There is always room for ambiguity when it comes to research findings. A margin for error. Except for this margin for error may just end up costing marketers a failed campaign. Among research communities, this margin of error is called bias. And we solve this error by conducting AI-led behavioral research.
Bias could come in various formats (we shall address this later) and sources. It could be from the respondent’s end or the researchers’ end. Where ever it may originate from, bias within in research is highly undesirable.
How Does Bias Creep In?
Social Desirability Bias
Humans look towards their peers for validation and acceptance. When giving surveys or attending FGD, this reflects in their answers as they assume it would be the socially accepted answer. This affects research findings and marketers craft campaigns that are set up for failure in the market.
Confirmation Bias
This happens on the researcher’s side. Sometimes, researchers ardently want their hypothesis to be true. In the haste of proving it right, they chose to only delve into the responses that align with their hypothesis and ignore the rest. This results in marketers being given a skewed understanding of their consumers.
Habituation Bias
When respondents must sit through a survey, respondents might give similar answers to all questions. This happens when similar questions are grouped together, and respondents are habituated into answering similarly for all. Not all positive feedback is good feedback.
Have a look: How to tackle Cultural Response Bias in 2022
When Bias Torpedoed Brands
New Coke
Pepsi was in its prime, and Coca-cola wanted to take over. Introducing “New Coke’. Respondents repeatedly chose New Coke in a blind taste test. However, when it hit the market, consumers were infuriated that coke didn’t taste the same. They didn’t care if it tasted “better.” This was a case of confirmation bias when researchers and marketers wanted an output and chose to ignore other factors such as packaging, brand, product name, etc.
Arch Deluxe from McDonald’s
McDonald’s tried to release a ‘sophisticated’ burger. In the consumer research they conducted, adults were excited about the burger. However, when the burgers hit the stores, it did not fare well. This can be due to a social desirability bias among the respondents to seem likable to big brand like that of McDonald’s.
The Future is Un-biased
AI is updating processes and smoothening workflows all over industries. It’s about time it entered consumer research. Introducing AI-led behavioral research, embedded in SaaS platforms that can be incorporated into research workflows to ensure minimum biased insights.
Researchers can select panels, send links to respondents, conduct surveys, display stimuli for consumers to react to, all the while gauging their behavior – through their web cam or mobile cam.
Also read: The Future of Consumer Research: Online Research Platforms
AI Technologies to Drastically Reduce Bias
Facial Coding
Once a respondent has consented to the use of their camera, researchers can gain insights into their facial expressions. Intelligent algorithms are in place to categorize human expressions into segments of emotion. These help researchers in understanding their actual sentiments, which in turn helps marketers gauge the consumer’s real reaction.
Eye Tracking
Humans are visual thinkers and learners. Our eyes catch the attention of visual cues, some of which we may not fully process. When people see a product/ad/TV show, it is crucial to identify where their eyes naturally fall. With intelligent eye-tracking technology, researchers can now study how the eye moves across a product/ad/TV show. This can help marketers create campaigns that accurately appease their consumer’s eyes.
Voice AI
When conducting surveys, in-depth interviews and focus groups it is desirable to understand the respondents’ intent and authenticity. AI-led behavioral research combines voice intelligence with NLP to give researchers a deeper understanding into the responses. This way, researchers can ensure that they are considering genuine responses and not misreading the intent/emotions of the respondent during transcript analysis.
AI-led Behavioral Research is the Answer
It is high time researchers adopted AI-led behavioral research technologies and platforms to conduct unbiased research.
Gone are the days when behavioral research was time-consuming, hardware dependent, and expensive. With artificial intelligence in the picture, researchers need not fall back on these traditional methods. AI-led behavioral research enhances agility by reducing logistical overheads and eliminating hardware dependency. This makes behavioral research exponentially more scalable and easily accessible. It is time to normalize AI-led behavioral research and use it as a regular methodology along with qualitative and quantitative studies.
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