How Data Science is the Latest Skill Demand for Marketing

When Microsoft CEO Satya Nadella declared data science and analytics as the new driving factors of business for the near future, he wasn’t far from stating the obvious. For marketers looking for better opportunities, the know-how of how SEO works might not be the only skill they’d need to mention in their LinkedIn. When it comes to digital marketing, professionals are required to wear different hats, and now Marketing Data Scientist is just one of them. A number of data science courses available on the internet can enable people to get sufficient knowledge of the domain, and marketers can thus use data science and analytics to bolster the marketing strategies for their brands.

Marketing has now evolved to more than just developing advertising campaigns. As consumers have limited time to spare on advertisements, a new era of marketing has emerged which focuses on segmenting consumers and targeting one particular niche to drive conversions. This is where data science comes into the picture, and here are the reasons why it’s the new go-to skill demanded by most recruiters:

What is the role of data science in marketing?

A new niche within the data science domain, marketing data science exclusively focuses on improving the effectiveness of marketing at an organizational level. While conventional marketing methods focused on planting the product’s idea in the consumers, marketing data science takes into account both internal and external factors while devising marketing strategies that work.

They make use of data science tools to analyze consumer data and then deliver insights into customer behavior. These insights define the modifications that need to be made to existing marketing tactics and methodologies on analysis. Thus, the data scientists involved in marketing make use of machine learning algorithms and statistical modelling techniques to produce well-grounded predictive suggestions.

When it comes to marketing, not every member of the team can make exact sense of the complexity of terminology associated with data science. Hence, marketing data scientists are also tasked with communicating these insights into simpler terms, so that the marketing team can completely utilize the ideas provided by them.

In a nutshell, knowledge of data science enables a digital marketer to work on the following areas of responsibility efficiently:

  • Perform data analysis on consumer information.
  • Use metric and method selection to generate insights.
  • Utilize the generated insights to improve marketing tactics and strategies.
  • Perform A/B testing on prescriptive data models.
  • Train and assist other members of the marketing team in working with insights derived from consumer data.

Predictive analytics for marketing:

Data science enables marketing stalwarts to make use of their knowledge about customer behavior to generate scientifically-backed insights. When digital marketers rely heavily on Google Analytics to make sense of historical user data like click-through rate and cost per link, it is more like looking through a rear-view mirror of the car; it makes sense but is seldom of much use.

Data science marketers, on the other hand, make use of predictive analytics to forecast the most likely outcomes of marketing strategies, thus optimizing them in a manner that would best serve the future market demands.

Data science kicks things up a notch beyond the level of conventional marketing methods. Just like an artificial intelligence weather prediction application that not only accurately tells its users how long it will rain but also about how long it will last; predictive analytics not only informs marketers about what’s likely to happen, but it also helps them modify their tactics to capitalize on the predictions.

The IBM Watson:

A perfect example of the use of predictive analytics in the field of marketing is IBM’s Watson. Watson is an AI agent designed to assist data scientists and machine learning programmers alike in generating predictive models. In an experiment, marketers instructed Watson to set up a series of social media advertisements aimed towards a particular segment of consumers.

After studying the performance of similar ad campaigns for the same category, Watson recommended replacing an image that had considerably low conversion rates. This is how predictive analytics works in a nutshell. A computer program is fed instructions and data, the data is processed, and the conclusions that are drawn from it instruct marketers on what they should do next.

This brings to light our next question, why then, is there a need for data science in marketing when a simple computer program can give the results that matter? While a computer-driven AI machine can provide results, it needs a data scientist to identify what kind of data needs to be fed into the program, and also to program the computer in a way that enables it to churn out predictions which matter.

Data science in marketing recurringly tests a company’s marketing tactics on all frontiers to make sure that they give maximum profits. It takes care of factors like demand generation, identification of an addressable market, segmentation and account selection, and lead scoring.

In a nutshell:

It is a point of no debate that when it comes to making business decisions, smarter solutions only come from informed predictions. The predictive marketing insights generated through data science techniques have the potential of increasing the earning capacity of businesses to the next level. By unearthing previously hidden customers insights, data science tools enable organizations to alter business tactics intelligently. When marketers think like data scientists, they develop predictions that are centered around the customers, and what it is about the product that makes them happier. As a result, every team, from content to execution, makes better decisions, and the organization gets more value out of its marketing campaigns.

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