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Breaking Down the Barrier Between Market Research & Data Science

Market research and data science need to be thought of as related entities. Combined insights from the two can lead to a deeper understanding of marketing issues.

Editor’s Intro: There is some confusion out there about what “data science” is, and how it relates to traditional market research. Brooke Patton addresses the relation between the two, and how clients would benefit if the two came together into something she calls “research science”.

Market research and data science both have something in common; they’re critical to a business that wants to learn more about its consumers and make better strategic decisions. Data science and market research are two sides of the same coin and it’s time to take them a step further, break down their walls, and leverage them together.

Moving From Market Research to Data Science

We know market research and data science aren’t one in the same, but what makes them so different and how can market researchers—or rather, businesses in general, leverage both in their organization?

Before we can answer that, we have to recall the purpose of market research and how data science was born. Many of us already know that market research is the process of gathering information in order to answer strategic business questions. Such information could include everything from consumer segmentations to reactions to stimulus to purchasing behavior. Often this research is done through the use of statistical methods or practices that collect and analyze data to make inferences about something. Of course, in today’s digital landscape market research has evolved to encompass far more such as behavioral targeting insights and social media listening.

But with the explosion of the internet and the myriad of new types of data—and lots of it—organizations needed a better means to extrapolate insights. Data science became relevant due to the need to select, analyze, and extrapolate insights from new and intimidating sizes of data. While data science also incorporates methods of statistics, this influx of data was unlike the kind that marketers and market researchers had ever seen before. As a result, it required a new skill set and role to sift through it—thus the data scientist was born.

Data Science vs. Market Research

Data science, in a way, moves beyond the capabilities of market research. It’s actually been around a lot longer than we think too—having got its start with database marketing and data mining. But, that’s not to say that market research is in any way inferior to data science. Data science has just grown beyond research to use more robust scientific and technology-enabled methods.

The methods that data science uses to analyze this information often includes algorithms and machine learning. As a consequence, data scientists have to be more than researchers and are usually part programmer too. As noted, data science also evaluates a much wider variety of different types of data, such as structured, unstructured, and semi-structured data. As a result, their process usually starts with determining what methods and types of data they need:

1. Identify the type(s) of data needed specific to organizational objectives
2. Assess and select datasets
3. Integrate and cleanse the data
4. Investigate and analyze data through the use of data modeling, machine learning, statistical modeling, and algorithms
5. Assess and understand results
6. Communicate insights

Yet, this process isn’t that different from the market researcher’s process. So, data science, while different, still has similarities with market research. Looking at the two, we find they’re just two pieces of the puzzle that sit side by side.

Research Science?

Recall market research uses more established methods to collect primary data on consumers. Data science can sometimes go rogue and use secondary data sets and differing methods for data analysis and insights. Further, data science can gather far more than just self-reported consumer data to build stories. So what’s its drawback? It lacks context. This is where combining insights from data science with market research comes into play—both are meant to gather insights to improve decision making after all.

Data science provides a diverse and robust set of insights. Market research builds the connection between those insights to translate results to specific needs—such as product development or creative messaging.

No business has ever achieved success by only looking at data in silos. Leveraging market research and data science together is necessary to make the responsive and anticipatory decisions that help a company thrive. The combined insights of data science and market research will be a force to be reckoned with; brands looking for an advantage would do well to coordinate the two earlier rather than later.

Research science—or the literal term combining market research plus data science—can move brands from answering simple business questions to developing a framework for personalization of products and optimizing their customer’s journey.

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