The importance of Big Data and Machine Learning in Marketing

The definition of Big data can be understood in a simplified way as the collection, processing and analysis of many data. It is considered a critical set of structured and unstructured data that cannot be processed with traditional methods. (Forbes, 2021)

To understand and systematize these innumerable data in a coherent and tangible form, it is necessary to treat and transform them into a piece of consistent information that helps several industries to use them strategically. And this is where the concept of machine learning gains its importance. Walkowiak (2016, p. 410) explains that the method used by machine learning to transform this information is the connection between data mining and statistical techniques that enable researchers to understand the sense of data, to create a relationship between variables or features, and to predict future events or values. In other words, machine learning seeks to analyze increasingly larger and more complex data, using algorithms to reveal a series of hidden patterns that would not easily be perceived by the human mind.  

Data and machine learning goes hand in hand. When combined, it is a powerful tool that enables insights into consumer behavior and improves decision-making performance. (Cui, Wong, and Lui, 2006). The ability generates analytical models in real time and to interpret through algorithms what a particular user is looking for at that moment, and thus to deliver almost immediately a suggestion of a product or service that might be of interest. 

As far as a business is concerned, Marr, B (2017, p. 40) says that making assertive decisions should be the priority of all businesses, once the data provides a better knowledge of the market, directs the development of products that meet the needs of customers, increases revenue, and provides a precise understanding of a new target customer. At a time when everyone is fighting for differentiation in the market, the proper audience insight gives organisations a clear competitive advantage.

Practically, the impact of this technology on marketing is related to accurate and real-time predictive insights that contribute to revenue growth by adapting content, driving Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQLs), optimizing campaigns and improving accuracy and profitability of pricing, retaining customer attention, and so on. (Forbes, 2018)

Amazon, for example, uses data intelligently to retain customer attention and increase sales, and thirty-five per cent of sales are generated from product recommendations. And the customer experience was improved by introducing "anticipatory shipping," which reduces delays and increases satisfaction. (Siegel, p.317)

In conclusion, the current technological landscape shows that the results of the dissemination and permeability of the Internet and the tools connected to the network have profoundly changed the way business is done. Nowadays, no matter how small the operation, it is difficult to imagine a successful company not on the Internet. Being on the Internet, owning a website, an online store, and telling an efficient communication system with customers and potential new stakeholders is no longer enough. It is necessary to go a step further! 

Collect, organize and understand all the data involved in the process of buying and communicating, know what is said and what you think about a brand, understand habits and hidden patterns that are currently as important or more important than the sale itself.

Author: Natássya Coelho

 

#BigData #MachineLearning #Analytics #Marketing #Business #Whatisbigdata 

 

Reference

Cui, Wong, and Lui, 2006. Machine Learning for Direct Marketing Response Models. Journal of management science, 52 (4), pp. 597-598.

Eric Siegel (2016) Predictive Analytics : The Power to Predict Who Will Click, Buy, Lie, or Die. Hoboken: Wiley. Available at: https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib,cookie,url&db=nlebk&AN=1157317&site=eds-live (Accessed: 21 February 2021, p.p 317

Forbes. 2018. How is big data analytics using machine learnig. [ONLINE] Available at: https://www.forbes.com/sites/forbestechcouncil/2020/10/20/how-is-big-data-analytics-using-machine-learning/?sh=4d843c0471d2 [Accessed 19 February 2021]

Forbes. 2021. How is big data analytics using machine learnig. [ONLINE] Available at: https://www.forbes.com/sites/forbestechcouncil/2020/10/20/how-is-big-data-analytics-using-machine-learning/?sh=4d843c0471d2 [Accessed 18 February 2021]

Marr, B. (2017) Data strategy : how to profit from a world of big data, analytics and the Internet of Things. EB. Kogan Page. Available at: https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib,cookie,url&db=cat01049a&AN=dbs.58093&site=eds-live (Accessed: 19 February 2021), p.p 22.

Walkowiak, S. (2016) Big Data Analytics with R. Birmingham, UK: Packt Publishing (Community Experience Distilled). Available at: https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib,cookie,url&db=nlebk&AN=1295358&site=eds-live (Accessed: 21 February 2021), p.p. 410.