The Data Science Fundamentals Every Marketer Needs to Know

By Tony Hudson On Feb 26, 2019

Data science is undeniably hot right now across industries, including marketing. But it’s hardly a fad. Rather, today’s growing incorporation of data science and scientists into the marketing organization is just the beginning of the next evolution of business. The benefits that data science can deliver to marketers are tremendous, but to realize the full impact, we need to begin bridging certain gaps in understanding.

When we think about applying data science to marketing technology, we’re really talking about an exercise in translation. The job of a data scientist is to translate raw data into insights. That said, data science is an exceptionally broad field, and not all data scientists are created equal. For marketers who are looking to bring data science expertise within their walls, they need to ensure they’re getting the right fit.

Specialization Matters

If you want to tap into data science to sell shoes, you don’t just need a great data scientist. You need a great data scientist who understands how to sell shoes. As translators of data into insights, data scientists need to understand the complexity of the problems they’re trying to solve. Otherwise, the work remains at a theoretical level. Only by having a deeper understanding of the industry in which they’re working can data scientists hope to not only glean insights, but also to identify how to collect new data that will help the organization get more out of existing data.

Of course, understanding must flow in both directions for marketing organizations to find the best fit in their data scientists. Not only must the data scientist understand the organization’s specific needs, but the organization’s marketers must have a basic understanding of their own needs and a given data scientist’s specialty. As an umbrella term, “data science” encompasses quite a few disciplines. There’s a vast difference between an expert in visualization and an expert in deep learning.

Both fall under the heading of “data scientist,” but these individuals would never be willing or able to do the other person’s job. Nor would you want them to try. In the marketing world, it’s almost like asking an event planning to suddenly handle digital marketing. While the two fall under the purview of marketing each role comes with a specific set of skills needed to do the job well.

The data science that marketing organizations require is nuanced, and that requires a nuanced data scientist. You need your data scientist to understand how your data works and where limitations exist. Simply describing data assets to a data scientist and eliciting theoretical advice is worthless. Marketers need to work with data scientists who have spent time with their data—literally hands on a keyboard.

At their core, data scientists love to solve puzzles. But you don’t want to hire a data scientist who’s just solving puzzles for the sake of solving them. You need a data scientist who actually thinks your specific puzzles are interesting.

Decision Making Trumps All

While data science can serve many purposes within a marketing organization, addressing budget allocation problems are the most important. Data science, when applied properly, can help marketers make better decisions about how to spend their advertising dollars.

Again, in leveraging data science to solve budget allocation problems, organizations need to be sure they’re not just asking their data scientists to solve puzzles for the sake of solving puzzles. We see this too often when it comes to segmentation. Organizations spend massive amounts of time and resources in developing sophisticated segmentation, but then they don’t know what to do with it. Unless segmentation is directly linked to activation, it’s worthless.

When applying data science to marketing problems, you need to focus first on how the insights will be used for decision making. Focus on measurement and optimization at the outset. Before you think about how your customers and prospects are grouped, think about how you’re spending dollars in the first place. Your method and application of data science should help you evaluate the effectiveness of current spend, and segmentation can be developed on a parallel path as relevant insights emerge.

The Scientist-Marketer Gap Will Narrow

The application of data science within the marketing organization is going to become easier as data scientists become more deeply infused within the organization. In the coming years, we’re going to see passionate data scientists advancing within marketing organizations and eventually ascending into decision-making roles.

There’s a growing need within marketing for data-fluency throughout the organization. Eventually, the people who have spent the time, hands on a keyboard, in an organization’s data will also be the ones paving the path forward. And in time, they’ll expect all of those around them to have the same fundamental understanding of the reasoning and work that went into their decisions. As the data-driven marketing industry matures, this shift will be gradual—but immensely powerful.