If you Google market research and data science, you’ll get a broad range of conflicting results. Market researchers and data scientists would confirm these definitional differences, saying that the work they do is worlds away from the other. Many major companies build entirely separate teams of market researchers and data scientists that work with completely different clients and vendors and have different daily tasks and even overall research goals. However, there is a common thread that makes these two disciplines a compatible pair: they are both driven to help their organizations achieve success by making sense of data. So, while they lack the clear appearance of a harmonious pair at first glance, the intersection between market research and data science can make for the ultimate dream team, and a super solid foundation for deeper and richer insights.
The Big Boom (Data Boom That Is)
Data science is (you guessed it!) the science of data, with origins in statistics and computer science. Having deep roots in the use of statistical models, the field now also includes machine learning, artificial intelligence, algorithm building and other advanced approaches that have earned it its own industry title. A recent FRESHMR post further explains data sciences as an area that deals with larger portions of the population. It places attention on observable data that organizations gather about their current or prospective consumers, “in order to answer questions about what is currently happening or predict what may happen”. The growing ability for these companies to collect that kind of information about their customers at such rapid speed has resulted in a “Big Data Boom”, necessitating skilled data scientists to sift through large data sets with unprecedented efficacy.
In response to the boom, many budget allocations that once went to traditional market research now support data science approaches. And while answering the data science questions (i.e. what is currently happening; what may happen) are of obvious importance to any company, an equally important explanation of attitudes, intentions, or motivations behind why something is happening or why something may happen, is not fully available through these methods alone.
It’s All About ‘The Why’
Market research was once limited to survey research, with a focus on smaller groups of the population used to understand consumer attitudes, beliefs, preferences, motivations and intentions, as well as some of their specific behaviors. However, much like data science, market research has had quite an evolution of its own. A day in the life of a market researcher may now involve consumer engagements through online communities and other netnographic methods, mobile research, and ethnographic efforts, among many other approaches.
Like data scientists, market researchers can be tasked with handling behavioral data, but they can most often be found digging deeper into why consumers behave the way they do. They strive to have a holistic understanding of the humans behind data points, and help brands build strategy around that understanding, ensuring that the voice of the consumer is known and valued across an organization. They see research through, from problem definition and research design to analyzing quantitative and qualitative methods and final reporting, all the while stewarding the consumer perspective.
A Dream Team in the Making
This exploration has revealed the slightly more transactional and relational natures of data science and market research, respectively. It’s through marrying the two methods that organizations will achieve the most holistic research findings. As seen through survey research, a far deeper understanding of why a consumer behaves a certain way can be uncovered, rather than simply making sense of what the behaviors themselves are. By combining data science methods with survey research in this example, a multi-dimensional view of the consumer is achieved with respect to what their behaviors are and why they behave this way. One without the other simply lacks the research dimension needed to achieve the greatest understanding of the consumer and ultimately provide the most informed recommendations to any company.
Stacking the Team
By now we’ve learned that market researchers and data scientists can learn from one another despite their different tasks and goals. But as mentioned before, they do share the common goal of driving business success, which leads us to explore how to practically “stack the team” of insights professionals within an organization.
Much like any team sport, stacking the team with the top performers wins the game. In the game of driving business success rooted in rich insights, stacking the company team with data scientists and market researchers is no different. Here are some practical steps that will lead to synergy between market researchers and data scientists and a big win for your organization:
1. Explore business needs together: a huge percentage of research projects fail because the wrong question is asked. In order to avoid these failures, put the market researchers and data scientists in a room together to discuss what data is already available, similar studies that have been done, what data is needed to answer the questions, and what approaches should be considered. This collaboration will result in clear objectives and expectations.
2.Data sources, variables and methodology: finding the sources of data used to answer the research question is critical. Market researchers collect data through surveys, discussions and observations, while data scientists typically handle transactional data or information about demographics or general behaviors. Both of these necessitate a deeper understanding of individual variables and relationships among variables. Constraints and biases should be considered by both parties as well, in addition to other quality check measures. After the preparation of the data comes to an end, a method can be chosen for analysis. A healthy discussion between market researchers and data scientists at this point can allow for a fruitful brainstorming session and suggestions that may not otherwise come up if the groups remained separated (FRESHMR).
3. Discuss the outcome: in that same room the data scientists and market researchers first met in? Yeah, put them back in there. It is so important that the data scientists help the market researchers understand their work and that the market researchers help the data scientists understand theirs in return. It isn’t always natural for one to understand the “language” of the other, but we are all on the same team here after all- so let’s help each other out. Hint: start by talking about what went well, what didn’t and what each learned from the other and can take into the next project. Communication and collaboration matter, for business success and for morale.
Data science and market research have their differences, no doubt. But, it’s their shared core goal of uncovering compelling and actionable insights that makes them a powerful pair. The complementary approaches, when combined, can only help an organization achieve holistic findings.