred to the rate with the help of which data is continuously changing (Srinivasa and Bhatnagar 2012). The variety finally incorporates the distinct types as well as formats of data and the various types of the ways as well as the utilities of the data analysis.

Research Questions

The data volume is the major big data attribute. It can also be enumerated with the help of the size in PBs as well as TBs and also the number of transactions, tables, files, records as well (Chen, Chiang and Storey 2012). Moreover, one of the things that have made the big dataset really huge is that it is deriving from a bigger source variance than before, inclusion of social media; click streams and logs as well (Rahm 2016). In analytics, the utilization of the sources refers to that now the commonly structured data is combined by the data, which are unstructured like the human language, text and the data in semi-structured manner like the “Extended Mark-up Language” or “Rich Site Summary” feeds (Zakir, Seymour and Berg 2015). There is data also that is very difficult for categorizing as it has come from video, audio as well as the other devices (Kitchin 2014). Additionally, the data, which have multi-dimensions, can also be retrieved from the warehouse of data in regards to integrate the historic context to the “big data”. Thus, variety is only as huge as volume by “Big Data”.  

In addition, the “big data” can also be illustrated by its velocity as well as its speed. It is actually the frequency of generating data or the frequency of the delivery of data (Talia 2013). The “big data’s” leading edge is the data that is mainly captured in real-time from the internet sources such as websites. Some companies and the researchers have also made a discussion regarding the addition of a veracity or fourth-V. Veracity aims on the data quality (Ghazal et al., 2013). It actually characterizes the quality of the “big data” as “undefined”, good or bad for the “consistency”, “approximations”, “deception, latency”, “ambiguity” as well as “incompleteness” in data.

The first and foremost things are those the enterprises have to manage while working with the “big data” is how and where this data would be captured once it is retrieved (Chen and Chen 2014). The conventional storage methods as well as the retrieval methods of the data incorporate the data warehouses, data marts and the relational databases as well (Taylor and Hunter 2013). These set of data is therefore uploaded to the storage from the stores of the operational data by utilizing Extract, Load Transform, or Extract, Transform, Load tools that can transform the data for fitting the operational requirements, extract the data from the outside sources or the data warehouse (Sathiamoorthy et al. 2013). Therefore, the sets of the data are cleaned, catalogued as well as transformed before being made available for the online analytical functions as well as the data mining functions.

Problem Statement

Nevertheless, the “Big Data” ambience calls for the “Magnetic Agile Deep” (MAD) skills of analysis that is different from the contexts of the old approaches of EDW discourage the inclusion of the sources of the new data until they are integrated as well as cleansed (Richtarik and Takac 2016). The environments of the “big data” require being magnetic due to the current data ubiquity, therefore, attracting all the sources of data regardless of the quality of data. Additionally, provided the growing numbers of sources of data and the data sophistication analyzes, the storage of big data should permit the analysts for producing easily as well as adapting data rapidly (Marais and Pienaar-Marais 2016). It also needs an agile database whose physical as well as logical contents can also adapt with the evolution of rapid data in sync. Since the current data analyses finally utilizes the complicated statistical methods as well as analysts necessary to be capable of studying enormous sets of data by drilling up and down, the repository of big data needs to be deep also and serve as the algorithmic and sophisticated runtime engine (Hampton et al., 2013). Several solutions ranging from the Massive Parallel Processing as well as distributed systems in order to provide the performance of high query and platform scalability to in-memory or non-relational databases have been utilized for the big data.

The Electronic Customer Relationship Management or ECRM includes all of the functions of Customer Relationship Management with the usage of the net environment such as the internet, extranet as well as intranet (Cameron 2015). The ECRM has the concern regarding all the forms to manage the relationships with the customers in terms of making the utility of the Information Technology. It also enterprises with the help of the usage of IT for integrating the resources of the internal organization as well as external strategies of marketing for understanding as well as fulfilling the needs of their customers (Zikopoulos and Eaton 2014). Comparison between the integrated information for the intra-organizational collaboration of ECRM and traditional Customer Relationship Management can also be more effective for communicating with the customers.

The actual meaning of the Customer relationship Management is still the concept of huge discussions. Nevertheless, the entire objective can be seen as the effective management of distinctive relationships with all the customers or the consumers of a particular organization as well as interacting with them individually (Manyika et al., 2015). The underlying thought is that the organizations can also realize that they can also supercharge the profits with the help of acknowledgement that the various groups of the customers widely vary in regards to their responsiveness, desires as well as behaviour towards marketing. The loyal customers of the organizations cannot provide only the operational organizations sustained revenue but advertise also for the new marketers (Russom 2012). Enterprises use the Customer Relationship Management for maintaining the relationship in terms of reinforcing the customer reliance as well as creating the additional customer sources as the general two categories B2C and B2B as well. The Customer Relationship Management implementation should come from the respective point of views because of the behaviours as well as the needs are different between B2C and B2B.

Characteristics	Customer Relationship Management	Electronic Customer Relationship Management
Customer Contacts	In case of CRM, contacting with the customers of a particular organization is made through the fax, phone as well as retail store.	In case of ECRM, all the conventional methods are utilized in addition to the PDA technologies, wireless, and email as well as internet technologies.
System Interface	Customer Relationship Management implements the utility of the ERP systems; emphasis is on the back-end.	Electronic Customer Relationship Management geared more to the front end that communicates with the back-end over the usage of the Enterprise Resource Planning systems, data marts as well as data warehouses.
System Overhead	In CRM, the client has to download several applications for viewing the web-enabled applications.	The Electronic Customer Relationship Management doesn’t have these needs as the client utilizes the browser.
Personalization as well as Customization of Information	The views of Customer Relationship Management differ relating to the audience. On the other hand, the personalized views are not available. The individual personalization needs changes in program.	In case of Electronic Customer Relationship Management, the individual personalized views based on the history of purchase as well as preferences (Lohr 2012). Individual personalization has the capability for customizing view.
System Focus	The Customer Relationship Management System is designed based on the products as well as the job function (Kulkarni, Joshi and Brown 2016). Web Applications are designed for a particular business unit or a single department.	The Electronic Customer Relationship Management system is designed depending on the needs of the customers. Web Applications are designed for the enterprise-wide use.
System Modification as well as maintenance	The Customer Relationship Management is more time involved in the maintenance as well as implementation. It is much more expensive as the system exists at distinct locations as well as on different servers.	In case of Electronic Customer Relationship Management, reduction in cost and time is taken place (Simmhan et al., 2013). Maintenance as well as implementation can take at one server as well as one location.

Research Outline

The Innovation of Product or the Product Innovation is the introduction of the services of goods that is new or has remarkably improved or intended utilities. According to Zikopoulos and Eaton (2013), it is the subsequent introduction as well as the creation of a service or good that is either improved version of the previous goods or the new goods. It is wider than the normally accepted innovation definition that incorporates the new product invention that is still considered as innovative in this context.

There are various examples of the innovation of product incorporating enhanced quality, introducing new products as well as improving its entire performance (Alexandrov et al., 2014). Product innovations along with the process innovation as well as the cost-cutting innovation are the three distinct innovation classifications those are aimed for developing the product methods of a particular organization (Gantz and Reinsel 2012). Therefore, the innovation of product can be segmented into two categories such as racial innovation that aims at the development of a new product as well as the incremental innovation that focuses on the improvement in the existing products.

Advantages

The benefits of the product innovation incorporate,

Expansion, growth and gaining a competitive advantage – A business that is able to differentiate their products from the other businesses of the other companies in the similar industry to a huge extent would be capable of reaping profits (Ohlhorst 2012). It can also be applied to how smaller businesses can utilize the innovation of product for better differentiating their products from the other organizations. The Product differentiation can also be demonstrated as a process of marketing that can showcase the distinctions among products (Crampton et al. 2013). The differentiation also looks for making a product more attractive by making a contrast its authentic qualities with the other competing products. Successful differentiation among products can create huge competitive advantage for the seller, as customers view these products as superior or unique (Cevher, Becker and Schmidt 2014). Thus, small organizations those are capable of utilizing the innovation in products effectively would be capable of expanding as well as growing into the bigger businesses at the time of gaining a competitive advantage over its remaining competitors.

Brand Switching – The organizations those are capable of successfully utilizing product innovation would therefore attract customers from the other competitor brands for buying its product instead as it has become more attractive towards the consumers (Tan et al., 2013).

Summary

There are few disadvantages of product innovation. These are as follows:

Product Innovation’s counter effect – Not all the competitors or businesses don’t always make resources or products from scratch, but rather different substitute resources for creating productive innovation as well as it could also have a reverse effect of what the competitor or business is trying to do (McAfee et al., 2012). Therefore, few of these competitors or businesses could be driven out of the industry as well as would not last long enough for enhancing the products of the organization during that particular time in the industry.

High risk as well as high cost of failure – While a business makes an attempt for innovating its product, it would injects huge amount of capital as well as time into it that needs severe experimentation (Kwon, Lee and Shin 2014). The continuous experimentation can also lead to the failure for the business as well as also would cause the business for significantly incurring higher costs (Wang et al. 2014). Additionally, it can take years for a particular organization for innovating successfully a product, thus lead to an uncertain return.

Disruption to the outside world – The business would have to change the way it runs for the product innovation for occurring as well as it could also lead to break down the relationships between the customers and the business, business partners and the suppliers as well. (Boyd and Crawford 2012) Additionally, too much change in the products of a certain business could also result in the business gaining a less reputable impression for the loss of consistency as well as creditability.

Big Data Analytics hold numerous promises for the organizations, few of which already it is delivering on and few of which yet have to be realized. The Big Data analytics have huge impact on the customer behaviour (Sagiroglu and Sinanc 2013). Especially, the marketers as well as the advertising agencies were aligned largely in order to determine the prime advantages of big data initiatives (Demirkan and Delen 2013). In case of the agencies, 64 % have stated that big data have permitted them for developing an in-depth insight into the experiences of the customers for helping drive useful strategy as well as 63 % of the marketers have said the same (Davenport, Barth and Bean 2012). Marketers as well as the agencies also were on the similar page regarding the advantages of utilizing big data analytics for parsing the feedback from customers as well as recognizing products those they desired for. 50 % of the marketers as well as 52 % of the agencies have seen this as an advantage (Boyd and Crawford 2012).

Big Data plays a significant role in the product innovation or the product development. The utilization of the Big Data for informing the development of a new product has numerous advantages (Sagiroglu and Sinanc 2013). According to Cevher, Becker and Schmidt (2014), Enterprises can make advantages that can link with the customers, give enhanced the customer value, reduce the risks along with the risks with the launch of a new product as well as both coordinate and allocate the utilization of internal research and development resources efficiently. Enterprises can also recognize the needs of consumers through the data mining (Tene and Polonetsky 2012). Enterprises can enhance the customer lifetime value as well as deepen the customer brand engagement by constantly developing products that would accomplish the consumer needs (Kwon, Lee and Shin 2014). The enterprise can also forecast the product performance in the market both the post-launch and pre-launch in near-real-time through the predictive and modelling analytics (Chen, Mao and Liu 2014). The enterprises can also determine the chains of the optimal distribution as well as optimize the marketing strategies in terms of acquiring the greatest number of consumers at the lowest cost with the help of the predictive and modelling analytics.

If once beta testing is done successfully, then the enterprises start to determine the fact how for scaling the manufacturing of the product as well as integrating it into the existing operations (Cevher, Becker and Schmidt 2014). It also incorporates everything from the determination of the optimal suppliers towards the planning of contingency. Companies can utilize the models of optimization for predicting the quality (Boyd and Crawford 2012). Organizations can also use it for yielding account for the variation in the processes of production down to the individual level and machine level as well as outcomes.

The development of product is very risky business. A huge number of new products those have entered in the market fail (Waller and Fawcett 2013). According to McAfee et al., (2012), the organizations are looking to their better odds by utilizing the big data in terms of pinpointing the needs of customers and tailoring the new products. The Big Data analytics refer to the varied as well as large stores of information that the organizations can mine for improving their next generation services and the products as well (Ohlhorst 2012). Product Development and the Big Data Analytics is an analysis of customer sentiment where the organizations can monitor the tweets, social media postings as well as the other online messages for understanding how people think. 

The Big data Analytics can play a crucial role in the process of achieving competitive advantage by a particular organization. The Big Data usage has become an important way to lead the organizations for outperforming their peers (Gantz and Reinsel 2012). The new entrants as well as the established competitors in most of the industries alike would leverage the data driven by strategies for capturing, competing as well as innovating value. Big Data would help for creating entirely new categories of companies as well as new growth opportunities like those that analyse and aggregate the industry data.

After the entire discussion made in this chapter, a significant fact can easily be evaluated that the Big Data Analytics has a huge impact on the product innovation and business transformation as well. Therefore, the impact of the big data on product innovation and business transformation simply result in the development of the innovative products as well as services by an organization. As a whole, it leads to the enhancement of the competitive advantage for an organization.

Figure 2: Conceptual Framework

(Source: Created by Author)

Introduction

Methodology in a particular research is that systematic and theoretical area of a particular research that plays a significant role in order to demonstrate as well as illustrate the suitable method that has to be considered in terms of acquiring the complete and a detailed process requirement (Coleman and Ringrose 2013). The research methodology in a research is defined as the analysis of the principles of postulates, rules as well as the methods those are employed by a discipline (Kumar and Phrommathed 2015). In other words, research methodology is the study of methods done in a systematic way and those are, have been or can be applied within a discipline (Takhar-Lail and Ghorbani 2015). Research Methodology is needed is a research for including a consideration of the theories as well as the concepts that underlie the methods. The proper application of the research methodology results in the exact findings that can also become very useful in order to throw light on the topic of the research. Nevertheless, some disadvantages can be encountered in the data collection method within this particular chapter (Kothari 2014). Otherwise, the research methodology is very useful in the creation of the findings with the help of the research that could be observed with reference to the topic of the research.

The researcher has tried in this particular section of the research methodology chapter for acquiring a deepened as well as thorough viewpoint with the help of positivism approach (Coleman and Ringrose 2013). The positivism concept has been utilized for carrying out this research with the help of the statistical as well as empirical data (Glaser 2014). The most important fact associated with the positivism is that it actually operates with the examining outlook of the data, which are available with the utilities of the emotions of human-beings (Kothari 2014). The positivism procedure is utilized because it is independent as well as more scientific of the thoughts of human-being (Kumar and Phrommathed 2015). The statistical information or the records those are got for understanding the current scenario on the opinion (Marais and Pienaar-Marais 2016). The data or the records are more discrete as well as the data can be based on the subject that can easily be researched only for the determination of the findings of this particular research.

Interpretivism, which is also called interpretivist involves the researchers in terms of interpreting elements of the study, therefore interpretivism also integrates the interest of human being into a particular study (Marczyk, DeMatteo and Festinger 2015). It is such a concept where the human thoughts as well as feelings are utilized that they have (Merriam 2012). In this particular scenario, the theory deals with the utility of the deepened understanding of the sayings of human beings about the topic of the research. The statistical data cannot be utilized in interpretivism as well as it is designed based on the social sharing of the human beings (Mies 2015). There are several specific consequences those could be understood with the data in a tabular form as well as requires the understanding beyond the statistics.

On the other hand, realism is that significant concept in research investigation, which demonstrated from both of the types of investigation such as interpretivism as well as positivism (Murry and Hammons 2015). Realism is the thorough usage of the numerous types of exploration that it can easily be found that few things are exists there without the human being’s knowledge (Neuman 2012). Apart from that, few things can also be there that cannot be analyzed only with the help of the statistical data.

Research Approach is such an approach in the research methodology, which can easily be utilized in terms of understanding the process with the help of which a particular research is to be continued by the researcher (Peffers et al., 2014). A specific research can be understood in research approach’s sub division (Wahyuni 2012). These sub divisions are namely deductive method as well as the inductive method. Any one of the both of research approaches can be utilized or even the research might also require both of the research approaches. The inductive approach in a particular research methodology is utilised for testing the subject with the help of statistical data that has to be gathered in a particular research (Merriam 2012). There can be a conclusion in order to use this segment of the approach that can also be implemented by using the data for forwarding a new opinion of the participants of the research.

While the deductive approach of research is concerned, then it must be stated that this particular research is all about the validation of the information as well as the theories, already those are available in the society. It is such a concept that main aim on the human being’s behaviour in the society. According to Perry (2016), the deductive approach is the utility of the hypothesis that mainly exists in the place of the formulation of the conclusions with the help of the statistical data or information.

The deductive approach in research methodology has been mainly utilized in this particular research study as few important theories are there those can be examined in the subject of this research (Peffers et al., 2014). This particular approach is utilized in this chapter of this study so that it can make a validation of the theoretical understanding with the help of the deductive techniques of research approach. The participants in this particular research have helped in order to make understand the deductive technique of the research approach for coming to the exact conclusion. Several versions are there on the similar thing as it has also been broadcasted by the different researchers in different researches. The utility of the deductive technique of the research approach would also generate the potential base in terms of analyzing the topic of this particular research.

The facts those available previously in this world as well as requires to be examined in regards to several scientific methods are widely known as the positivism philosophy. On the other hand, as per realism, the objects are totally free from the human being’s perception but at the same time are similar objects those are available and are felt by the sense of human beings.

This particular research goes head to head with the deductive research philosophy that is one of the vital causes behind selecting positivism philosophy. The entire analysis is accomplished with the help of the primary as well as secondary data and it is the key reason to select this particular research philosophy.

Design of the research in the research methodology can be segmented into three categories such as descriptive, explanatory as well as the exploratory design of research (Welman, Kruger and Mitchell 2015). The research design also offers a clearer as well as better understanding on the designing of a specific research (Pinsonneault and Kraemer 2014). The exploratory design of research is such a type of research where there are new possibilities those can be researched. In the same there is no any well-maintained hypothesis. The researcher has explored in terms of finding the research hypothesis in this study (Reigeluth and Frick 2012). It is stated in case of the explanatory that some occurring is there in the society those are repetitive in nature as well as so that it is also obtained at any point of time.

Figure 3: Research Design Classification

(Source: Scandura and Williams 2014, pp.1250)

The reason to use the descriptive approach of the research design in this particular research is that this research design approach is mainly utilized for getting the detailed and complete concept for utilizing the Big Data technologies or ECRM in the product innovation (Murry and Hammons 2015). With the help of the descriptive approach of the research design, it can easily be rightfully analyzed that the IT organizations are using the Big Data technologies in order to make innovative products as well as services.

Sampling is such a technique that can help in an essential basis iin order to find the avenues for reaching the participants who have taken part in a particular research. There are mainly two approaches in which the techniques of sampling could be segmented widely. These segments are the non-probability as well as probability techniques. In this research, the probability technique has been utilized. In the probability sampling that has been conducted in this current research, a proper random technique has been utilized for carrying out the research on the employees of several IT organizations. There are mainly 50 employees from various IT organizations have been selected with the help of the simple random method of sampling. This particular procedure that has been utilized is very much cost effective as well as a feasible option within the time.

The data collection methods are such techniques those are utilized in terms of continuing the research with the help of a specific tool of data collection approach. There are mainly two important as well as essential divisions or segmentations of the data collection methods have been selected such as the quantitative data collection and qualitative data collection techniques.

In case of the quantitative data collection, 50 employees of various IT companies have been selected for carrying out a survey. Therefore, a set of questionnaires has been sent via online medium as well as the questions in the survey are close ended. The employees of numerous IT companies are provided the probable options with all of the questions in the survey those are required for carrying out the research as well as for using the similar thing in order to analyze further. The statistical results those are gathered are utilized for putting forward the study or the research on an in-depth basis of understanding the subject. On the other hand, in case of the secondary research, several themes have been implemented by collecting several secondary data from various peer-reviewed journals, articles as well as online websites on the big technologies.

The data those have been gathered in this particular research on big data are related to the literature review. The gathered data from survey that has been conducted among the employees of various IT organizations, who are working on Big Data, have been formulated into tables as well as charts. These tables and charts have been utilized for understanding the subject in a better manner with the help of the survey. The percentages that have been got from the gathered data has been utilized for continuing he topic of the research as well as understanding the opinions of the employees who have participated in the survey regarding the individual questions. On the other hand, a secondary research has been done with the help of the secondary data those have been collected from various secondary sources such as the peer-reviewed journals as well as articles and various online websites with big data information and the company websites whi utilize big data technologies.

While conducting the survey of the employees of several IT companies who work with the big data technologies, the researcher has utilized the online medium for getting as access to them. Nevertheless, the employees also had to be reminded time to time for filling the questions in the survey so that the data can be segregated in a tabular form within the time frame that is constrained. In case of the secondary research, the validity of the information or the data collected from several secondary sources has been checked. All the recent secondary data have been collected to implement or conduct the secondary research. This checking has been done in order to gather authentic and correct information regarding the topic of the dissertation.

During the process of research methodology, a researcher needs to follow a code of conduct that helps in identifying the wrong and right set of behaviours required to be adopted during the process (Toloie-Eshlaghy et al. 2011). The researcher for analysing the role of branding in customers’ decision-making process tried to follow few ethical considerations that can help in adding standardisation to the research topic.

Data Application: Data gained via study of the topic is helpful in understanding the present trend of consumers’ buying behaviour and decision-making process with special reference to Primark. However, any commercial application of the data will be avoided so that the findings can be strictly limited to academic purpose only.

Respondents’ Involvement: The researcher tried to insert no external influence on pressure over the respondents for taking part in the feedback process of the research topic. Respondents with a sense on voluntary participation were encouraged to participate in the following research topic.

Respondents’ Anonymity: It was ensured that any form of mental or physical harassment was not involved with the respondents so the identities of the respondents were concealed as per the requests of the participants.

Based on the above-mentioned list of ethical considerations, the researcher tried to maintain the basic research ethics.

Main activities	1st week	2nd week	3rd week	4th+5th week	6th week	7th week
Selection of topic
Literature review and study of existing theories on the research topic
Research methodology
Data collection- primary and Secondary
Analysis of data as well as interpretation
Findings
conclusion
Final work and submission

(Source: Created by Author)

Introduction

In this particular chapter of this research, data analysis has been done with the help of both of the secondary research or with the help of the secondary data collection method. Therefore, in case of the secondary research done in this chapter, data have been collected from several peer-reviewed articles, journals as well as various online resources such as several company websites based on the Big Data. Thus, with the help of the entire analysis on the collected secondary data, the evidences of the significance influence of the Big Data in the business operations have been evaluated.

Theme 1 – Popularity of Big Data in Digital

The Big Data has become one of the important as well as the most popular software tool in the IT industry along with many other industries. Most importantly, this popularity of this particular software is increasing day by day as its advanced features can help a particular business to achieve huge success (Alexandrov et al. 2014). In case of the manufacturing applications, the Big Data Analytics is marketed as the 5C architecture which is comprised of configuration, cognition, cyber, conversion as well as connection. The Big data simply allows a company for shifting its aim from the centralized control to the shared model for responding towards the change in the information management dynamics (Bettencourt 2014). In today’s digital world, Big Data has widely enhanced the demand of the specialists of information management in that Dell, HP, EMC, SAP, Microsoft, IBM, Oracle Corporation and Software AG have spent more than $15 billion on the software organizations who are specializing in data analytics as well as management. This particular industry was worth in 2010 more than $100billion as well as was developing at almost 10% in a year. Big data has become very popular in several fields of operations. The developed economies or the developed countries are using increasingly the technologies those are data intensive (Boyd and Crawford 2012). The big data has huge applications over the governmental processes, international development processes, manufacturing operations, cyber-physical models, healthcare, educational activities, media, Internet of Things, private sector and especially the information technology operations. Big Data has come for prominence as a tool within the business activities for helping the employees for working more efficiently and effectively as well as streamlining the distribution and the optimization of Information Technology especially since 2015. The big data utility for attacking data collection as well as Information Technology issues within an organization is called IT Operations Analytics. The Information Technology Departments can predict the potential consequences as well as move to give the solutions before the issues even happen with the help of the application of the principles of Big Data into the concepts of deep computing as well as the machine intelligence.

Findings –

It can be proven with solid evidence that the Big Data has gain huge popularity in the business world. The Big Data analytics refer to the varied as well as large stores of information that the organizations can mine for improving their next generation services and the products as well. Big Data is becoming popular in the business world simply because the big data analytics can help the companies for harnessing their data as well as using it to recognize the new opportunities.

Theme 2 – Role of Big Data in Product Customization

Product customisation is such a process to deliver wide-market services and goods those are modified for satisfying the specific needs of the customers. Big Data can efficiently handle product customization, as this technology takes a significant role in the product development and innovation as well. The role of Big Data can be significantly identified in the product customization in the aspect of the big data application in the retail industry (Chen, Chiang and Storey 2012). Big Data analytics are giving ideal for the retailers. Any organization who offers consumer goods can easily harness the big data in terms of improving operations, streamlining the supply chain, managing inventory, adjusting pricing as well as building better programs of marketing. On the other hand, the retailers all over the world are increasingly utilizing big data for the customization of products (Chen, Mao and Liu 2014). Big Data can also play a significantly role in order to improving the appeal as well as design of customers. The big data can be utilized for predicting consumer behaviour for the existing products. For an instance, few fitness brands are utilizing the smart-phone apps for helping the consumers with their workouts as well as gathering data for helping with the marketing and design of the product. The smart retailers can offer the options of real-time shopping based on previous purchases for e-commerce. Therefore, Big Data is the perfect tool for developing the customer’s 360 degree perspective.

Findings –

It can be proven with solid and proper evidence that the Big Data can play a significant role in the product customization. This is because, with the help of the big data, the product customization can help the brands for boosting sales on their own websites or gaining share on the site of the retailers.

Theme 3 – Role of Big Data in recognizing the customer requirements

For the businesses, the real value or the impact of the Big Data is actually the scope or the opportunity for learning about their consumers at such speed as well as depth that people can put truly them at the centre stage. The Big Data can hinder or help us on the way to the centricity of the customer. People have the means of analyzing as well as capturing much larger amounts of data than ever before and for making the meaningful linkages among several kinds of it. Big Data Analytics is nothing but the trending practice that several organizations are adopting (Chen and Chen 2014). The process of Big Data Analytics incorporating the use as well as the deployment of the big data analytics tools can help the organizations for improving operational efficiency, gaining competitive advantage and driving new revenue over the business competitors or the rivals, by recognizing as well as understanding the requirements of the customers of a particular organization.

Findings –

It can be proven with solid and proper evidence that the Big Data technologies play effective as well as very significant role in order to recognize the requirements of the customers. It has been seen from the above discussion that the Big Data can significantly help in terms of creating the new opportunities in regards to the development of a business as well as completely the new organizations like those that can analyse and aggregate the industry data. On the other hand, it can also be stated that the Big Data Analytics can recognize the innovative scopes in the major roles, functions and processes within can help the organizations to achieve the competitive advantage in the business industry.

Theme 4 – Role of Big Data in achieving competitive advantage for an organization

The Big Data technologies can play an effective role in order to achieve huge competitive advantage. Business Intelligence as well as the big data tool is very crucial for the organizations as well as to the executives especially. On the other hand, Business Intelligence requires big data and analytics in terms of accomplishing its mission (Chen, Alspaugh and Katz 2012). Big data analytics have become the major basis of competition as well as underpinning the new waves of consumer surplus, productivity growth and innovation. The idea of business value, which is created by data, is not new in this digital world. Nevertheless, the effective utility of the data has become the basis of competition (Crampton et al. 2013). Big Data can easily and fundamentally change the way through which the businesses can operate as well as compete. The organizations that invest in as well as derive successfully the value from their data would have different advantage over their rivals or the competitor business organizations. The big data can evaluate a performance gap that would continue for growing as more relevant data is formed, digital channels as well as emerging technologies offer better delivery and acquisition techniques and the technologies that makes enable easier and faster the analysis of data continue to grow.

Findings –

The Big Data technologies can play effective as well as very significant role in order to achieve competitive advantage for an organization. On the other hand, it can also be stated that the Big Data Analytics can recognize the innovative scopes in the major roles, functions and processes within can help the organizations to achieve the competitive advantage in the business industry.

Theme 5 – Influence of Big Data adoption in product innovation in small enterprises

The enhancing or the key focus or the aim on the big data as well as the potential of the big data for impacting almost every sector of industry as well as provides it the edge that is to be observed as the new resolution for the companies (Davenport, Barth and Bean 2012). The Big Data has taken a significant role in order to change the rules of generating new scopes, challenges for the small and medium enterprises, business operations, commerce as well as the customer satisfaction. The Big Data is not only for the large businesses with the huge amount of budgets. Nowadays, the small businesses can also reap the advantages of the huge quantity of the offline as well as online information for making data-driven and wise decisions for growing their businesses (Demirkan and Delen 2013). However, most of the discussions on the Big Data concern the organizations that have all the resources for hiring the data scientists and research enterprises. There are various ways with the help of which the small businesses can easily analyze, collect as well as make sense of data that they already have. Businesses all over the world are mining successfully the Big Data Analytics. Thus, these businesses are cross-referencing their histories of internal information-pricing, the traffic patterns of customers with several outside sources for increasing revenue by making better understand the behaviour of the customers and reducing costs by removing human bias and inefficiencies. Therefore, big data is not only for the big businesses but it is also very important in the small businesses (Gandomi and Haider 2015). There are several big data solutions such as, ClearStory Data, Kissmetrics, InsightSquared, Google Analytics, Tranzlogic and few others can be effective for the small companies with the help of which the small companies can move forward with their business operations.

Findings –

It can be proven with solid evidence that the adoption of the Big Data can significantly influence the product innovation in the small as well as medium organizations. The Big Data analytics refer to the varied as well as large stores of information that the organizations can mine for improving their next generation services and the products as well (Lyon 2014). Product Development and the Big Data Analytics is an analysis of customer sentiment where the organizations can monitor the tweets, social media postings as well as the other online messages for understanding how people think. The discussion made above has underlined the opportunities as well as importance for the SMEs in terms of investing in the Big Data Analytics for improving their foundation of strategy formation and the decision making.

Theme 6 – Role of Big Data in Product Innovation

The utilization of the Big Data for informing the development of a new product has numerous advantages (Laurila et al. 2014). Enterprises can make advantages that can link with the customers, give enhanced the customer value, reduce the risks along with the risks with the launch of a new product as well as both coordinate and allocate the utilization of internal research and development resources efficiently (Kim, Trimi and Chung 2014). The enterprise can also forecast the product performance in the market both the post-launch and pre-launch in near-real-time through the predictive and modelling analytics (Guzzo, Nalbantian and Parra 2014). The enterprises can also determine the chains of the optimal distribution as well as optimize the marketing strategies in terms of acquiring the greatest number of consumers at the lowest cost with the help of the predictive and modelling analytics.

If once beta testing is done successfully, then the enterprises start to determine the fact how for scaling the manufacturing of the product as well as integrating it into the existing operations. It also incorporates everything from the determination of the optimal suppliers towards the planning of contingency (Gandomi and Haider 2015). Companies can utilize the models of optimization for predicting the quality (Moniruzzaman and Hossain 2013). Organizations can also use it for yielding account for the variation in the processes of production down to the individual level and machine level as well as outcomes. The development of product is very risky business. A huge number of new products those have entered in the market fail. Therefore, the organizations are looking to their better odds by utilizing the big data in terms of pinpointing the needs of customers and tailoring the new products (Chen, Alspaugh and Katz 2012).

Findings –

It can be proven with solid evidence that the Big Data plays a significant role in the product innovation or the product development. The Big Data analytics refer to the varied as well as large stores of information that the organizations can mine for improving their next generation services and the products as well (Lyon 2014). Product Development and the Big Data Analytics is an analysis of customer sentiment where the organizations can monitor the tweets, social media postings as well as the other online messages for understanding how people think. 

Theme 7 – Role of Big Data in Electronic Customer Relationship Management

Big Data is very important and essential in the business transformation of a particular industry. As per the business transformation aspect, big data can expand customer intelligence as well as it can also improve the operational efficiencies (Hu et al. 2014). On the other hand, big data also helps in the evaluation of the implementation of the new business processes. Apart from that Big Data CRM is the practice of the integration of the big data into the processes of Customer Relationship Management with the objectives of the improvement of the customer services by calculating the return on investment on several initiatives as well as the prediction of the clientele behaviour (Bettencourt 2014). In general, organizations mostly struggle for making sense regarding big data as of its speed, sheer volume in which it is gathered and the huge content variance of content it encompasses (Dong and Srivastava 2013).

Findings –

Big Data combines the internal Customer Relationship Management with the customer sentiment data that have an existence outside of the existing system of a company like on the networks of social media (Dittrich and Quiané-Ruiz 2012). With the help of the combination of the big data with the other data of Electronic Customer Relationship Management data can make an improvement on the customer analysis as well as results in the predictive models and the other practices. Therefore, with the help of these evidences, it can easily be stated that the big data plays an effective role on Electronic Customer Relationship Management.

Theme 8 – Role of Big Data in Business Transformation

As per the business transformation aspect, big data can expand customer intelligence as well as it can also improve the operational efficiencies (Kambatla et al., 2014). Apart from that, the combination of the mobile applications as well as the big data is equivalent to the new processes of businesses (Tene and Polonetsky 2012). In case of the business transformation, big data can increase the time consistency for the businesses (Lyon 2014). In case of the business intelligence expansion, the organizations utilize the big data in terms of identifying its most valuable existing or new customers (Moniruzzaman and Hossain 2013). On the other side, Big Data can finally forge the last connections of the value chain that would help the organizations for driving more operational efficiencies from the current investments, which can easily help in business transformation (Chen, Alspaugh and Katz 2012). Apart from that, the mobility would accentuate the impact of the big data on business transformation as well as the customer intelligence by implementing everything actionable on an immediate basis (Gandomi and Haider 2015).

Findings

The above analysis has clearly interpreted that the Big Data is very important and essential in the business transformation of a particular industry. Therefore, with the help of these evidences, it can easily be stated that the big data plays an effective role on business transformation of companies. On the other hand, big data also helps in the evaluation of the implementation of the new business processes.

Conclusions and Recommendations

Conclusion

This particular chapter in this research has tried for bringing out the clear as well as precise observations those have been observed with the statistical or the primary data collected from the survey conducted among numerous employees of different organizations and the secondary data collected from various peer-reviewed journals, articles and online websites. The objectives of the topic of this research were framed based on the subject as well as data gathered based on the similar thing. In the primary as well as the secondary research, it has been observed that the big data has huge impact in several fields of operation in corporate world. As an overall conclusion, it can be stated that this research has easily implemented the fact that the big data has huge impact on the product innovation, business transformation as well as in Electronic Customer Relationship Management.

Linking Objective with Conclusion

Research Objective

To portray the relation between Big Data (ECRM) and Customer’s behaviour

To portray the relation between Big Data and Customer’s behaviour – Big Data and the Customer Behaviour are interrelated with other. Big Data Analytics hold numerous promises for the organizations, few of which already it is delivering on and few of which yet have to be realized. As per the literature review, the Big Data analytics have huge impact on the customer. Especially, the marketers as well as the advertising agencies were aligned largely in order to determine the prime advantages of big data initiatives. Therefore, the primary research as well as the secondary research conducted in the data analysis section has given enough evidence in order to validate this particular objective of this research.

To implement the role of Big Data in finding Customers Behaviour – Big Data plays an important role in order to find the customer behaviour. The Big Data also helps to understand the customers of a particular business organization. It can be done by Big Data by teasing out the behaviour trends as well as the demographic trends that can correlate with the best customers of an enterprise. Thus, the above conducted primary as well as secondary research conducted in the data analysis section has given enough evidence in order to validate this particular objective of this research.

To state the role of Big data in product customisation – Big Data also plays a significant role in the customization of the products developed by an organization. As per the literature review, Product customisation is such a process to deliver wide-market services and goods those are modified for satisfying the specific needs of the customers. However, Big Data can efficiently handle product customization, as this technology takes a significant role in the product development and innovation as well. Therefore, the above conducted primary as well as secondary research conducted in the data analysis section has given enough evidence in order to validate this particular objective of this research.

To identify the role of Big Data in identifying the demand of customers – The applications as well as the features of Big Data are very effective in order to recognize the requirements of the customers of a particular business organization. This is because; according to the literature review, the big data technologies are capable enough to implement the products or services as per the customer demands regarding that particular product and service. Therefore, the above conducted primary as well as secondary research conducted in the data analysis section has given enough evidence in order to validate this particular objective of this research.

To establish the role of Big data in making innovative products and services – The Big Data can also play a significant role in order to develop the innovative products as well as services of a particular organization. The Big Data usage would strengthen the new waves of the productivity growth of a particular organization. This particular fact has been portrayed with the help of the responses got from the participants who have taken part in the survey. This is because, with the help of the inclusion of Big Data in product development reduced risk, cost reduction and greater operational efficiency can be ensured. Therefore, the above conducted primary as well as secondary research conducted in the data analysis section has given enough evidence in order to validate this particular objective of this research.

To recognize the role of Big Data in achieving Competitive Advantage – The Big data Analytics can play a crucial role in the process of achieving competitive advantage by a particular organization. According to the literature review, the Big Data usage has become an important way to lead the organizations for outperforming their peers. The new entrants as well as the established competitors in most of the industries alike would leverage the data driven by strategies for capturing, competing as well as innovating value. Big Data would help for creating entirely new categories of companies as well as new growth opportunities like those that analyse and aggregate the industry data. Therefore, the above conducted primary as well as secondary research conducted in the data analysis section has given enough evidence in order to validate this particular objective of this research.

After the entire discussion made in this research, few recommendations can be implemented or illustrated to resolve some issues those may be encountered while incorporating big data in any organizations in the product innovation. These are as follows:

From the entire research, it can be stated that the scalability of the big data can become a major issue in product innovation as well as in Electronic Customer Relationship Management.  Therefore, an attempt should be considered by the organizations to overcome the issues or the consequences encountered due to the scalability problems of Big Data.

On the other hand, there are very few people who know the utility of the big data. Therefore, it would become a critical issue for an IT organization, if its employees do not know the proper usage of Big Data. Thus, the productivity would be hampered. Therefore, the company should take the initiative to properly train their employees so that they could be able to work with big data very effectively.

The prime as well as the most important limitation of this current research is that the time constraint as well as small size of sample. Due to the time constraint there is a cross-sectional research was carried out. On the other hand, in case of the secondary research, the validation of the information gathered from several secondary sources of data can be a question while the sources are not recent. On the other hand, the research could have been more concrete by conducting the longitudinal research. With the participation of more employees the authenticity can be more ensured for this particular research.

As per the scope for future research, the findings of this current entire research can be easily determined by utilizing the qualitative analysis in the future researches. The similar research can also be conducted in the other industries such as the hospitality, retail industry and the other industries as the Big Data can play a significant role in the Electronic Customer Relationship Management, Customer Relationship Management as well as in the product innovation.

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