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The Innovation of Big Data in Marketing

Paper Type: Free Essay Subject: Marketing
Wordcount: 2508 words Published: 8th Feb 2020

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In the 21st century, the biggest use of big data becomes the core of marketing processes.

Marketing defines by Britain based Chartered Institute of Marketing as ”the management process responsible for identifying, anticipating and satisfying customer requirements profitably.” Data has been used to track and control businesses data 7,000 years ago since the innovation of accounting propagates to Mesopotamia for the record the growth of crops and herds (Rijmenam, 2016). In 2005, big data has been coined for the first time by O’Reilly Media, one year later they created Web 2.0. which indicates to a huge set of data(Rijmenam, 2016). For marketing organizations, big data refers to the fundamental consequence of the new digital marketing landscape (’Big data, bigger marketing.’).

This essay will explain the innovation of big data that works in the field of Marketing and describe how big data could be applied in Marketing. It will then evaluate the benefits effects of big data on the digital marketing system.

Big data is large and complex data sets that are collected by companies and governments. The data that involve many types of information arriving in increasing volumes and with the incredibly fast rates (Grable & Lyons, 2018), ”innovative forms of information processing for enhanced insight and decision making” (’Big data’). Normandeau points out that big data signifies colossal volumes of data are being generated from assorted sources such as business processes, machines networks, and social media. Historically, it is a challenge to reserve the enormous volume of data, by the progression computing capacity that storage is not an issue anymore.

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Big data can be classified into three types of data which is structured data, unstructured data, and semi-structured data. Rai (2018) points out that structured data being easily entered, processed, queried, stored and recover into a fixed format. The typical examples of structured data contain numbers and dates. (Halper, Hurwitz, KaufmanRai & Nugent). Rai (2018) then indicates unstructured data cannot be fit or classified into a net box and the process and analysis are very hard and time-consuming. For instance, objects from blogs, text messages, books and videos (Robb,2017). Semi-structured data is a form of structured data that contains semantic tags but it does not belong to typical relational databases such as email, XML and some markup language (Robb, 2017).

Big data collection plays the most important role in marketing. According to Goddard (2019), customer data have been collected from a variety of different ways such as the company is going to ask for customer data directly, pulling in customer data from, social media, email tracking and there are data companies selling data. The majority of firms are asking for customers data directly in the early on (Goddard, 2019). The writer also points out that customers usually need to fill out forms when they want to subscribe to a service, first time buy things online and become a member of a brand. Nowadays, social media becomes an integral part of people’s daily life by the increasing use of the Internet. The social media data are related to the online activity for individuals, includes such as shares, likes, comments, and mentions (Segal). When customers using their Facebook account to log in to the third-party application that the data from the customer will interflow as well (Goddard, 2019). In addition, firms are purchasing customer data from a data company that has the sole purpose of collecting data and analysing and selling the data to targeted advertising campaigns, such as Acxiom and Oracle (Goddard, 2019).

Nowadays, big data has been applying in different domains, the wide-ranging applications of big data in marketing include drive innovation and product development. Production evolution requires large data quantities to the recognition of customers’ demands. Companies can create a new product that is closely related to their consumer and minimise the risk by data mining and analysis which can identify customers preference and needs (Anastasia, 2015). Hence, big data become an ideal method for improving production success (Medouri, Rahich, Saidali & Tabaa, 2019). Anastasia also indicates the firms use big data as an intelligence tool to innovate new products, recognise the opportunity to launch the new product, as well as advance the existing product lines. Moreover, big data is able to correct the involve substantial trails and errors (Farood, 2017). Medouri, Rahich, Saidali and Tabaa address that the real-time data can be analysed immediately thereby significantly reducing the time for analytics and increase the efficiency of decision making and enhance the speed of idea generation to product delivery. According to Kopanakis (2018), ”Amazon fresh and whole foods” are the typical example of big data improve the evolution of products which the Amazon whole foods are focused on the analysis of big data thus they are able to understand customers primary need are groceries.

In addition, big data can help to optimise pricing in marketing. Saran points out that traditionally the price of a product is based on the product cost, competitive pricing, and perceived value of the product customer and requirement. However, big data can provide more factor for firms to set a price. For instance, companies could utilise the data from completed deals (Saran, 2018). Moreover, big data can determine the optimal price for firms’ maximizing their profit margin by first view customers’ purchase history, choices and behaviour, then determine the price adaptability of the requirement by statistical analysis and finally optimization of business by investigate the cost price with profit maximization (’5 Practical uses of big data in Marketing and sales, ’ 2017).

The usage of big data in marketing helps to solve advertisers issues and provide marketing insights since it can give assistance to companies to understand their customer. Big data can be efficient and precise target customer with the product which they like or demand (Mesal, 2017). The writer points out that advertising agencies collect customers’ Information about motivations and observe online activities in order to save money and guarantee efficiency. Netflix is one of the compelling examples of big brands utilize big data for targeted advertising (Kopanakis, 2018). According to Kopanakis, Netflix collects enormous data from over 100 million subscribers and the data sets are about the past search and view recording which were done by then. In order to understand what is the most interesting type of film in the subscriber and give them suggestions for movie recommendation.

Therefore, the utilisation of big data is identified in a variety of aspects of marketing. Big data can bring many positive impacts on marketing at the same time. For instance, big data provide companies with the condition to forecasting, it enhances the risk management system and big data is able to improve social media marketing.

Initially, big data enable firms to anticipate and plan to deliver success. Big data allows marketers to behave proactively and become future trends ( Richardsom, 2017). Whitehead states that expediting reporting of increasing volume and range of data set and real-time forecasting, thereby the company will obtain increasing budget, understanding of the influence of revenue by different levels of spend. In addition, marketers are able to create comprehensive strategies and arrange for more efficient actions by the latest trends of consuming behaviour from analysis of real-time data and the latest trends (Richardson, 2017). Hence, the company are capable of targeting segmented sub-group consumers by their own specific features and gives them the possibility of adapting activities to each of the customers individually (Richardson, 2017).

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Moreover, big data can be a business intelligence tool are able to efficient risk management for banks and the the the market. People are able to identify potential risks related to money leading processes in banks, understand market trends and setting different interest rates for each individual from various regions by the usage of big data analytics (AK, 2018). Likewise, the markets become more and more interconnected and the financial risk increase at the same time. According to Pribanic (2018), big data can use a simulation scenario system to observe the hidden risk related to all financial transaction. The writer also addresses financial institutions are capable of identifying potential issues faster by the analysis of big data for risk management supplies real-time reactions.

Furthermore, big data boost social media marketing. While social media set up a connection between friends and family, it also becomes the majority of the data source. Warner (2018) states that social media become a platform for brands to advertise and expand their client base. The likes, comment, shares, mentions and follower are all indicates data for consumers’ behaviour which provide actionable insight to brands and marketers (Warner, 2018). Facebook ads strategies are a remarkable example of big data developed social media marketing (Hyde, 2017). Facebook has been collecting data over the past thirteen years, while they sell results to third parties. The marketers advertise from the Facebook platform and they are able to maximise campaign effectiveness by using big data indirectly.

However, technologies are needed for using big data. Marketing is usually the least automated department in many companies and analyses the huge amount of data becomes the greatest challenge of utilising big data (’Marketing: big data benefits& challenges, ’ 2013).

In conclusion, big data is an important investment in marketing. Companies can accomplish competitive advantage, save cost for operation and keep their own customer base by analysing the big data. Historically, it is a challenge to reserve the enormous volume of data, but nowadays the storage of data is no longer a problem since the growth of computing capacity (Grable & Lyons, 2018). Nonetheless, the complex technology of using big data have not been popularising to the majority of the company. Hence, the improvement of technology in big data is an issue which needs to be taken into consideration.

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