AI Foundations: Part 2 – AI and ML in Market Research

Let’s Understand their Roles and Applications

Gaining Actionable Insights from Data is the foundation of the Market Research Discipline. Naturally, the developments in data-focused domains like AI and ML have the undeniable power to transform the way Market Research is conducted.

In fact these sciences are not new in the world of MR. Foundational models of Machine Learning like Decision Trees were developed over 50 years ago.

• So why do we need to talk about it today?
• Availability of advanced computational power
• An ever growing pool of data
• And improvements in learning algorithms and techniques are propelling machine learning (ML) applications into new realms of possibility.

Combined with the launch of generative AI, the landscape of insights from data has fundamentally changed. By enabling the creation of new data, synthetic insights, and advanced predictive models, Generative AI has expanded the scope and depth of capabilities beyond its traditional role in Data Analytics.

As these technologies continue to evolve, their impact on market research will only grow, making it essential for industry professionals to understand where and how they can be used in a relevant and responsible way.

This article is a primer on the role of AI and ML in Market Research and Consumer Insights.

 

What is AI & How is it different from ML

At the outset, AI refers to the broader concept of machines being able to carry out tasks in a way that we would consider “smart.” It encompasses a wide range of technologies designed to simulate human intelligence.

Whereas, ML is a subset of AI that involves the development of algorithms and statistical models that enable computers to perform specific tasks without using explicit instructions. Instead, ML systems learn from data and improve over time.

Want to know more about how AI and ML differ from each other? Read our first article in the AI Series: Is it AI or ML to understand the terms and learn how to tell one from the other.

Both AI and ML can play an important role in modern market research by enhancing the ability to process vast amounts of data, generate actionable insights, and optimize research methodologies for better decision-making.

 

Applications of AI & ML in Market Research

The core difference in their applications in Market Research stems from the distinct learning approaches they employ.

AI encompasses a broad range of systems, such as rule-based systems and expert systems, and often integrates other forms of intelligence like vision and language. AI can adapt to different types of tasks beyond pattern recognition, such as decision-making and problem-solving. Learning in AI can be more general and flexible, incorporating various methods, including ML techniques.

ML refers specifically to systems that learn and adapt through algorithms applied to data. ML models are specialised in learning from data and improving performance through experience. Learning in ML is more focused on pattern recognition and making predictions based on data.

They are also different in their implementation and the level of autonomy that is enjoyed.

• AI systems may possess fully autonomous decision-making capabilities once trained by humans on large volumes of data.
• ML systems need human involvement to prepare and pre-process data, select appropriate algorithms, and tune hyperparameters. Once set up, ML models can learn autonomously from the data.

In this deep dive, we will focus primarily on Generative AI as a subset of AI and Machine Learning.

We’ve already spent time understanding what constitutes Machine Learning, so let’s take a closer look at Generative AI.

Generative AI refers to models that create new content or data that mimics the characteristics of the training data. These models generate novel outputs rather than just making predictions or classifications.

For the sake of accuracy we must mention that while ML and Generative AI are distinct in their primary objectives, there is a significant overlap. Generative AI often employs ML techniques to learn the underlying data distributions necessary for generating new content. But it’s useful to classify Generative AI and Machine Learning as two key aspects of AI when we focus on their applications.

 

From Design to Deployment: AI in Market Research

The Market Research Process has three distinct steps: Design, Data Collection and Diagnosis

And today, AI has made in-roads into influencing the Research Process at each step!

Research Design

The recent advances in Generative AI provide a clear advantage in the Research Design Process.

This includes taking over a lot of the documentation that goes into designing a Research Plan right from brief creation to stimuli development to survey designs. The ability of Generative AI to use expert frameworks to develop new content provides an invaluable aid in automating these aspects of Research that was typically purely manual saving a lot of Researcher time,

Data Collection

Beyond documentation, Generative AI can also be used as creative methods of data collection reducing the manual effort needed to conduct interviews and collect feedback by using dynamic surveys, and automated interview bots. These give businesses the ability to collect data across different points of the customer journey, personalize the questions to the individual’s specific experience and capture relevant feedback.

Finally AI is also beginning to play a role in Synthetic Data Creation – these can be data augmentation or filling in gaps in information or persona creation. When actual data collection is challenging or limited, this ability of generative AI to create synthetic data that mirrors real-world data distributions can solve data scarcity problems.

Analysis

As we move from the generative steps of design and data collection towards analysis, the role of Machine Learning starts dominating the Research Outcomes that are required from the process. Machine learning helps with all forms of data classification techniques that form the basis of many analytics techniques, be it the analysis of structured data like segmentation, identification of data issues / outliers, and predictive modelling.

Subsets of Machine Learning like NLPs and Deep Learning systems help in analysis of unstructured data to define sentiments, themes etc. which are used to understand large scale open ended comments and opinions.

It’s evident that between Generative AI and ML every step of the MR process can be augmented by AI – right from the initial design phase to data collection, analysis and reporting.

Their ability to enhance efficiency, take over grunt work, and uncover deep insights makes them invaluable tools for modern researchers.

At InsightGig, we use the combined capabilities of AI and ML to bring you a suite of apps to augment and accelerate your research process. Check out our AI tools >

 

However, the story doesn’t end here. In fact, it shouldn’t.

To fully appreciate the transformative potential of AI and ML in market research, it’s crucial to consider the broader context in which these technologies operate.

This includes the ethical implications of their use, the evolving relationship between humans and machines, and the future trends that will shape the industry. It’s only by considering these areas along with the pure potential of the technology can we better understand how to harness AI and ML responsibly and effectively, ensuring that the insights we derive are not only powerful but also fair, secure, rigorous and forward-thinking.

 

Bias & Other Ethical Considerations

1. Bias in Survey Design and Stimuli Development:

• When using AI to automate the creation of research designs, surveys, or stimuli, there is a risk that the over reliance on AI might introduce or perpetuate bias leading to non representative outcomes. Being representative of the target population is a core research responsibility, hence care must be taken by us as researchers to review and ensure that the stimuli used is as it would be when curated manually if not better.
• We have already emphasized the role of Generative AI in creating synthetic data to fill gaps where real-world data is scarce and this could be a great advantage. However, the ethical challenge lies in ensuring that this synthetic data accurately reflects the diversity and complexity of the real world, without reinforcing stereotypes or biases. This is a guardrail that we must never forget to reinforce even as we enjoy this newfound potential to fill in data gaps

2. Transparency & Informed Consent

• AI-driven data collection methods, such as automated interview bots or dynamic surveys, can collect large amounts of data quickly and across multiple points in the customer journey. However, participants may not always be fully aware of how their data is being used, leading to ethical concerns about consent and transparency. As responsible researchers, we must ensure that the respondent experience is paramount, and they are fully aware of how their data is being used.
• The use of AI in data collection raises significant concerns about the privacy and security of participant data. AI systems are often trained on large datasets, which can include sensitive personal information. If these systems are not adequately secured, they could become targets for data breaches or misuse. Data privacy and confidentiality in this context becomes even more important to secure and safeguard
• Automated data collection tools can streamline the research process, but they may also reduce the level of human interaction and empathy in the research process. This could lead to a lack of understanding of the participant’s context, particularly in sensitive research areas and impact both the feedback experience for the participant and insight generation for the business. It is crucial for us as researchers to remember that AI tools are designed to complement, not replace, human interaction, and thoughtfulness should not suffer at the hands of automation.

3. Traceability & Explainability

• One of the major ethical challenges in using AI and ML for data analysis is the “black box” nature of many models, where the decision-making process is not easily interpretable. AI models often capture complex, non-linear interactions between variables that are difficult to decompose into simple, human-understandable rules. These interactions can be crucial for the model’s accuracy but make it harder to explain the decision-making process. This lack of transparency makes it difficult to identify the reason behind recommendations & decisions when it’s solely based on AI. Until AI models evolve to solve for explainability, the closer the researchers can be to the process of the research, even if it is automated by AI tools, the greater the chance of determining outcomes which are reasonable, efficient and effective for the business.

 

The Human-AI Collaboration

AI and ML are powerful tools, but that’s what they are… TOOLS.

They are most effective when used in conjunction with human expertise. This is especially true as we explore the ethical considerations that we need to keep in mind as we adopt AI systems. Instead of viewing AI as a replacement for human researchers, we see them as an enabler of efficiency and effectiveness.

AI can handle the heavy lifting of data processing, documentation, quality control etc – it doesn’t fatigue, it doesn’t get bored and can process far more information in far shorter time than humans can.

However as researchers, we must remember the privileges that we must always keep for ourselves without ceding control to any AI system we might adopt. This is the privilege of knowing how we want to interact with our respondents, what are the questions that must be answered to drive business outcomes, and how best can this consumer’s story be told to drive action.

As systems evolve and contexts change, these privileges might change too. But the fundamental philosophy of determining the privilege we want to cede and that which we want to hold will be foundational in building our relationship with AI.

 

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