AI for Market Research

Traditional market research methodologies like large scale surveys, focus groups, and reviews are immensely potent when it comes to quantitative analysis, where researchers are looking for answers to simple questions like when and where does the customer s
pend money. However, they aren’t so good at qualitative analysis where the parameters are subjective and can’t be measured numerically. For instance, using traditional market research to answer questions like “What exactly does a customer feel when he/she interacts with the product?” may prove to be ineffective.

Let’s understand why.

Suppose a researcher conducts a one-on-one interview with several participants to find out how they feel when they interact with a certain product. The interviewer asks several questions and the respondent answers them. Now, is there any way the researcher can tell whether the respondent was telling the truth?

No, there’s not.

This is where most traditional market research avenues start to fail. There are visual and tonal cues linked to consumer behaviour that are often ignored by traditional market research methodologies. With AI-based tools like emotion AI that can use ML models and advanced Facial Action Coding System along with retina tracking and tonal analysis to uncover emotional heatmaps of research participants. Emotional insights can be coupled with traditional insights to differentiate between honest and dishonest responses, and then make decisions.

AI’s undeniable prowess in qualitative analysis does not mean that it falls short when it comes to qualitative analysis. AI can help automate manual tasks like data cleaning, sorting, and processing, since AI models can perform these tasks at unimaginable speeds with 100% accuracy.

Moreover, AI plays a pivotal role in deciphering text structures, recognizing sentiments, and extracting key themes from unstructured datasets like customer reviews and social media posts. AI models can process and analyse textual and multimedia content to uncover hidden insights about customer opinions, biases, and natural inclinations. AI-driven voice recognition systems facilitate the transcription and analysis of recorded interviews and focus groups, helping researchers uncover conversational patterns and extract meaningful insights at scale.

By automating coding and categorization processes, AI accelerates qualitative analysis workflows, enhancing the depth and accuracy of qualitative research outcomes. Lightbulb’s premier quantitative analysis tool called Insights Pro Quantitative offers a unique feature set that facilitates tasks like creative testing, user journey testing, and UI/UX testing while automating repetitive tasks making the overall process smarter and more efficient. You can sign up for a free demo here.

Qualitative analysis tries to answer objective questions like “What is the likelihood that a certain customer demographic will like the product?” “What causes customers to churn?” and “What is the customer’s perception and attitude towards a product?”.

AI offers a game-changing boost to qualitative analysis, streamlining processes with quicker data processing and surfacing hidden patterns that might not be perceptible to humans. It dives deep into the contextual fabric of qualitative data, helping researchers grasp nuances that enrich understanding. Emotional insights add another layer of detail to the overall insights, helping researchers enhance the quality of insights and hence the decisions driven by them.

By spotting complex cases and outliers, AI adds depth to the analysis. Beyond its analytical prowess, AI fosters inclusivity in academia by levelling the playing field, granting researchers from all backgrounds access to advanced tools and resources, empowering them to pursue meaningful and independent research endeavours.

One such AI-based tool for qualitative research is Lightbulb’s Insights Pro Quantitative that uses facial coding, voice transcription, and image processing to analyse live and recorded interview sessions. This helps reduce manual effort from the entire process and offers a speedy turnaround time for instantaneous results. You can sign up for the free demo here.

We have discussed at length how using AI in market research enhances the efficiency of the entire process, offering rich and user-centric insights. Here’s a summary of the inherent advantages of using AI in market research.

Although using AI does lend a helping hand to researchers and exponentially raises the efficiency of the resulting insights. Doing so does not come free of challenges. We have mentioned some of the most significant challenges to AI adoption in the market research space.

We’ve already introduced you to Lightbulbs premier market research tool Insights Pro. Here are some of the other popular AI-based market research tools that you can use to propel your market research efforts

Pecan AI is a powerful market research tool that uses an advanced predictive analytics model and advanced machine learning capabilities to unearth crucial user insights that steer pivotal decisions in an organisation.

Speak is a specialised AI-based market research tool designed to transform unstructured audio and video feedback into actionable consumer insights using natural language processing (NLP). With Speak, research teams can convert a variety of sources such as consumer interviews, digital recordings, podcasts, YouTube videos, and focus groups into a valuable and actionable dataset.

Appen offers a comprehensive market research solution encompassing a wide array of data services, including data collection, data preparation, model evaluation, ad evaluation, benchmarking, and more. Their suite of products serves as a reliable means to accumulate extensive datasets tailored for training and AI testing purposes, facilitating the development and refinement of artificial intelligence technologies.

Insights Pro is an AI-based ad testing tool that combines emotional intelligence with new-gen AI tools like speech transcription, facial coding, text sentiment analysis, and eye tracking to generate an emotional heatmap of an interaction between a viewer and a creative while also offering AI-based databases and tools for creative optimisation to accelerate the creative testing process.