Data science is a relatively nascent field that emerged to cater to the explosion of data at the intersection of various fields like economics, social science, statistics, environment, marketing, and statistics among others. It is defined as utilizing modeling tools to analyze and predict patterns that enable businesses to garner more return on investment (ROI).
Marketing of products and services is described as advertising/promoting the same. Digital marketing adds an online promotion mode to traditional marketing with more focus on attracting customers through advertisements in digital media like the internet, smartphones, e-mail and other digital media.
Combining data science and digital marketing makes a lot of sense in the 21st century due to reasons such as:
- Proliferation of digital media tools like mobiles, tablets among the general populace
- Digital communication tools like e-mail being used almost on a daily basis for personal and professional purposes
- Spread of social media bringing many potential customers and their social behavior into the digital realm
- Massive rise in online transactions leading to huge increases in customer footprints in the field of consumption of products and services
Thus if digital marketing campaigns employ data scientists or add data science skill-sets to their marketing staff through training then they really have a significant edge over competitors and can enable their clients to attract more customer and get better ROI. It is not essential for marketers to get a formal data science degree. Adequate training and willingness to learn are sufficient.
To transform a traditional marketing/digital marketing professional into a data science utilizing digital marketing professional, one needs knowledge of tools that help comprehend and filter large data sets for the useful bits of information. Some of the advanced marketing automation tools are:
- GetResponse - This tool is particularly effective for data-targeted email campaigns. It is easy to use and affordable for businesses, especially small and midsize businesses (SMB).
- HubSpot - It is known for using data science concepts like "contact management" that enables users to manage and record their customer/client contact lists. It also offers an ROI calculator which is simple to use.
- Marketo - This tool helps businesses find their target groups of potential customers using filters. It is suitable for SMBs and global enterprises.
- InfusionSoft - This is a tool that enables a customer relationship manager to automate their company's sales and marketing management. Its special feature is to identify and prioritize leads that lead to improved ROI
- Constant Contact - This tool's major benefits include usage of data mining software and prediction software to analyze and determine customer behavior. Also, it offers businesses, tools to help optimize every business cycle and process to increase ROI.
Data science tools are not always seen as beneficial. There have been cases where companies circumvent local laws and misuse personal information.
In recent times, big data or also referred as meta-data analytics has run into issues especially when US companies perform such activities abroad. Facebook, Twitter, Google are all major companies that employ or have begun employing data scientists to research their customer data. They have run into legal trouble in Europe due to differing concepts of data as a material to be used or not to be used.
While in the US, the concept of data is utilitarian, which means that personal data is viewed and accepted as something that can be analyzed and used to improve ROI for companies. But in Europe, the prevailing culture is that a person's personal data is only allowed to be seen and used by that person alone. Hence American companies like Facebook, Google, Twitter face issues with European regulators when employing data science research in marketing and analyzing personal data of Europeans. This ethical vs. legal dimension of data science in digital marketing needs to be accounted for since businesses are global in today's world and have to take into account local sensitivities and sensibilities with regard to mining personal data.