Research methods and methodologies in management

CONTENT

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Introduction

Chapter 1 Big data in digital marketing

1.1. Big data and advertising analytics

1.2. Framework overview

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1.3. The problems of modern digital marketing in FCMG

Chapter 2 Moving to effective digital marketing

2.1. Implementing standards of digital media buying

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2.2. Verification of targeting accuracy

Conclusion

Literature

Introduction

Big Data is one of the fastest growing areas of information technology, according to statistics, the total volume of received and stored data doubles every 1.2 years. Between 2012 and 2014, the number of data transmitted monthly by mobile networks increased by 81%. According to Cisco estimates, in 2014 the volume of mobile traffic amounted to 2.5 exabytes (a unit of measurement of the amount of information equal to 10^18 standard bytes) per month, and in 2019 it will be equal to 24.3 exabytes. Thus, Big Data is already a well-established sphere of technology, even though it is relatively young, it has become widespread in many areas of business and plays an important role in the development of companies.

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This paper studies how the emergence of big data is driving the adoption of broader and increasingly sophisticated quantitative analysis techniques across media channels by large, medium and even smaller sized firms. The era of big data is underway. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by, and about people, things, and their interactions [4] (Boyd and Crawford, 2012). Experts in the field of studies of Economics believe that further progress will be closely linked with the widespread introduction of mathematical and economic methods and models. If previously dominated by qualitative analysis, today revealed the quantitative regularities and mathematical models of many economic phenomena and processes. In modern conditions even, an experienced Marketer is not always able to detect and objectively compare the advantages and disadvantages of different solutions, so modeling marketing mix can reduce the level of harmful consequences. The relatively low cost of modeling allows to replicate the “economic storm”, while saving millions and even billions of rubles. Today in the field of marketing there is a serious competition for customers. In this connection, it’s obligatory to optimize marketing strategy basing on collected Big Data to offer each customer as much personalized and consistent experience as possible.

The goal of this work is to study the ways of using Big Data in digital marketing to solve difficulties of building FCMG brand in digital.

Among the tasks of the paper are the following:

• studying the current state of the Big Data framework in marketing;
• Identification of problem areas of digital marketing;
• Finding solutions for problem areas using Big Data;
• Making recommendations for FMCG brands on using big data

The object of the research is the influence of digital marketing channels on brand health indicators

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This paper will discuss the theoretical basis for the use of big data in the marketing strategy of the company. In the first chapter the ecosystem and the key problems faced by most players in the market will be discussed. The definition of big data will be considered. The second Chapter describes the methods of verification of marketing strategy and the quality of investments in digital channels of promotion, as well as key indicators for assessing the implementation of the strategy.

Chapter 1                  Big data in digital marketing

1.1.              Big data and advertising analytics

Nowadays, the concept of “Big data” does not have a generally accepted definition. In the open online encyclopedia «Wikipedia» the following definition is given: «big data – is the designation of structured and unstructured data of huge volumes and significant diversity, effectively processed using scale-out software tools that appeared in the late 2000s and alternative to traditional database management systems and solutions of the business intelligence class». In a recent article, Lev Manovich, Professor of computer science at New York University, says that the concept of “Big data” was previously used in Sciences to refer to data sets large enough to use supercomputers. However, with the growth of PC performance-the situation has changed. Now the same data sets can be processed on personal computers by means of standard software. ” There is no doubt that the amount of data available is often quite large, but this is not a defining characteristic of this new data ecosystem [10]. Paul Zikopoulos, together with a team of researchers from the international company IBM comes in his book entitled “Understanding of Big Data” to the following idea: «Big data is a bit wrong, because it means that the previously existing data is somehow small (it’s not so) or that the problem is a clean one (Size is one of them, but often more.). Thus, the term “Big Data” should refer to data that cannot be processed or analyzed by using traditional processes or tools.”. The most common academic definition of Big data is the definition of Dana Boyd and Kate Crawford. They define big data as a cultural, technological and scientific phenomenon, which is based on the interaction of three factors:

1. Technology: maximize the computing power and algorithmic accuracy to gather, analyze, link and compare large data sets.

2. Analysis: use of large amounts of data to identify patterns in support of economic, social, technical and legal requirements.

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3. Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insight that was previously impossible with an aura of truth, objectivity, and accuracy [4] (Boyd & Crawford, 2012).

Based on the definition of Big Data, the following basic principles of working with such data can be formulated:

1. Horizontal scalability. Because there can be as much data as you want – any system that involves processing large data must be extensible. The amount of data has doubled – the amount of iron in the cluster has doubled and everything has continued to work.

2. Fault-tolerance. The principle of horizontal scalability implies that there can be many machines in a cluster. For example, Hadoop cluster Yahoo has more than 42,000 machines (at this link you can see the size of the cluster in different organizations). This means that some of these machines will be guaranteed to fail. Big data techniques should take into account the possibility of such failures and deal with them without significant consequences.

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3. The data locality. In large distributed systems, data is distributed across a large number of machines. If the data is physically located on one server and processed on another, the data transfer costs may exceed the processing costs. Therefore, one of the most important design principles of BigData-solutions is the principle of data locality-if possible, process the data on the same machine on which they are stored.

All modern big data tools follow these three principles one way or another. In order to follow them – it is necessary to come up with some methods, methods, and paradigms for developing data development tools.

Big data popularization is a trend that is officially supported by W3C for many years through the support of the so-called Semantic Web. “The semantic web is a collaborative movement led by international standards of The World Wide Web Consortium (W3С). The standard promotes common data formats on the World Wide Web. By encouraging the inclusion of semantic content in web pages, the semantic web aims to transform the existing network, dominated by unstructured and semi-structured documents, into a “data network”. The semantic web is based on the Framework Description of the W3C resource consortium (RDF)” (from W3C, 2011). There is confusion between the terms “semantic web” and “web 3.0”. According to one of the leading technology bloggers Akhilesh Sharma, the “semantic web” is sometimes appropriate, used as a synonym for “Web 3.0”, though the definitions of these terms differ (Sharma, 2011).  The importance of the semantic web and/or web 3.0 in the development of big data is hard to deny. In modern times, the technology itself seems to be evolving faster than its clear definitions, such as big data and its applications to business and society. The impact of big data spreads much wider than a PC or even a smartphone. Paul Zikopoulos and his team of researchers at IBM state the following: “Simply put, the era of big data today is in full force because the world is changing. With instrumentation, we can feel more things, and if we feel it, we tend to store it (or at least some of it). Advances in technology have made people and things increasingly interconnected-not only part of the time, but all the time.  The level of interconnectedness is a runaway train ” (Zikopoulos et al., 2011). With the lightning-fast spread of big data, an increasingly important business task is to learn how to use IT to improve marketing efficiency. One solution is Predictive Analytics. Predictive Analytics is not new, as it is widely used in the fields of public health, environmental protection, and national security. Predictive Analytics is currently being applied to integrated marketing communications (IMC), which leads to more spread of media on the Internet. According to recent reports from Duke University and the society of digital agencies (SoDA), advertisers are transferring significant budgets from traditional media advertisers to various online channels. According to TNS Mediascope, the volume of the advertising market on the Internet in November 2017 for the first time compared with the volume of the television advertising market in Russia.

Corporate clients require greater accountability and detailed measurement of the impact of their advertising campaigns, regardless of the form of application. Historically, online advertising has been used primarily as a means to accelerate direct transactions with customers and to a lesser extent as a tool for brand building. This trend is changing and the movement towards the use of online advertising in brand order will shift a higher percentage of their advertising budgets on Internet sites. The Nielsen online advertising report in Outlook 2013 confirms this view of digital media as channels of brand development:

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“Digital media continues to develop as a branding medium, moving beyond its roots as a channel of interest solely to direct response marketers. Today, it seems that branding in the online environment appears to have reached adulthood, because the costs of brand advertising on the Internet in 2013, according to forecasts, they will compete with the advertising direct response. Moreover, growth projections for branding surpass the predictions of its brothers and sisters on the basis of performance” (Nielsen, 2013).

Advertisers, for the most part, still work in the “swim lane” mode to assess the effectiveness of the advertising campaign. This means that the performance of each media channel is evaluated as a separate silo or “swim lane”. Thanks to the emergence of powerful big data analysis technologies, measuring the impact of one media channel on another (the concept of “assist power”) is becoming increasingly realistic. For example, you can measure the contribution of television advertising to social media communication. As online takes on an increasingly important role in the brand media mix, intuitive performance measurement will be more critical. There are tools for analyzing the performance of traditional media, such as a television in an integrated form with the performance of an online channel. An excellent example is the Nielsen Cross-Platform rating service, which was released in the United States in October 2012 (Nielsen, 2013). In addition, the increase in computing power and the ongoing standardization of the format data in the web using semantic web agreements facilitates the acquisition of data related to traditional use (Blomqvest, 2014). The above trends in the competitive marketing environment lead to the emergence of an advertising strategy called Advertising Analytics 2.0. Wes Nichols, co-founder, and CEO of MarketShare based in Los Angeles global forecast and analysis company says: ” The days of correlating sales data with a few dozen discrete advertising variables are over. Many of the world’s biggest companies are now deploying analytics 2.0, a set of capabilities that can chew through terabytes of data and hundreds of variables, in real time, to reveal how advertising touch points interact dynamically. The results: 10% to 30% improvements in marketing performance” (Nichols, 2013). The results of the Economist Intelligence Unit survey confirm the positive effect of Big data implementation. 46% of companies claim that they have improved customer service by more than 10% with the help of Big Data technologies, 33% of companies have optimized stocks and improved productivity of fixed assets, 32% of companies have improved planning processes.

1.2.              Framework overview

The first finding uncovered in this study is that large investments have been made in big data start-ups in the past several years. This prompted a search for recent merger and acquisition news. More than 16 acquisitions of privately financed start-ups that have taken place in the past 4 years with 10 acquisitions (62.5%) occurring in 2014.  Clearly, investment activity is active and picking up speed, as seen in Table 1. The term big data investor seems appropriate for firms in this first typology.  These firms are investing billions of dollars in big data.

Table 1 Big Data Investors

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