Who needs market research anyways?
The short answer is everyone. If you are in the business of influencing consumer behaviour, or more simply put, if you are trying to sell any product, service or idea, you need market research.
There is a strange misconception, surprisingly still held by many, that marketing efforts, product innovation and brand positioning can be successfully guided by trends, best practices, anecdotal evidence and even assumptions. It goes without saying that assumptions are highly unreliable, as they are at the mercy of each and every one of our cognitive biases, but a heavy reliance on trends, best practices and anecdotal evidence is no better; these methods are imprecise and set you far behind competitors who are investing in data-driven methods of understanding your target market.
Many also mistakenly believe that big data and user analytics have the answers to all of their consumer-related questions. And while we can agree that user analytics provide companies with critical insights, we argue that relying solely on user analytics to understand your consumers is like walking around with blinders on; you see what’s right in front of you, but not what’s around you. Meaning that, you know what happened, but you know nothing about the context surrounding it, i.e., why it happened and what might cause it to happen again in the future.
If your objective has anything to do with getting people to adopt/purchase/consume your product/service/idea, you need market research. And it is not just a nifty set of tools to have handy – it’s a necessity for surviving and thriving in an ever-evolving, fiercely competitive market.
What are the different types of market research?
There are four classic techniques for conducting market research; they are currently the most frequently used methods. Let’s briefly explain what each one entails and what the strength and weaknesses of each are.
1. Focus Groups
What: A group of up to ten ideal participants* engage in a discussion led by a facilitator. Facilitators then pull insights from these discussions and extrapolate them to the broader target population. Facilitators delve deep into topics, allowing them to extract detailed verbal insights.
Strengths: Facilitators have an opportunity to ask follow-up questions and build context around these insights.
Weaknesses: The insights are non-numerical ∴difficult to analyze. Risk of bias: social desirability bias, dominance bias, facilitator bias. Very expensive and inefficient. Participants often do not know, or are unable to articulate, what they truly want.
What: A set of closed-/open-ended questions are distributed to any number of ideal participants. The answers are analyzed, key insights are identified, and those insights are then extrapolated to the broader target population. This is the most commonly used market research technique.
Strengths: Analyzing closed-ended responses is very easy because they are numerical. Analyzing open-ended responses is slightly more difficult, but still relatively straight forward. Quick, easy, and relatively inexpensive.
Weaknesses: Risk of social desirability bias. Participants often do not know, or are unable to articulate, what they truly want.
What: A discussion between one ideal participant* and one facilitator.Facilitators pull insights from the discussion and extrapolate them to the broader target population.
Strengths: Facilitators delve deep into topics, allowing them to extract detailed verbal insights and identify relevant non-verbal cues. Facilitators have an opportunity to ask follow-up questions and build context around insights.
Weaknesses: Very expensive and inefficient. Participants often do not know, or are unable to articulate, what they truly want.
What: A facilitator observes and takes notes on ideal participants (representatives of a company’s target market) engaging with a product. The facilitator then draws conclusions from those observations.
Strengths: Facilitators are able to observe relevant non-verbal cues. Easy and inexpensive.
Weaknesses: The insights are based solely on the facilitator’s interpretation of the user’s experience. Risk of bias: Hawthorn effect and facilitator bias.
Why the four just needed a little something… more.
While focus groups, surveys and interviews all suffer from the fact that participants often do not know, or are unable to articulate, what they truly want, observation suffers from an assumption on the other side of the spectrum; the facilitator assuming that they know what the participant wants. Unfortunately, when it comes to truly understanding what goes on in a customer’s mind, the traditional methods always fall short.
Over the past few decades, a new method has been emerging – pattern analysis. It aims not to supersede these classic techniques, but to supplement them. Traditional methods are like a magnifying glass, and pattern analysis is like a microscope. Traditional methods allow market researchers to take a closer look at a topic and identify the broad themes of interest that companies should be focusing on. Searching industry publications, social media, reviews and publicly available survey data is also part of looking through a magnifying glass. These tools for identifying broad themes are critical for directing our marketing efforts, but they are not powerful enough to see the specifics. This is where the need for a microscope arises, and pattern analysis is that microscope. It allows you to zoom in and delve into the intricate details of those previously identified broad themes. In a sense, the role of pattern analysis as a market research tool is to complete the road to results from where it has always ended prematurely.
What exactly is pattern analysis?
Pattern analysis is kind of like a scientific experiment disguised as a survey. It looks like a survey in that it contains a set of closed-ended questions for respondents to answer, but it is really much more than that. This activity that respondents are engaged in is actually an experiment. Researchers develop a set of stimuli that describe very specific brand/product/service features and they input these stimuli into the MindCart AI platform. The software program then uses a patented experimental design to generate a unique set of twenty-four screens, for each respondent, that include a randomized combination of stimuli, a rating question and two response options: thumbs up or thumbs down. The results are analyzed, and respondents are clustered according to similarities in their ratings of the stimuli; these clusters represent distinct ways of thinking. Pattern analysis performs two primary functions: it identifies product-specific preference segments,and it tells researchers the exact value that each segment ascribes to the tested stimuli.
Pattern analysis fills the gaps left by traditional market research methods. The cost, compared to traditional methods, is negligible. Thanks to this, users can conduct iterative testing to validate their results and develop more context around insights from initial experiments. The results are quantifiable, and therefore, easily measurable. The unique experimental design ensures that each respondent sees a different set of twenty-four screens, allowing the researchers to measure interactions within and across combinations of messages, rather than repeatedly testing the same combinations. The experimental design also effectively minimizes the risk of participant bias – as Dr. Howard Moskowitz, the founder of pattern analysis, has said in the past, “… the respondents experience a blooming, buzzing confusion”. Therefore, their responses are far more likely to reflect their true appraisal of the stimuli. Finally, and most importantly, pattern analysis identifies what people want by exposing them to stimuli and measuring their responses to those stimuli. This fundamentally different approach toward understanding people’s preference is at the heart of what makes pattern analysis so unique and effective.
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