industry thoughts from Clarteza

31 December
Mag Retelewski

From Gray to Clear: Quantitative vs. Qualitative Research

Both quantitative and qualitative research data play important roles in understanding consumers, developing and designing products, and creating effective marketing strategies and executions. Quantitative research provides a wealth of broad-based data on market sizes, demographics, and user preferences and helps us understand differences between groups of people, products and usage occasions.  But, quantitative research can also lead to an over-reliance upon numbers, leading researchers to analyze percentages and statistical significances, rather than people.  Qualitative research, on the other hand, can sometimes lead to biases based on the opinions of a small set of people, at a certain place or time.  But, qualitative research can help tell us the why behind quantitative results, providing insights into exactly how people use products, how they go about their daily lives, what thoughts and feelings they have as they use or shop for products, as well as reactions to product or advertising prototypes.  In today’s fast-paced consumer products world, in which information overload is a common problem, how do we know which to use, and when?  And how do we combine the two effectively? 

Gray: “Quant and qual are separate disciplines”

Oftentimes, quant and qual are treated like two entirely separate disciplines, rather than two sides of the same coin.  We use separate research shops for quant than we do for qual, we operate under timetables that dictate when in a process we should use each, and we often have separate line items for each in the market research budget. Depending on your background or your firm, you may lean much more strongly toward one vs. the other, assuming that “quant doesn’t provide enough insights” or “qual is too expensive, and doesn’t provide enough data that I can act upon”. Most commonly, we use a set process whereby qual has to be done before quant and then we forget about the qual once the quant results are in.

Clear: Quant and qual go hand in hand

Fundamentally, quant is used to test pre-determined hypotheses, while qual is used to generate hypotheses.  Is there really a point in the marketing or new product development process at which generating new hypotheses is no longer relevant?  Certainly not for a company that plans to be around longer than the next product launch or the next ad campaign.  While both qual and quant each have their own strengths and weaknesses, there are a number of simple ways in which they can be combined effectively to generate richer consumer data and insights over time.  In addition to the standard qual-before-quant process, qual can also be used after quant research to provide richness around the quant findings, or to help better explain the results obtained from quant.  How often do we struggle to understand quant results?  Yet, how often do we consider follow-up qual to help shed light on those results?  Growing in popularity as well as the use of integrated qual-quant methodologies that continuously combine results from each for decision-making as well as to inform future research needs.


Gray: “Qual is nice, but ultimately, business decisions are made on quantitative data”

For many firms, qual research is considered too warm and fuzzy to be used in making real business decisions.  Qual is a means to inform quant research that will ultimately be the determinant of investments in product and marketing activity.  After all, quant is what tells us market sizes, identifies target groups, highlights differences between sets of people, and is “statistically valid” – meaning we have a scapegoat if what happens in the market doesn’t go as planned (“hey, our research was solid…just look at the numbers!”).  We like quant because is ensures objectivity and reliability of the data, and big sample sizes make us feel more comfortable.

Clear: Your business decisions need some qual, too!

Unfortunately, if business decisions are always made with quant, and we scrimp on the qual, we eventually run out of novel hypotheses to test in our quant studies.  And while quant can tell us “what” – what product, what target market, what price, etc., it can’t tell us why or how.  Qualitative research is what really helps us understand human behaviors, emotions and personalities.  Qual provides us with consumer needs and behaviors, desires and emotions, attitudes and preferences beyond what quant can give us.  Qual can do more than identify areas to further investigate with quant; it can also help illuminate quant results, but most importantly, can and should inform major design and advertising decisions.  Ultimately, no single methodology can sufficiently capture a full understanding of the market in which you are competing…markets and people are complicated, and should be studied from multiple perspectives and analyzed from all angles.


Gray: “I don’t know what I need…give me everything!”

In today’s competitive environment, with terabytes of information about everyone and everything, some of us end up moving to the extreme other end of the spectrum:  we want all the perspective, all the angles, all the data.  We want more qual and quant data than we know what to do with.  One of the major trends in the information overload era is to integrate qual and quant more often, and a number of research suppliers and clients are moving toward hybrid, or integrated approaches.  While in many cases, these certainly yield benefits, we must be careful to ensure we are not falling into the “more data is better” trap.

Clear: Take the time to figure out what you need

Executing more research studies, and executing more hybrid approaches requires more work and more money.  Everyone wants more data, but simply getting more data does not necessarily lead to better business decisions.  Too often as researchers, we fall into habits or lean toward familiar types of research for different needs…it’s time for a concept test here, a market assessment there, a focus group there.  Other times, and becoming more common with the explosion of online qual and social media data, is the desire to explore new and cool methodologies, incorporate hybrid approaches, and simply try everything we can to make sure we didn’t miss something.  Before we start choosing methodologies and pressing ‘go’ to make sure we are moving the process along, we need to take the time to figure out what we really need.  The key to successful research, whether quant, qual, or a mix of both, is to fundamentally understand what questions and issues we seek answers.  Once we have taken the time to identify the true questions, both the burning immediate questions, and the long-term questions, we can identify the right mix of methodologies that will best provide the types of answers we seek.


No matter which methodologies are used when, one of the critical challenges that has always faced marketers and researchers is how to ensure the data remains useful and ‘stays alive’ within the organization.  Particularly with qual, too often we run a focus group, use the data to design a survey, analyze the quant results, and then forget about the rich insights we got from the qual.  The emergence of mixed methodologies, while useful in answering particular research questions, also results in the acquisition or more data, much of which may sit on the shelf if it wasn’t relevant right in that moment.

Exploring the question of quant vs. qual means also exploring the question of how we use, store, and re-use data that we obtain from the investments we make in research.  As the amount of available data on our products and consumers continues to grow, it becomes more incumbent upon an organization’s marketers, researchers, consultants and suppliers to ensure that the data continues to remain alive and part of the decision making beyond the moment the first report is delivered.  Market researchers within client-side organizations are increasingly pressured to spend all of their time executing and analyzing research…but also to spend all of their time re-visiting data, connecting old dots, and anticipating the needs of marketers and cross-sets of other players, from advertisers to R&D.  It is in this area that the supplier relationship can play an integral role in ensuring that data stays alive, that quant and qual play nicely together, and that someone is keeping tabs on all the different types of data around – and knowing what is needed where.