Steal a glance at the future: using data science in digital marketing
How data science techniques can improve companies’ digital marketing
The usage of relevant indicators and relevant performance metrics will help companies, marketers and researchers to conduct better researches and to more efficiently measure the time they spend analysing and structuring their databases.
Data Science is an increasing field, which enhances decision-making processes. However, as underlined by Saura (Department of Business Economics, Rey Juan Carlos University, Madrid, Spain) “evidence on the measures to improve the management of Data Science in Digital Marketing remains scarce”.
According to the scholar’s new article (published in the Journal of Innovation & Knowledge — 08/2020) marketers without expertise in Information Sciences, Computer Sciences and Data Science, who lack the knowledge of data management, have difficulties in acquiring that knowledge and in using these skills technically, operationally and strategically.
Generally, Digital Marketing persuades users to buy product using different techniques (Search Engine Optimization, Search Engine Marketing, Social Media Marketing, etc.). According to the scholar a new way to increase the effectiveness of DM strategies is the application of Data Science techniques. In order to improve chances of success on social media and digital platforms, companies should use unexpected patterns using Artificial Intelligence and Machine Learning techniques.
According to the scholar a new way to increase the effectiveness of DM strategies is the application of Data Science techniques.
The key tasks used by Data Science until now have been: improving of storage capacity of companies’ data; performing market research and customer segmentation; extracting key information regarding companies’ problem.
This may arise two kind of questions according to Saura
1. What are the main methods of analysis, uses, and performance metrics of data science applied in Digital Marketing?
2. What are the areas of further research on the use of Data Science in Digital Marketing?
The final goal of Data Science is extracting knowledge from data analysis, Data Science techniques make it possible to extract patterns from databases to explain a problem or to formulate hypothesis. The patterns identified must be non-obvious and useful for companies. In terms of detecting patterns, human beings can identify three attributes of an item, while with Data Science patterns thousands of attributes can be simultaneously identified. These patterns identify actionable insights. According to the scholar these two words can be analysed as follow: actionable means that these insights must be usable from companies; insights is the capacity of a pattern to provide meaningful information.
In terms of detecting patterns, human beings can identify three attributes of an item, while with Data Science patterns thousands of attributes can be simultaneously identified.
One of the most important topic in Data Science is the origin and sources of data. Databases are made up of different variables or indicators: these databases are known as datasets. For datasets analysis, Data Science relies on models based on Machine Learning, that provide algorithms to automatically analyse large datasets; these models can be trained to extract actionable insights and identify patterns. The objective of the methods used in Data Science applying statistical learning is to perform functional analysis, exploratory analysis and predictions of results based on the analysed data.
One of the main challenges in Digital Marketing is controlling and defining the success of a Data Science strategy; at this purpose marketers should choose and understand the main performance metrics for the measurement of the model used.
Each of the topics and the influence that these topics may have on the development of Digital Marketing strategies using Data Science, according to Saura, can comprehend
Medical data and e-Health strategies: the analysis of users’ medical data help to find trends and facilities the creation of new vaccines. Marketing must promote companies use of such strategies to collect data for further analysis.
Smart cities and governance: efficient management of energy resources, as well as sustainable and intelligent construction and development are based on automation and AI of large structures. Social and responsible marketing, also known as Corporate Social Responsibility (CSR), is driven by Digital Marketing based communication through digital platforms and social media channels.
Internet of things: refers to management and collection of daily use data from connected devices; this includes order and identification of new features that help personalize and offer new products and services to create new needs. Digital marketing adapts to the mobile environment with mobile friendly designs initiatives and strategies.
Data privacy and management: this includes rights, access, and legitimate profitability of large, public database. One of the functions of Digital Marketing is to raise consumer awareness about how companies will make use of their data.
People (movement, organization and personalization): includes analysing movement and organizations of people through the analysis of large databases of citizens/vehicles/purchases. Digital Marketing has the challenge of personalizing massive messages and using Data Science methods identifying specific habits according to the type of people and according to their demographic and psychographic characteristics in order to increase ROI of digital campaigns.
Development of Machine Learning models: new machine learning methods that companies can train and apply in their projects; these models can be created, trained and debugged for a specific purpose.
Operational CRM and Data Management: this includes creation of automatic company information management system that can identify better unsuspected patterns and extract actionable insights to help companies to manage their information in real time.
Sustainable strategies based on data: this includes the study of sources of data resources and management of globalization processes to increase sustainable strategies and actions based on data analysis. Saura indicates social marketing or green marketing as relevant research areas in this field.
Social media listening: automated researches on important trends in social network and messages released by opinion leaders, as well as exploring the responses of communities to massive messages in the face of crisis environmental or social movements. Digital Marketing should understand how these communities are organized and take adequate actions with persuasive and responsible messages.
Saura concludes that nowadays companies are involved in an increasing data driven ecosystem, however businesses have been reported to waste a lot of time organizing, cleaning and structuring the databases of their users and customers. The usage of relevant indicators and relevant performance metrics will help companies, marketers and researchers to conduct better researches and to more efficiently measure the time they spend analysing and structuring their databases.
