The Use of Big Data in Marketing Analytics

The Use of Big Data in Marketing Analytics

Chai-Lee Goi (Curtin University, Malaysia)
DOI: 10.4018/978-1-7998-4984-1.ch004
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Abstract

Big data has broken through the public imagination, has revolutionised the process through which business find innovative ways, and has transformed the data into valuable information that will shape business intelligence and gain business insights to make better decisions. The purpose of this study is to review the development of big data, architecture, and the use of big data in marketing analytics. From the analysis of literature reviews, a big data in marketing analytics model has been proposed. In using big data in marketing, marketers need balanced analytics and then identify opportunities for improvement based on reporting or analysing past and present big data to predict and influence the future.
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Introduction

Big data analytics have been embraced as a disruptive technology that will shape business intelligence and to gain business insights to make better decision making (Fan et. al, 2015). The term of big data has been first used in 1989 by Erik Larson (Marr, 2015) and then being popularised by John R. Mashey in 1990s (Mashey, 1998). Big data is not just about the buzzword, it is a movement (Sarjana & Sanjana, 2013). Big data analytics has been around since 1663, when John Graunt dealt with overwhelming amounts of information using statistics to study the bubonic plague (Foote, 2017). Our current lives are filled and surrounded by all kinds of data and this data never sleeps (Domo, 2019). Chen et. al (2012) commented that it is also a step forward ‘from big data to big impact’. Big data has broken through the public imagination (Baer, 2013), has revolutionised the process through which business find innovative ways (Baig et. al, 2019), and has transformed the data into valuable information that could make the difference between business success and failure (Kauffmann et. al, in press).

In marketing, as discussed in New Gen Apps (2017), big data can be used for several purposes. This includes market identification, trend analysis, understanding the consumer, markdown optimisation, market prediction, measuring influencers’ impact and improving cross-selling. Customer Analytics, operational analytics, fraud and compliance, new product and service innovation, and enterprise data warehouse optimisation are use cases in sales and marketing. Also, Customer Value Analytics (CVA) is making it possible for marketers to deliver consistent omnichannel customer experiences across all channels (Columbus, 2016). Lozada et. al’s study (2019) discussed the use of big data and its incorporation into the core of the processes associated with innovation management and along with the strategy, including value co-creation initiatives. To contribute to digital marketing success, Saran (2018) highlighted that big data helps marketers to design better marketing campaigns, to have better pricing decisions, and to show appropriate web contents.

Marketing analytics is the ticket to better decisions and stronger results, however many marketers still struggle with shoring up that foundation. When it comes to making the most of data, marketers must get the information in order if they want to turn insights into action (Carey, 2017). The major problems and challenges of big data are handling of data; security, privacy and regulation; lack of skilled staff; technology development is too fast; and financial resources. The summary of these challenges can be found in Table 1. There is a need for systematic planning in big data to be used in marketing analytics. Thus, the purpose of this study is to review the development of big data, architecture and the use of big data in marketing analytics. From the analysis literature reviews, a model will be proposed in relation to the big data in marketing analytics.

Table 1.
Problems and challenges of big data
Problems and challenges
Handling of data12345
  Handling a large amount of data/ Complexity of managing data qualityÖÖ
  Real-time can be complexÖ
  False equivalencies and biasÖ
  Data silosÖ
  Inaccurate data/ Reliability and validityÖ
  Finding the signal in the noiseÖ
  Tricky process of converting big data into valuable insightsÖ
  Troubles of upscalingÖ
Security, privacy and regulation
  Data security/ Vulnerability to security breaches and unauthorized accessÖÖÖ
  Legal and ethical concernsÖ
  Lack of international standards for data privacy regulationsÖ
Skilled staff
  Shortage/ Lack of skilled staffÖÖ
  Insufficient understanding and acceptance of big dataÖ
Technology development
  Technology moves too fastÖ
  Confusing variety of big data technologiesÖ
Financial resources
  Big data adoption projects entail lots of expensesÖ

Key Terms in this Chapter

Extensible Markup Language (XML): Extensible Markup Language (XML) is used to describe data. The XML standard is a flexible way to create information formats and electronically share structured data via the public Internet and corporate networks (Doszkocs et al., n.d.).

Big Data: Big data refers to the situation when the dataset exhibits several characteristics, such as high volume, high variety, and high data processing velocity ( Choi et al., 2018 ).

Marketing Analytics: Marketing analytics involves collection, management, and analysis such as descriptive, diagnostic, predictive, and prescriptive of data to obtain insights into marketing performance, maximize the effectiveness of instruments of marketing control, and optimize firms’ return on investment ( Wedel & Kannan, 2016 ).

Hypertext Markup Language (HTML): HyperText Markup Language (HTML) is a computer language devised to allow website creation.

SWOT Analysis: SWOT analysis is a compilation of a company’s strengths, weaknesses, opportunities and threats. SWOT analysis helps a company to develop a full awareness of all the factors involved in making a business decision ( Schooley, 2019 ).

Customer Value Analytics: Customer value analysis refers to a research method that is used to identify how an organisation is perceived by consumers of an organisation and their competitors ( Haire, n.d. ).

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