AI-led Behavioral Research

AI-led behavioral research is a growing trend in consumer research that uses machine learning algorithms to analyze and predict consumer behavior.

Tanvi Moitra
March 24, 2023
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The behavior of humans is driven by a variety of factors. Every individual feels, thinks and acts differently. This has helped humans become the most evolved species. But, as a Researcher who is trying to navigate the tangled web of brain neurons to derive actionable insights, it is nothing but an arduous exercise. But don’t worry, AI-led behavioral research is here to modernize and scale up your behavioral studies.

You don’t need to be Magneto to decipher what your consumers want. Even though behavioral research is expansive, diverse and predominantly logistics heavy, it is one of the best ways to assure success in this highly competitive market. Keeping up with the shift in consumer preferences may seem like an unending task, but it is highly rewarding.  

Monitoring behavioral factors like- 1) subconscious thoughts, 2) unstated motivations, 3) emotional triggers, 4) social influences and 5) contextual effects will allow you to reap the best out of your research investment. Validating quantitative and qualitative insights with unbiased behavioral insights fosters not only innovation but helps you stay on track by taking better data-driven decisions.

Even though behavioral research is the key to understanding consumer behavior, researchers usually shy away from it. This is because the traditional process involves high dependency on hardware causing increased cost, scalability issues and higher turn-around-time.  

In this article, we’ll address how behavioral research has come a long way. As a researcher, you will be able to understand more about how you can optimize your research process and eliminate the above-mentioned pain points with the adoption of AI led technology.  

So, let’s dive in!

Behavioral Research- A Deep Dive

What is Behavioral Research?

66% of customers expect brands to acknowledge their needs. Behavioral research is a great way to know your audience better and provide an unforgettable experience.

Behavioral Research is the examination of cognitive processes and analysis of an individual’s behavior and interactions. People have always been intrigued about the factors that affect a person’s behavior. The study of human behavior is not new and has evolved since 1960.  

Recent consumer research attempts to decode the many aspects of human experience. It considers the impact of biological, social, and cultural factors on consumer behavior. Modern Research uses technologies like facial coding, eye tracking and voice AI to delve deeper into the subconscious behavior of consumers.  

When you have behavioral data to help you streamline your marketing and product initiatives, you don’t need to remotely rely on guesswork and intuition. This removes any shadow of doubt about the success of your product or service and helps validate your ideas and campaigns.

Bias, The Limiting Factor for Accuracy

Bias is an unfair inclination towards people, groups, ideas, or things. It can be a result of multiple factors like- social, cultural, past experiences etc.  

It mostly stems from the human brain’s tendency to segment new information. Be it new people or new ideas, the brain correlates them with past experiences. As a result, the person’s natural response automatically gets skewed for or against the new person or idea. For example, have you ever bought a candy bar and didn’t like it? It could be from any brand, but your brain remembers it as something you felt an aversion to. Now when you see a new candy bar which is similar in any way, your brain signals you to be wary of it.  

It does seem unfair that you will suffer from bias in Quant and Qual studies just because someone didn’t like a particular thing in the past. But completely ignoring the impact bias has on research results can do more damage than acknowledging it and not doing anything about it.  

Bias can be of many types but the deadliest of them all is response bias. It is a one-sided love story. Imagine trying to know your audience better but you receive skewed, inaccurate, and false responses from the respondents. It may hurt your feelings, but research is research.  

Blindly trusting data derived from research can potentially mislead you. The domino effect of false conclusions can prove to be fatal for your research and the brand. But like a scorned lover, don’t give up just yet. Response bias may derail your research but if dealt with correctly, you can be rest assured that your data quality is good and bias levels are at a bare minimum.  

Related Read: How to tackle Cultural Response Bias!

A Montage of Behavioral Science

The goal for any researcher is to provide data that will empower other teams and holistically benefit the brand. But researchers face challenges like- 1) scalability, 2) market uncertainty, 3) bias and data quality, 4) time to insights and 5) data collection.  

These challenges majorly impact the way research is conducted. An ineffective method will leave loose ends causing your results to be vulnerable to fluctuating market dynamics. The results will be outdated, and your competitors will have an edge over you.  

It’s time to turn the tables and play your ace card. Behavioral Science is being widely adopted by brands and research agencies. According to McKinsey, over the past two decades, behavioral science-driven insights have been used to reduce biases, take better strategic decisions, improve customer experience, improve marketing campaigns, foster innovation and avoid making bad investment decisions.  

With its complex social structure and enormous capacity for learning, our species benefits greatly from the ability to communicate. Expressing intentions to one another and making requests of one another. Verbal communication enables cooperation and allows us to establish norms and rules of behavior. Perhaps the evolution of this ability has resulted in the phenomenon of consciousness. That is, our ability to send and receive messages from other people allows us to send and receive messages from within our own minds, to think and be aware of our own existence. [1]  

A multitude of factors influence the choices we make, some conscious and some subconscious, for example-

  • Psychological – Motivation is one of the key drivers of any decision-making process. Unmet needs frequently spur people to action and have an impact on their behavior. A person’s self-confidence to complete a task also impacts their choices and actions.
  • Behavioral – People’s behavior and decisions can be influenced by a variety of factors, including their culture, values, risk assessment, and whether a choice clashes with their attitudes or beliefs.
  • Biological- Behavior and emotions can be influenced by variables like age, sex, and genetics. People may inherit qualities that influence qualities of behavior like impulsivity or reluctance.
  • Societal – To fit into a social group, people may alter their behavior and beliefs. To conform to the expectations of their own social roles or perceived authority, people may also change their decisions or views.

Behavioral Science attempts to decode exactly what triggers our unique behavioral patterns. It may sound like a fancy word, but it is not new. Behavioral Science gained popularity between 1920-1950 because of prominent thinkers like B.F.Skinner and J.B Watson.  

The most popular ways to understand a person’s behavior are through the study of neuroscience and cognitive science. The methods used for studying the brain and the sensory organs remain the same, but they have evolved over time to become more accurate, fast, agile and scalable. Let’s learn about how AI has changed the game-  

Behavioral Neuroscience

This study involves monitoring neurotransmissions in the brain. It analyzes the interplay between the person’s brain, behavior, and environment.  

It evolved from several scientific and philosophical traditions in the 18th and 19th centuries. René Descartes and other philosophers suggested physical models that explain both animal and human behavior.

After years of experimentation, it was realised that the brain, not the heart, was the proper location for the seat of thought and emotion. Despite the knowledge that the brain serves as the connection between the mind and the body, human consciousness was thought to be too abstract to be studied. Descartes believed that this dualism was a fundamental characteristic that set humans apart from other animals.  

One of the most popular methods to study behavioral neuroscience is EEG (electroencephalography).  


Hans Berger recorded the first human EEG in 1924. During an EEG Test, small electrodes were placed on the scalp to pick up the brain’s electrical signals. In today’s world evaluating the mental states of potential and existing consumers through neuroscience can help explain the cognitive, emotional, and possible triggers that influence decision-making. This technique can be used for validating an idea, verifying the efficacy of an ad or product, etc. EEG has the potential to act as a compass for navigating through various consumer thoughts.


EEG older machine



EEG Headband


The motive behind conducting EEG tests were mostly medical in nature. But, over time its potential was realised, and it was soon incorporated as part of market research. However, the problem with traditional EEG testing techniques were that they looked like torturing devices and were extremely inconvenient. The EEG machines required tons of wires, electrodes and a highly specialised person to run the test. Now, you can simply wear an EEG headband and let AI monitor brain activity on the go. It requires less time, is lightweight and with its intelligent technology, it does not need a controlled environment for conducting the test.  

Behavioral Cognitive Science

This studies the mental actions evoked through experience, thought or senses. There are five major topic areas – perception, language, learning, thinking and representation of knowledge. This study gained prominence as a part of an intellectual movement called the cognitive revolution in the 1950s.

Cognitive Science

Cognitive Science is used to understand a multitude of topics, for example-  

  • Attention- It is a cognitive process where a person selectively concentrates on a particular element, while ignoring others. A person is subjected to millions of stimuli on a day to day basis, but only a few are focussed upon by the human brain.
  • Memory- It is the process by which a person takes in information, processes it and then stores it for retrieving later. Cognitive science tries to understand the relationship between memory and cognition. For example, triggers of certain memories or recall.
  • Perception- We take in a lot of information on a daily basis, how we process using our senses is known as perception. Perception is mainly visual, auditory, haptic, olfactory and gustatory.  
  • Action- The output of what we perceive often translates into actions. Various motor responses display what a person feels internally.

A few popular methods for studying behavioral cognitive science are-  

Eye Tracking

The origins of eye tracking can be traced back to 1879, when French ophthalmologist Louis Émile Javal discovered for the first time that readers’ eyes do not skim fluently through the text while reading, but instead make quick movements (saccades) intermixed with short pauses (fixations). In the absence of more advanced technology, these studies relied on naked-eye observations.  

Edmund Huey created a device that tracked eye movement during the reading process in 1908. The first eye tracker was extremely intrusive, requiring readers to wear contact lenses with a small opening for the pupil. The lens was attached to a pointer, which moved in response to the movements of the eye.


Eye Tracking older machine



Webcam based eye tracking

Eyes are supposed to be the portal to your soul. But here, this technology is the portal to a successful campaign. Eye Tracking is used to monitor what the consumer chooses to look at, the directional path the eyes follow and the frequency. But, the traditional eye tracking tool had limited capability, was intrusive and required test takers to wear a heavy helmet.  

Eye tracking tools evolved where most hardware-based Eye Tracking devices use infra-red technology with a high-resolution camera to monitor and predict gaze. The light reflecting from the cornea and pupil centre are used to monitor the eye movement.

Most AI-powered behavioral research platforms have an explicit eye calibration to develop a self-learning model for higher accuracy. This reduces overall dependency on hardware and reduces expense as software-based eye tracking can be done from anywhere with the use of webcam. It does not require a controlled lab environment.

Imagine spending millions of dollars on package redesigning only to realise consumers look only at the brand logo and not the rest of the packaging. You could have saved that amount of money with this simple technology.

As a researcher, this can help you-  

  • Evaluate what influences attention and engagement  
  • Get a clear understanding of what consumers find appealing
  • Gather accurate data about what influences the purchase decision of a consumer
  • Analyze how consumers navigate through your app, website, or store
  • Optimize the noticeability of your product or service

Related Read: Eye Tracking Technology Whitepaper

Facial Recognition

Facial Recognition is over 50 years old. It started with manual measurements in 1964 by Woodrow Bledsoe, Helen Chan Wolf and Charles Bisson. Because the coordinates of the facial features in a photograph had to be established by a human before the computer could use them for recognition, their early facial recognition project was dubbed “man-machine.”


Facial Recognition old technique


Wecam based facial coding

It was soon realised that Shakira’s hips can lie but your face won’t. Facial Coding stemmed from the idea that human emotion can be measured through facial expression recognition. Our face is capable of making 10,000 facial expressions. These expressions are natural and difficult to suppress as the muscles involved are directly triggered by the brain.  

Facial Coding


AI-based Facial coding technology is used to read these muscle movements and help understand the underlying emotion. There are a few basic emotions like- happiness, sadness, anger, fear, surprise etc. Evaluating the emotional journey of a consumer helps evaluate their emotional triggers and get honest feedback about your product or service.  

The Facial Action Coding System (FACS) was originally developed by Carl-Herman Hjortsjö and updated by Paul Ekman and Friesen in 1978. The components of the facial movements are measured in terms of Action Units (AU).

Modern facial coding technology uses advanced machine learning based on an artificial neural network with multiple layers between input and output. This goes via a series of nonlinear computations, combining lower-levels of information to form higher-level features (e.g. expressed emotion). Computer Vision algorithms are also used to identify facial features.

Earlier the facial coding system was manual and not fully automated. With AI, the dependency on manual work has been reduced. This has led to higher accuracy and reduced turn-around-time. It has also made the entire test taking process a whole lot easier as you only need a webcam for your expressions to be recorded and analyzed.  

As a researcher, this can help you –

  • Reduce Bias
  • Get reliable and accurate data
  • Tap into the subconscious mind of the consumer
  • Quantify the consumer’s emotional response and triggers
  • Understand what influences their choices

Related Read: Facial Coding Technology Whitepaper

Voice Recognition

The first example of modern speech recognition technology was “Audrey”, a system which was designed by Bell Laboratories in the 1950s.

Voice Recognition Audrey


This technology soon found its home in qualitative research. It was automated by integrating it with AI technology to evaluate the change in pitch and tone to give you insights into the underlying feelings of a consumer. AI interprets, processes and converts speech to text to give easy accessibility to information post-conversation.  

Imagine in a focus group discussion you could make out if someone is genuinely responding to your questions. That would save you the hassle of segregating accurate responses from the ones that were given just for the sake of it.  

This finds application not just in consumer research, but sales calls, virtual assistants etc.  

Voice AI


As a researcher, this can help you-

  • Get accurate responses  
  • Discover if a potential customer is going to convert
  • Evaluate underlying feelings
  • Have genuine conversations
  • Conduct your qualitative research better
Infographic traditional vs ai based research tools

Behavioral Research Is A Hard Nut to Crack- Better Call AI!

From watching the Matrix and questioning our very own existence to being elated seeing WALL-E save mankind, AI has been a topic of controversy. There is intrigue and then there is skepticism. We worry about being enslaved by robotic AI and losing our jobs to it. But AI is not some monster created by mankind for its doom. It is a resource with unlimited potential.  

Related Read: Artificial Intelligence for consumer research. Is it worth the investment?

The paranoia around Artificial Intelligence has caused a delay in its adoption in research. AI can identify patterns which we as humans may miss. For example, every purchase you make on Amazon gets recorded by AI which then recommends more products to you based on your behavior and past purchases. This drives 35% of Amazon’s revenue!

With the increasing complexity of the market and the consumer’s mind, it is very difficult to optimize time and deliver the best product out there. As a researcher, you will have the burden of delivering results on time every financial year. But with AI, you will have more than 24 hours to your day. With a click of a button, you will be able to analyze data within a matter of minutes. Not just that, today AI is used to interact and gather underlying data while conducting research, but in another decade or so, AI will be running the entire research show.  

From maintaining data integrity to the nitty gritty, AI has the potential to deliver accurate data which will not be bound by a specific data set it is trained on. AI can mimic human behavior, and this in the future can help teams cross-collaborate to find the optimal buyer.  

You may feel AI is mainstream or just a tool, but it is that Wild card in your consumer research deck, that can completely change the game for you. Letting AI work with you for you can help you not only harness the true potential of consumer research but also yourself, as a researcher.

AI meets Myth- Still a better love story than Twilight

#1 AI is not a necessity

91.5% of leading brands invest in AI regularly. Now imagine, you are trying to launch a new product in the market, but by the time you are done with your research, another brand has already taken the spotlight.  

Don’t underestimate the power of AI. It can help you stay a step ahead of your competition while making your life a whole lot easier.

#2 We have to be too tech savvy

If you still think that adopting AI means turning researchers into computer engineers, you are wrong. Most AI-powered tools require no upskilling, just basic tech-savviness.  

With the advent of DIY platforms for conducting consumer research, you can conduct research in-house without reading up on AI-related tech.

#3 Bills…Bills…Bills

AI sounds fancy but it doesn’t necessarily mean it will burn a huge hole in your pocket. AI-led research is an investment. With actionable insights, you not only save time but a ton of money.  

In today’s world time is the real currency. With AI you can optimise your entire research process and save on those unnecessary expenses that were slowly draining your money and resources.

#4 Revamp of Tech Stack

Adopting AI does not mean you have to uproot your existing tech stack. With the use of APIs, online DIY research platforms, SDKs etc, it is easy to integrate your existing technology with AI-based tools.  

# AI can’t understand us

If you think AI cannot understand the nuances of human behavior like a human, take a look at virtual influencers, self-driven cars, virtual assistants etc.  

AI can identify underlying patterns, emotions and trends which humans can miss with their naked eye.  

Key Takeaway

Behavioral Research is essential to foster innovation and drive growth. As a researcher, if scalability and accuracy are your major problems, incorporating AI in your behavioral research is the ultimate way to achieve success. The skepticism around AI may be a result of scientific dystopias but it is an efficient technology which can completely change the way you conduct research. It provides fast, accurate and actionable insights with minimal hassle. It also allows you to validate your decisions and make better data-driven investments. Don’t be left behind and adopt AI for consumer research today!


Supercharge your research with actionable insights faster on Decode's integrated consumer research platform with Insights AI.
Want to conduct lean and unbiased research? Try out Entropik's tech behavioral research platform today!
Want to conduct lean and unbiased research? Try out Entropik's tech behavioral research platform today!
Want to conduct lean and unbiased research? Try out Entropik's tech behavioral research platform today!
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Author Bio

Tanvi Moitra
Tanvi can usually be found anxiously treading the office floor to get her content reviewed, here at Entropik. When not absorbed by researching and writing, she loves to read, go for a swim, play badminton, paint, and otherwise spend too much time bingeing on the Office and cuddling her German Shepherd, Whiskey. An absolute foodie, she would love to cook and bake for you and even give you the best dessert suggestions in the office.

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