5 Lies You’ve Been Told About Behavioral Science Market Research

5 Lies You’ve Been Told About Behavioral Science Market Research

This article originally appeared on Greenbook.com

The rise in behavioral science terms flooding into the market research community has been a blessing for some, and a curse for others. Identify and avoid these 5 lies told by behavioral science bandwagoners to avoid getting actionless insights.

If you work with consumer insights, chances are you have heard buzz statements such as “System 1”, “Behavioral Science,” or “Understanding Emotions” tossed around quite frequently over the past year. Beyond the buzz, these terms, if harnessed correctly are valuable for understanding what informs human behavior.

The rise in behavioral science (also known as behavioral economics to some) terms flooding into the market research community has been a blessing for some, and a curse for others. Research buyers who have selected trusted, qualified research partners that are well versed in applying behavioral science should stop reading this article now.

If you classify yourself into the other category of research stakeholders that have fallen victim to false claims and empty promises, the following five lies as told by legacy research companies attempting to jump on the behavioral science bandwagon will sound all too familiar:

  1. “We capture future purchase intent” – Every time I hear this, I cringe. Consumers have trouble vocalizing their thoughts and feelings in the present, let alone in predicting future behavior 30 days from now. Traditional measures of future purchase intent such as Likert scales have been shown not accurate for predicting future behavior. If future purchase intent metrics do not offer any statistically significant or predictive validity, they are not valuable enough to report on, let alone to collect from respondents.
  2. “Respondents love our surveys” – For those not in market research, online quantitative studies can be a bore. With the average length of segmentation studies creeping above 30 minutes in length, respondent fatigue and dissatisfaction comes at the expense of collecting a multitude of dimensionless metrics through scale questions. Respondent friendly surveys must be succinct to the point where they avoid long-winded open response boxes and monotonous scale type questions.
  3. “We’re powered by behavioral science” – If a firm tells you this and is unable to identify which scientifically validated methodologies they use, or what experience they have in understanding what informs behavior, the chances are high that their practice is not powered by any behavioral metrics… or science at all for that matter.

Additionally, be careful to not fall for the alphabet soup: a team full of Ph.D.’s is a fantastic resource in market research, but if not appropriately leveraged can yield actionless results for industry players. The benefits of incorporating behavioral science principles lie within the power of understanding what truly drives behavior and examining such behavior through the lens of a specific business application.

  1. “We do System 1” – Implicit association tests that evaluate nonconscious responses are occasionally believed to be the only methodology available to assess the System 1 mind. In reality, there is more to understanding behavior than gauging fast associations alone.

If we made every decision based upon what our System 1 or “fast” brain suggested, half of us would be in jail, and the other half would find themselves indulging in too much dessert at the dinner table. Emotions, experiences, and relationships are also prominent factors in understanding System 1 behavior. Evaluating the speed of attribute associations alone is not enough to understand what drives behavior. It is imperative that System 1 and System 2 research techniques be layered together holistically.

  1. “We’re innovative” – Research methodologies such as conjoint analyses and the Net Promoter Score (NPS) have been around for 30 years. Yes, these are useful tools at a thirty-thousand-foot level, but we must challenge what they tell us about behavior.

Identifying preferences is only half the battle for understanding what informs consumer choice. Without taking a step back and evaluating all of the inputs into decision making, researchers run the risk of telling an incomplete story.

Just because a large (or small) market research firm has adopted the hip terms of the day does not mean that the promised deliverables will meet expectations. If a vendor is unable to identify, quantify, and offer predictions or actionable insight into what informs human behavior, they cannot be considered “System 1” or “Behavioral Science” driven. The field of behavioral science is not limited to observing behavior, as it also can provide insight into how to influence such behavior.

Imposters will say that they have been practicing System 1 research for years, but in reality, offer little by way of methodologies or expertise. It is imperative that research buyers can separate experienced vendors from those seeking to ride a wave of popularity. Research vendors must be able to substantiate claims to be trusted and credible.

If you can’t change behavior with your insights, are they insights into behavior at all?

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How Behavioral Economics Found Its Way into Market Research

How Behavioral Economics Found Its Way into Market Research

Market researchers, regardless of the industry they find themselves working in, are in the business of understanding people. Understanding how people or consumers behave has been of interest to business leaders for decades as it can determine success in the marketplace.

By understanding who customers are and what they want, businesses can tailor their product and service offerings to match expectations.

Serving as a tool to avoid managerial bias in decision-making, market research provides a necessary ‘checks and balances’ system for business leaders.  This practice ensures that consumer needs are met and exceeded, further strengthening the triangular relationship between customer, product (or service) and brand. Business leaders must make decisions involving changes to products or services with the customer’s voice in mind, at all times.

Traditionally, consumer research has taken a normative approach. Normative views on behavior involve projecting how we believe consumers will act based upon available information. If you have ever conducted an online study that yielded positive results upfront, only to find out that what you uncovered didn’t hold true in the field, you have observed the nuances of evaluating human behavior first hand.

 

When consumers deviate from the normative path, or how we think they will act based on what we know about them or what they tell us, they engage in descriptive behavior. Descriptive behavior refers to how the person acted, regardless of the prior prediction. It is this difference between normative and prescriptive types of behavior that has given rise to a new field of social sciences that continues to grow exponentially in relevance: behavioral economics.

Behavioral economics was born at the intersection of two namesake fields: economics and psychology.

Thinking about economics, a normative field, and psychology, a descriptive field, the newfound approach to evaluating consumer insights allows researchers to observe how people behave in the “real” world when no one is watching (or paying for their input).

As Richard Thaler, the winner of the 2017 Nobel Prize in Economic Sciences, began to observe how people made seemingly irrational choices that differ from what the traditionally normative economic theory would predict, the importance of understanding how and why consumers make decisions became ever important.

Traditional market research studies (such as concept testing or brand tracking) often ask consumers to predict how likely they will be to engage with a product or brand at some point in the future. This approach is commonly believed to predict precisely how loyal customers will be, but when asked on a 7-point Likert scale without additional context, has been shown to not accurately predict future behavior.

How can it be that consumers can say how they will act at one point in time, and engage in different behavior in the future? The answer lies in the field of behavioral economics.

Daniel Kahneman, a psychologist (go figure!) who won the 2002 Nobel Prize in Economic Sciences explained this differing behavior in his 2011 New York Times bestseller, Thinking Fast and Slow. Kahneman suggests that the brain thinks and makes decisions using two separate metaphorical brains, the System 1 and System 2.

The System 1 brain is responsible for “fast, automatic, frequent, emotional, and unconscious” thinking. A few examples of thinking with the System 1 brain as explained by Kahneman are: solving the math equation 2+2, completing the phrase “war and …”, also, connecting the description ‘quiet and structured person with an eye for details’ to a specific job, all in a fast and automatic manner.

In contrast, the System 2 brain is responsible for “slow, effortful, infrequent, logical, calculating, and conscious” decisions. Examples of System 2 thinking are solving 17×24, determining the price/quality ratio of two washing machines, and actively counting the number of times that the letter A appears in this paragraph. All of these functions require a significantly more amount of time and mental bandwidth.

When consumers make decisions, they often rely on their System 1 (with the occasional moderative input from their System 2) to make choices. This approach is more efficient than thinking with the System 2 alone, saving valuable brain power to process other mental tasks that are frequent or habitual in nature.

Due to the vast amount of processing that the System 1 brain conducts, it is typically handled in the nonconscious parts of the human mind, or in the background while the System 2 is working on completing other complex tasks.

 

Without relying on their System 1 brain as they currently do, consumers purchasing a soda at a supermarket would be passed by dozens of other shoppers in the aisle while they deliberated over which brand has the best price/quantity trade-off. The System 1 uses automatic thinking that is guided by past experiences, relationships, and mental models to help make decisions faster, easier, and more automatically than the more deliberate and rational System 2 brain.

Unfortunately, traditional market research taps into the System 2 brain- an old approach that is normative in nature and does not begin to reflect how consumers descriptively make decisions.

Protobrand leverages behavioral economics to understand behavior better by tapping into the System 1 to evaluate the factors that drive behavior such as emotions and subconscious preferences.

This new approach utilizes methodologies such as visual metaphor elicitation and response latency. These tools can better predict actions as they tap into the emotional and non-conscious drivers that play a role in informing behavior. By asking questions that consumers can answer about how they feel, at the moment, researchers can better predict the choices that consumers will make in the future.

It is essential that the field of market research continues on its path to understanding what truly drives behavior: implicit preferences and emotions, to deliver actionable consumer insights to clients. Without a keen focus on identifying the drivers of descriptive behavior, business decisions using normative predications become at risk for failure.

The Value of Incorporating Qual Data into Cluster Analyses

The Value of Incorporating Qual Data into Cluster Analyses

Qualitative data comes in many shapes and sizes. When performing cluster analyses for marketing functions, the value of incorporating qual data along with traditional quant metrics is paramount.

Qualitative data are pieces of information that cannot be accurately represented by common numerical characteristics or methods. To a market researcher, “qual data” can be the most valuable, but often the hardest to acquire and analyze en masse. This data type differs from the more common quantitative data, which is classified as a piece of data with a numerical characteristic or classification.

For those not too familiar with market research and data types, I provide the following examples below:

Quantitative Data:

  • Question: Rate your satisfaction on a 1-7 scale below (1 = extremely dissatisfied, 7 = extremely satisfied).
  • Answer: 1

Qualitative Data:

  • Question: Please tell us about your meal in the space below:
  • Answer: “The pasta dish was too salty. Next time I want the chef to make it sweeter.”

While the brief example above is centered on a post-meal restaurant survey, it showcases the differences between the two types of data. While both types arrive at the same general idea, one type without the other only uncovers half of the diner’s true experience.

More advanced marketing techniques such as consumer segmentation and product groupings require advanced statistical tools known as “cluster analyses.” This type of analysis is an example of machine learning, a commonly heard phrase in today’s data-centric universe.

A cluster analysis is a statistical model that arranges data into groups with similarities that are significantly different than other groups which share their own unique sets of similarities and characteristics.

Traditionally, cluster analyses have included quantitative metrics with little inclusion of qualitative data. Only recently however, has there been a way to quantify this qualitative data. Using a technique known as metaphor elicitation (analyzed through Meta4 Insight), market researchers uncover insights by asking respondents to select an image, and then to answer a brief question describing how or why that image relates to the question being asked. This data is then analyzed with a text analytics tool, and is codified based on similar words or phrases used throughout the responses in the data set.

By employing this technique and incorporating the results into cluster analyses, the statistical model can account for a “whole brain analysis”. This whole brain analysis accounts for both implicit and explicit thoughts that are uncovered using the combination of traditional quantitative and qualitative metrics. Rather than asking respondents to answer with a set of pre-formed responses, the free response aspect and interpretation capabilities of the metaphor elicitation exercises allow for previously uncoverd thoughts to be conveyed. Incorporating qualitative data into cluster analyses strengthens the probability that the research team has captured enough viable, and emotionally relevant data to feed into the model.

Traditional quantitative metrics have the ability to tell a compelling story driven by insights and hard numbers, but when qual data is incorporated into the mix, trends and groupings are uncovered that wouldn’t have been otherwise predicted.

Welcome to the new-age of market research.

Garrett Meccariello is a brand strategist and market researcher based out of Boston. In his free time he can be found building the next great brand, exploring a new city, and eating a lot of cured meat and cheese.

The Entrepreneurs Fallacy

The Entrepreneurs Fallacy

Have a great idea? Check. Have some spare cash to fund your idea? Check. Convinced you have the best idea in the whole wide world? Check. Well, you probably think you do. Before you match that idea with your savings, conduct some due diligence to make sure that this potential business will be successful.

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Let me introduce you to what I call the “Entrepreneurs Fallacy.” The fallacy occurs when an entrepreneur, experienced or not, launches a business predicated on the idea that the concept is the best in the world and will be wildly successful upon implementation (Read: best idea without researching if there is a market or if a customer base will engage).

When asked for advice about turning ideas into reality, I recommend that friends, family and colleagues do their due diligence before jumping any further into their business model. This involves asking questions. Is there a market for the product or service? Is the idea both scalable and profitable? Does the idea fit the needs of a target market? If the answer is “no” or there is any hesitation, it’s time to step back and do some market research.

Market research is a scary phrase. It requires defining the target market and formulating questions that will gauge the market’s interest. While daunting and time consuming, a little patience and legwork can boost success.

Great ideas abound and some may be more profitable than others. However, some ideas aren’t cut out for scaling up into a full time business or product offering. The process of starting a business or adding a product or service to an existing operation is expensive and time consuming. Entrepreneurial personalities take quantifiable risks. Entrepreneurs are creative and habitually think about the “next best thing” or improvement. These personality traits accompany tunnel vision. Past success can create a false sense of confidence and bravado which inhibits a businessperson from seeking the counsel of others for feedback. Even when these creative minds do ask for outside opinions, sometimes they fail to listen. They respond to criticism with phrases like “I like it”, “I think it’s a great idea”, or “It’s going to work because I…”

Take a minute to consider what those three phrases above have in common. The person uses the term “I” rather than “we” or the “customers”. Today, running a business is predominantly customer focused. Just because a chef will only cook a steak medium rare, doesn’t mean the customer will be happy with that offering.

If the customer won’t use your product or engage with your brand, what are the chances of success? A successful business is predicated on maintaining a near cult following with a strong demand for the product or service. Entrepreneurs and business leaders must understand that there is more to running a business than personal preferences. A successful business model involves a deep understanding of the market and identifying with that target market.

Here’s a quick example of what I mean: Let’s say that you love peanut butter and jelly sandwiches. You believe that if you love these sandwiches, everyone else will, too. Five months after opening your peanut butter and jelly stand, business is slow. You ask your neighbor why she thinks business is slow. She tells you that the town has deep roots in the ham and cheese industry and favors that flavor combo. By failing to spend time determining if the product would be profitable in a particular market or if there was any market demand to begin with, you set your business up for failure. Saying, “I like this idea” will not drive demand for your product or lead to success. There must be a determination that there is a demand in your target market at the outset.

Garrett Meccariello is an aspiring brand manager based out of NYC. In his free time he can be found building the next great brand, exploring the city, and eating a lot of cured meat and cheese.