AI for Market Research

AI for Market Research

If you’re a market research professional, business owner, or a marketer, you will most certainly resonate with the core proposition here. With the increasing complexity of datasets and constantly evolving customer behaviour spectrum, moving beyond traditional market research methodologies is paramount. One avenue that facilitates this with great efficiency is AI. Using AI for market research offers certain advantages, the most important one being the immense processing capabilities of modern computers coupled with the versatility of machine learning models that can do anything from uncovering hidden patterns to surfacing emotional insights.

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Unveiling the Impact of AI in 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.

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AI in Quantitative Analysis

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.

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AI in Qualitative Analysis

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.

Advantages and Challenges of Using AI in Market Research

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.
Greater Efficiency
NLP models facilitate rapid processing of vast datasets from multiple sources such as focus group transcripts, survey responses, social media posts, and reviews. Which allows professionals in identifying trends, patterns, and sentiment shifts much faster than traditional methods.
Richer Insights
AI based prediction models can analyse structured and unstructured data rigorously and consistently to uncover hidden correlations between certain attributes and customer behaviours.
Automation of Redundant Processes
Advanced AI tools can automate focus group discussions and moderate them for days without break. This makes this process easier and offers on-demand access to consumer perspectives without the logistical challenges of recruiting and running traditional focus groups.
Custom Surveys
AI models can tailor surveys to reflect research goals and target audiences, leading to higher engagement and more accurate insights.
Early Trend Identification
Consistent analysis of multiple data sources such as news, financial data, and research reports can help identify early signs of emerging trends, disruptions, competitor strategies, and industry shifts.
Enhanced Scalability and Accessibility
Chatbots integrated into messaging apps, websites, and other platforms allow for direct interaction with target audience members at scale.

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.
Over Automation
Although AI algorithms can sort, clean, and process data at scale without human interruption, lack of human supervision at key points may lead to complications. Unlike humans who’ve real experience in the space, machine learning models operate on pre-defined data sets and lack key industry insights, which may lead to oversight.
Lack of Synergy Between Quantitative and Qualitative Teams
Rather than relying on algorithmic excellence alone, market research professionals should employ both machine intelligence and human expertise. For instance, while algorithms design and conduct surveys, data analysts should keep a watchful eye on the insights to spot outliers or discrepancies.
Fighting Privacy Issues
Since modern AI models operate on large datasets, more customer data is being mined than ever before and this can cause some serious cyber security and privacy issues. Let’s take emotion AI for instance, not all respondents would consent to being monitored in a live session, or being recorded for sentiment analysis.
Lack of Dedicated In-House Resources
Not all organisations have an existing fleet of data scientists, engineers, and analysts to run and maintain AI-based market research campaigns. The digital infrastructure needed to successfully incorporate AI in traditional market research does not come cheap and isn’t easily accessible.

Some Real-World Examples of AI being Used in Market Research

Amazon
Amazon, the global e-commerce giant, has a powerful recommendation engine that uses AI to analyse customer’s browsing and purchase history and dynamically refresh the recommendations on their homepage for every customer.
Facebook
The social media behemoth uses sentiment analysis to understand the sentiments and views of users in the comment section. This unveils crucial insights on user preferences that helps them identify trending topics and run targeted ads for better conversions.
Google
Google has one of the most powerful predictive analytics models that forecasts market trends and user preferences. These powerful AI prediction models analyse user data to reveal search trends, consumer preferences, and emerging topics to help businesses make informed decisions.

Popular AI-Powered Market Research Tools

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

Pecan AI

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

Speak

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.
Appne

Appen

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.

About Insights Pro

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.

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    Other technologies similar to facial coding

    Eye tracking
    Speech transcription
    Text sentiment
    Audio tonality

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    Frequently Asked Questions

    Facial expressions coding involves capturing and interpreting facial muscle movements known as action units (AUs). Each AU corresponds to specific emotions, providing a systematic way to decode facial expressions.

    The Facial Action Coding Scheme (FACS) is a comprehensive system developed for precisely describing facial expressions. It assigns numerical codes to different facial movements, aiding in understanding emotional states across diverse contexts.

    Facial expression analysis is a methodical examination of facial behaviors to decipher underlying emotions or reactions. It encompasses studying the intensity, duration, and sequence of facial movements to gain insights into emotional responses.

    Emotion detection technology utilizes various techniques, including facial emotion recognition through computer vision algorithms, analyzing physiological signals like heart rate variability, voice modulation analysis, and sentiment analysis of text data, enabling a multidimensional.

    Facial expression analysis involves scrutinizing facial movements to discern emotional cues. By analyzing the dynamics of facial expressions, researchers and practitioners can glean valuable information about individuals’ emotional states and responses to stimuli.

    Although there are six primary facial expressions recognized universally—happiness, sadness, anger, surprise, fear, and disgust—there’s a spectrum of nuanced expressions reflecting complex emotional experiences. While these basic expressions serve as foundational categories, human emotions are multifaceted and diverse, leading to a myriad of facial expressions beyond the fundamental six.