20 Wide-Ranging Applications of AI in Market Research

The way we use AI has evolved. A joint study by Harvard Business School and Boston Consulting Group highlights this.

The researchers tracked how AI impacts worker productivity and accuracy (judging by your interest in this post, those two things are dear to you). The researchers found that workers who used GPT-4 completed 12.2% more tasks and were 25.2% faster.

However, you should note that AI’s effectiveness is context-dependent. Workers who used AI on tasks outside its optimal application scope were 19% less likely to find correct solutions than their non-AI counterparts.

So, there’s a balance there. As long as you’re using AI for the right tasks in market research and maintaining human expert oversight, you can complete more work efficiently — and with accuracy. In this blog post, we’re going to show you 20 of such tasks where applying AI leads to faster and accurate insights.

Applying AI in market research work

AI as your market research assistant

20 Applications of AI in Market Research to Speed Up Your Work

1. Predictive analytics and modeling

This is where AI plays the oracle, predicting future market trends and consumer behaviors. Here, AI analyzes past and current data to forecast future scenarios, using algorithms that recognize patterns and predict outcomes.

It works by applying machine learning to historical data, identifying trends, and using these to make informed predictions about future market movements. For example, a big online fashion retailer might use AI to predict upcoming fashion trends (after providing the right amount of data input), helping them stock the right products at the right time or begin R&D on products that will capture expected fashion trends.

2. Data collection and analysis from multiple sources

AI can process data from social media, reviews, forums, and more to provide a comprehensive view of consumer preferences. A specialized tool for this will integrate data from diverse sources, using advanced algorithms to analyze and find correlations, offering a holistic market perspective.

A secure, AI-powered qualitative research platform (read: Voxpopme Platform) can facilitate this, in a way that benefits your team, department, or organization. By unifying all of your qualitative data, it helps you conduct integrated analysis across different data sources, subjects, or themes.

This way, your researchers get a more comprehensive understanding of the entire body of qualitative research, including inter-project themes, dependencies, opportunities, and potential risks.

3. Sentiment analysis

AI here acts as an emotional intelligence expert, gauging how people feel about products, brands, or services. It uses natural language processing (NLP) to evaluate customer feedback and social media posts, understanding consumer sentiments. It will decipher the nuances in language, identifying positive, negative, or neutral sentiments and even subtle emotional cues.

4. In-depth interview (IDI) analysis

AI can process audio and video from interviews (and in Voxpopme’s Live Interviews, recordings of IDI sessions and focus groups) to extract valuable insights. It employs speech recognition and transcript text analysis algorithms to identify key themes, sentiments, and patterns in interview data.

It will analyze tone, pace, and speech patterns, providing a deeper understanding of respondents’ attitudes and perceptions. You can use this for virtual focus group sessions/discussions as well and identify key consumer pain points and preferences significantly faster.

5. Behavior analysis

You can get deep insights into consumer preferences and purchasing habits with AI. It can analyze consumer behavior data to reveal underlying patterns and trends using data mining and machine learning to process vast amounts of consumer interaction data, identifying buying patterns and decision-making processes.

Picture using AI to track user browsing and purchase history on an e-commerce platform, offering real-time personalized product recommendations.

6. Trend detection

Your market research AI assistant can also be a trendspotter. It’ll help you identify emerging market trends from data analysis. AI can scan through various data sources to pinpoint new consumer interests and market shifts.

Using data mining techniques, AI analyzes patterns over time, flagging emerging trends often missed by traditional methods and human researchers. For example, a cosmetics brand could use AI to detect a rising interest in organic products, enabling them to focus their market research and product development on this segment.

7. Competitive intelligence

Here, AI is the swift and inquisitive competitor analyst, monitoring and deciphering competitors’ strategies from diverse data sources. It sifts through public data like news, social media, and financial reports to provide insights into competitors’ moves and market positioning.

AI employs algorithms to analyze industry data, offering strategic insights about market share and competitor tactics. You might use AI to track patent filings and product launches by competitors, and stay ahead in innovation.

8. Market segmentation

You can also use AI to segment markets based on complex datasets. It can process your consumer data (which you should do securely) to create detailed segments based on demographics, behavior, and psychographics.

With clustering algorithms, AI can uncover nuanced consumer groups, so you get more targeted marketing opportunities. A streaming service like Prime Video could use AI to segment their viewers by viewing habits and preferences, offering even more tailored content recommendations.

9. Real-time market monitoring

This works by continuously monitoring market and consumer behavior changes — something that’ll be difficult to do manually since you can’t work 24/7.

Artificial intelligence, however, enables real-time tracking of market trends, sentiment shifts, and consumer reactions, providing businesses with up-to-the-minute insights. It uses streaming data and real-time analytics to keep a pulse on the market, adapting quickly to new information.

Imagine you run a Super Bowl ad for a product launch and want to measure sentiment shifts through real-time social media reactions, so you can adjust marketing tactics as early as possible. AI can step in here.

10. Data quality management

AI can enhance the accuracy and reliability of your market research data. It can clean, organize, and validate large datasets, identifying and correcting anomalies. It does this with machine learning algorithms, which helps detect inconsistencies, fraudulent responses, or biases in survey data, ensuring high data integrity.

You can use this ability to automatically filter out low-quality or biased survey responses, improving the overall validity of your study.

11. Automated insight generation

Here, AI steps in as an insightful analyst, automatically sifting through data to highlight key findings. It processes vast datasets to identify significant patterns, trends, and outliers, converting them into digestible insights.

This can speed up your market research work 60 fold. Use AI to automatically analyze survey results, quickly identifying key drivers of customer satisfaction.

12. Discussion guide generation for qualitative research

AI can generate prompts and questions for focus groups or interviews. We’ve built a discussion guide generator that you can try right now for free. By analyzing your research objectives, AI proposes relevant topics and questions to elicit insightful responses.

13. Deep diving into research data

AI uses advanced analytics to reveal patterns and correlations in your research data. This gives you a deeper understanding of market dynamics based on data you already have, but would’ve otherwise missed the nuances without the help of AI’s deep diving capabilities.

14. Customer/buyer journey analysis

AI can map out the buyer’s or customer journey, providing you with a comprehensive view of the customer experience across multiple touchpoints. It will track interactions and transactions, piecing together a holistic customer journey map. For instance, you can use AI to analyze customer purchase paths for your company’s online store and identify key touchpoints that influence buying decisions.

15. Proactive community management

If you have an active community, it can be a pool of rich insights. AI can monitor the discussions in your community, gather feedback, and identify trends within your online forums and social groups. This works great for tech companies or gaming communities who can identify recurring common problems to prioritize fixes.

16. Social media analytics

As a ‘digital strategy assistant’, AI analyzes social media data to gauge public opinion and track brand mentions. It employs algorithms to assess sentiment, identify influencers, and understand factors affecting consumer decisions.

17. Voice, image, and video recognition and analysis

Today’s latest large language models (LLMs) are multimodal. This means they can see and hear — instead of just taking text input, they can listen to voice recordings and recognize visual input like images and videos. And they can give audio and visual output as well.

You can leverage this ability to analyze images and videos for consumer insights at scale. Because AI will always be faster than humans in listening to audio, viewing images, and watching videos. AI tools already exist that can help with this, such as Voxpopme’s AI Insights. We demonstrated this fact in our AI vs Humans webinar.

It can also analyze tone, emotion, and content in voice data from interviews or customer calls. This’ll give you deeper insights from video survey feedback than you thought was possible last year.

18. Chatbots for interactive surveys

AI-powered chatbots have been around for a long time now. What’s changed in recent months is how powerful and contextually-smart they have gotten. Many advanced chatbots sound almost indistinguishable from humans.

And you can use this to engage users in surveys, making data collection more interactive and user-friendly. They can adapt questions based on responses, enhancing survey relevance and engagement.

19. Customized survey design

Yes, AI can help you design and set up your surveys. But it can also tailor your surveys dynamically, adapting questions in real-time based on respondent feedback. It analyzes previous responses and adjusts the survey flow to maximize relevance and engagement.

20. Interactive data exploration tools

AI enables interactive exploration of marketing research data, allowing you to investigate hypotheses in real-time. It can also provide dynamic data visualization tools for exploring various scenarios.

Benefits and Limitations of Applying AI in Market Research

The bright side

It’s like we have a new species of super-intelligent helpers in the market research world. One that isn’t just trying to catch-up with human intelligence but is showcasing its own brand of cognitive functions. And by applying its abilities in their work, 72% of marketers cite AI as a business advantage (Mekhail et al., 2020).

With the help of artificial intelligence’s improved efficiency, your consumers benefit more from your brand in the form of innovative products that actually resonate with their present needs. And they get them faster than with traditional methods.

Let’s not forget: With AI tackling the grunt work, fresh opportunities open up for human creativity to shine through more in market research.

But it’s not all roses and sunshine.

The flip side

First off, complexity and interpretability, or rather, the lack of it. Some AI models, especially deep learning models, are complex. Deciphering how they reach their conclusions can be as mystifying as reading ancient hieroglyphs without a Rosetta Stone.

This “black box” scenario is a problem when you’re trying to explain your insights in clear, transparent terms to busy stakeholders.

Then there’s the data problem. AI models are hungry for quality data, and lots of it. Feed them data that’s skewed, biased, or just scanty, and they will generate inaccurate results. Plus, getting your hands on the right data can be a hassle, with privacy laws and data protection regulations you must respect.

Finally, you need technical expertise to apply AI in market research. Organizations need to either train existing staff or hire experts, which can be a barrier to adoption. And when that isn’t feasible, you’d need to hire AI-savvy external qualitative research service providers.

References

Martinez, Camilla and Mezitis, Tiffany. “Harvard Business School Partners with BCG on AI Productivity Study.” The Harvard Crimson (2023). https://www.thecrimson.com/article/2023/10/13/jagged-edge-ai-bcg/

Mekhail Mustak, Joni O. Salminen, L. Plé and Jochen Wirtz. “Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda.” Journal of Business Research (2020). https://doi.org/10.1016/j.jbusres.2020.10.044.

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