5 Ways Qualitative Research Can Complement Big Data

Chris Martin

Pitch It: The Business Case for Customer Salience

As insight experts, we understand the power of insights, their inherent value in key decision-making...


Emily James

    Big Data is one of the most popular buzzwords in business. If you were to believe the social media hype, you might be forgiven for thinking that it holds the solution to every marketing problem in the history of business. Perhaps even for thinking that Big Data will someday replace market research entirely. But the truth is, this is not the case.

    That is not to say that data analysis is not important, but it has different strengths and weaknesses to traditional market research methods. Big Data is not the replacement to market research that many proclaim it to be – but rather a tool that can be combined with research for greater insight. This article aims to outline five ways in which businesses can take advantage of the synergies offered by combining Big Data and market research.

    1. Aligning Prediction with Cognition

    The first opportunity is the chance to solve an age old problem – the divide between action and reaction. What people say, and what people do can be worlds apart. Market research, in a traditional sense, asks people what they would do in a given scenario. Data analysis, on the other hand, uses historical information to understand behavioural patterns.

    By combining Big Data analytics with market research, it is possible to gain insight into both opinion and previous behaviour. Comparing these two insights gives rise to much more reliable information upon which to base future decisions. For example, although consumers may say they would purchase a new brand extension – their previous actions might not support that statement. Therefore, a product extension with a much more impactful launch strategy could solve such a dilemma. But, without conducting both data analysis and market research it would have been impossible to reach this conclusion.

    2. Testing Hypotheses

    Similarly, using Big Data mining as a secondary research method can aid in the process of testing hypotheses. For example, if your depth interviews and creative qual tasks tell one story, using behavioural and financial data sets can verify the likelihood of your predictions holding true. Of course, if you are to use data and research in this manner, it is vital to avoid confirmation bias at all costs.

    Confirmation bias is the mind-set of looking only for information that supports your point of view. It is especially dangerous in scenarios involving Big Data as the information provided can be interpreted in so many different and varied ways. To avoid confirmation bias, manage information in your team with care, so that analysts do not know the hypothesis they are proving or disproving and researchers cannot influence the outcome with misinterpretation.

    3. Completing the Story

    Market research and Big Data are, in essence, two sides of the same coin. Their goal is to provide insight to the business upon which managers can act. But the way in which the two reach this goal are notably different. Big Data tells us the ‘what’ of a given story. What did customers buy? What did customers avoid? What advert affected their behaviour the most?

    However, it is in-depth qualitative research that completes this story with the ‘why’. Why did customers buy that product? Why did they avoid others? Why did they prefer our advert over competitors’? By piecing together these two sides of the same story, it is possible to create compelling narratives that people will listen to. Complete stories drive business action. Incomplete ones only lead to more questions.

    4. Humanising Data

    When it comes to presenting findings, there is widespread acceptance that storytelling is the way forward. Presented with statistics, we have a natural inclination to be critical – to not be fooled by words we know are designed to beguile and confuse. Listening to stories, we are much more open to suspending our disbelief. There is something that connects us on a human level to others through the medium of storytelling. We can empathise; imagining ourselves and our own reactions in the same situation.

    So how do you collect stories to not just support your data, but become the backbone of a presentation? Through deep dives into emotion and cognition. Use data analysis to understand people’s actions, then ask them why they act in such a manner. Ladder the questioning to the highest level of abstraction you can reach and look for the true, emotional insight.

    5. Continuous Development

    Another creative way to integrate qualitative research and data analysis is through a continuous development cycle. Data analytics is strongest when analysing previous behaviours and building predictions for the short term future. Market research is able to address a wider range of consumer issues, but not necessarily those that can be immediately solved in the short term.

    By playing to the strengths of both tools, it is possible to create short, continuous development cycles that serve the business better. Data can be used to make small, incremental improvements to products, services and experiences at a rapid pace. While this is happening, long term qualitative research should also be taking place that identifies core structural improvements to the brand architecture. Of course, acting on these insights is a much longer process – thus meaning it is even more important to pay attention to the short term immediate improvements that can be made at the same time.

    In conclusion, it’s clear that Big Data and market research are not the rivals that many make them out to be. Rather, they are complementary tools that cannot only exist together, but work together in your organisation to create some spectacular outcomes. It is my hope that in the future we will begin to see many more combined efforts and integrated teams working together, rather than in competition. It is only when we accept this that we will truly begin to see the value of the Big Data economy.

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