Data Science for the Marketing and Advertising Industry
By Kat Campise, Data Scientist, Ph.D.
An enterprise or individual can have a mind-blowing product or service, but without the proper marketing and advertising, attracting customers will be extremely difficult. Marketing research isn’t a new concept. However, when compared to data gathering in the early 20th century, market researchers are now swamped with data. Between Twitter, Facebook, Google, and other social media channels, we can now collect an exponential amount of information about consumer preferences and other trends that may impact marketing efforts. As a matter of fact, digital advertising comprises a majority of the incoming cash flow for the likes of Twitter (86%), Facebook (99%), and Google (71%). Whether we are conscious of it or not, we are being “sold to” at almost every point throughout our daily lives. Enter the marketing data scientist who is armed with an arsenal of predictive algorithms and the analytical acumen to parse through a massive number of data points. Granted, the title “marketing data scientist” hasn’t quite materialized in terms of employers explicitly stating that they are looking for this type of data scientist. At the current time, most data scientists wear many departmental hats, meaning they work inter-departmentally to build predictive models for a variety of business objectives: marketing, sales, human resources, risk mitigation, finance, robotics, cyber security, etc. But, data science is still evolving, and more distinct specializations are likely to emerge. For example, natural language processing (also known as text analytics) is a particular discipline where the focus is on processing and analyzing human language. Computers are intrinsically mathematical. When compared to straightforward measurements (e.g., height, weight, test scores), human language has far more nuances and interdependencies that don’t fit neatly into a numerically measurable classification. Language conveys emotion and intention, and even human beings tend to struggle with accurately interpreting what the other person is trying to communicate. This is an important aspect of being a marketing data scientist as marketing and advertising are essentially a conveying of emotion and intention.