AI in market research: rethinking how companies listen, learn, and launch

The quest for true customer understanding

Every business, no matter the size, wrestles with one key challenge: getting to know its customers. In the modern era, this challenge has been made exponentially more difficult. We’re inundated with more information than ever, customer tastes change in an instant, and attention is scattered across several channels. Many companies are overwhelmed with data but starved for insight. AI in market research is assisting companies in closing the gap between the data they possess and the insight they require.

The magnitude, velocity, and diversity of customer data produced have weakened the effectiveness of conventional, human-driven research approaches. AI in market research can quickly process enormous amounts of data and identify relevant patterns, which solves at its root an inherent weakness of older methodologies. This implies we’re not only observing an improvement but a seismic change in how market research needs to work.

This blog will examine how AI is transforming market research across key industries. We’ll begin by de-mystifying AI in market research. Next, we’ll reveal six specific ways AI is revolutionizing the way companies derive consumer insights. We’ll then explore the key considerations and possible pitfalls involved with leveraging this technology. Finally, looking to the future, we will discuss how to best leverage this power.

1. Listening to the market: AI-powered trend forecasting

Staying ahead of consumer trends is a constant challenge for brands. AI in market research is transforming how companies listen to the market by sifting through mountains of social media chatter, blog posts, and search data to spot emerging patterns.

Unlike traditional research methods that might lag weeks or months behind, AI algorithms can provide real-time trend forecasting. This means companies can catch the early signals of changing consumer preferences and act on them faster.

A great example comes from the denim giant Levi Strauss & Co. In 2020, Levi’s knew looser jeans were gaining traction, especially among younger shoppers on TikTok. But it was their AI-powered data infrastructure, developed in partnership with Google Cloud that revealed the full scope of the trend.

By integrating data from over 50,000 global distribution points, including retail partners, ecommerce behavior, and loyalty programs, Levi’s ran daily machine learning models to track emerging purchase patterns. What they discovered was unexpected: the demand for baggy fits wasn’t just limited to Gen Z. It was growing across age groups and genders.

This insight helped Levi’s act quickly. They ramped up production of looser silhouettes, launched campaigns like “Live Loose,” and optimized retail strategies based on hyperlocalized demand. The result? A 15% quarter-on-quarter spike in loose fit sales, driven by better trend visibility and faster go-to-market execution. This is something that would’ve taken months using traditional forecasting methods.

Read more: AI-powered demand forecasting transforming the future of retail

2. Predictive analytics: anticipating consumer needs

Another powerful manner in which AI in market research is making insights stronger is by predictive analytics for product development. By processing enormous amounts of data, from purchase habits and questionnaires to customer reviews online, AI can anticipate what consumers may want next and even assist in creating the proper product for them.

This goes beyond simply tracking what’s popular now. It’s about anticipating needs and preferences that are on the rise. For businesses, this means less guesswork and more confidence when investing in new product ideas. Companies can lean on AI’s pattern-spotting prowess to guide creative decisions, blending technical insight with a bit of foresight magic.

One such exemplary real-world use case is National Australia Bank’s (NAB) ‘Customer Brain’ — an adaptive machine learning-driven personalization engine with 800 adaptive models and more than 1,000 customer attribute data.

On top of Pega’s customer decision hub platform, it provides more than 150 next-best-actions in service, sales, and engagement. They include in-app payment suggestions, contextual product recommendations, and milestone-triggered messages such as congratulating customers on savings milestones or loan repayment milestones.

What sets it really apart, however, is the manner in which the system learns based on real-time feedback and adjusts messaging or promotions based on live customer activity. NAB’s customer analytics executive Jess Cuthbertson said this created a 40% boost to engagement the moment the engine became live. Through the study of customer journeys, complaints, NPS trends, and frontline banker feedback, NAB translated qualitative and quantitative signals into actionable, predictive intelligence that now fuels the majority of customer interactions.

3. AI-driven retail analytics: real-time in-store insights

The retail industry has been revolutionized by AI in market research, especially when it comes to understanding in-store shopper behavior and optimizing store performance. Traditional retail research might involve periodic audits or manual customer surveys that only capture a snapshot in time.

In contrast, AI in market research can continuously analyze data streams from point-of-sale systems, loyalty programs, and even sensors or cameras on the shop floor to provide real-time insights. This means retailers can observe how consumers interact with products and aisles as if they were watching a live feed of aggregate shopper behavior, all without infringing on individual privacy. The result is a much clearer, data-backed picture of what’s happening in stores, enabling quicker and smarter decisions on merchandising and customer experience.

A real life example is Sephora, which launched an AI-driven digital mirror in its store in Madrid. Created in collaboration with Wildbytes, the mirror takes live data points like a customer’s gender, age, style of clothing, and even environment like weather in the surrounding area. Based on these, it suggests customized makeup, skincare, and fragrance products. This is entirely done through automation, without the customer having to provide any manual inputs. This innovation enriches the in-store experience by making product discovery more relevant, interactive, and attuned to the shopper’s unique profile.

By converting live environmental and behavioral signals into tailored shopping experiences, AI-powered retail analytics enable brands such as Sephora to build smarter, more responsive store environments centered around the customer.

Stores become smarter and more responsive to customer needs, shelves are stocked with what shoppers want, and aisles are organized in ways that make sense for how we actually shop, all thanks to AI’s analytical muscle behind the scenes.

4. Voice of the customer: AI-driven retail sentiment analysis

Knowing what customers say has long been at the heart of market research. But in the modern digital-first world, the amount of unstructured feedback is unprecedented. In retail, where it’s always on and always conversing on social media, forums, and reviews, surveys and human review can only scratch the surface. Companies require a method for processing feedback in real time, at scale.

With natural language processing and machine learning, AI in market research enables the scanning of millions of customer interactions, picking up on sentiment, recognizing patterns, and marking potential issues. Rather than merely tallying keywords, these platforms read emotional tone, urgency, and repeated themes across channels. The outcome is a much greater insight into what customers truly feel.

A Netscribes success story: turning digital chatter into strategy

A major office supplies retailer in Indonesia faced growing online mentions and a widening customer base, but lacked a structured listening approach. Decisions to market were based on guesswork, and response teams tended to act too late.

Netscribes rolled out a real-time social media monitoring system that integrated AI-powered sentiment analysis and intelligent data segmentation. Brand mentions were automatically grouped by theme, sentiment, and business impact. Daily performance reports and on-demand insights provided the team with a holistic representation of what customers loved, what annoyed them, and where opportunities lay.

The outcomes were instantaneous. The retailer could detect negative sentiment early, react quicker, and adapt communications before problems escalated. Campaign performance was enhanced, engagement was boosted, and customer satisfaction improved.

This strategy illustrates how AI in market research can enable businesses to shift from reactive communication to proactive customer interaction. By listening at scale, brands can act with purpose and establish deeper connections with the people they serve.

You can read the full case study here.

5. Scaling qualitative analysis: NLP for open-ended feedback

Market research isn’t only about numbers and structured data; qualitative feedback like open-ended survey responses, reviews, and comments are equally valuable for understanding the “why” behind consumer behavior. However, analyzing thousands of written responses or transcripts in multiple languages is a herculean task for human researchers.

This is a classic scenario where AI in market research shines by using Natural Language Processing (NLP) to automate and scale qualitative analysis. AI can digest free-form text, detect themes, gauge sentiment, and even pick up on cultural nuances in ways that would be extremely time-consuming manually.

Turning open text into actionable insights

For companies, it means they don’t have to ignore or oversimplify the wealth of insights buried in open text. AI in market research helps turn that unstructured feedback into clear, structured understanding. The tone here remains conversational and insightful: think of AI as an tireless analyst who reads every comment and neatly summarizes what people are saying and feeling, so human experts can focus on interpretation and action.

In the telecom industry for instance, it could quickly flag if many customers in one region were mentioning network coverage issues, or if elsewhere customers kept praising a particular pricing plan. The result was a much faster turnaround for insights, what used to take weeks of human effort came back in near-real-time. This can also enable more consistent analysis since the AI applied the same criteria to all comments​.

Importantly, this approach also reduced human bias in interpretation. Since the AI was systematically coding the responses, there was less risk of an analyst subconsciously ignoring an outlier comment or seeing a pattern that wasn’t really there. Human researchers can then take these AI-curated insights and dig deeper where needed, perhaps conducting follow-up interviews on a specific issue the AI highlighted.

This harmonious collaboration means that the client gets the best of both worlds. Scale and speed from AI, and strategic context and recommendations from human experts. It’s a powerful example of how AI in market research can enhance consumer insights by making sense of qualitative feedback at scale. No honest opinion from a customer gets lost in a dusty spreadsheet. Instead, every voice is heard and can influence decision-making.

6. Virtual consumer simulation: AI for healthcare strategy

Perhaps the most exciting development of AI in market research is virtual consumer simulation. This is where algorithms not only examine previous behavior, but actually model and forecast how individuals may engage with goods, services, or experiences. This enables businesses to try out ideas, iterate on design, and predict user reaction before launching into the world at large.

Decoding patient behavior is one of the most difficult problems in healthcare market research. Conventional approaches falter at reflecting the nuance of real-world treatment responses. Step in AI-powered virtual patient simulation, a technology that’s changing the way healthcare organizations derive insights, prior to products entering clinical or commercial phases.

An example from the real world is Unlearn.AI, a firm that creates digital twins. These are synthetic patients created by AI, employed to build control arms for clinical trials. Although the ultimate aim is to enhance the speed, efficiency, and ethics of medical research, this technology has a strong secondary advantage: it allows early, data-rich insight into how various categories of patients might react to new treatments.

Through the simulation of thousands of patient pathways, researchers are able to identify trends in disease development, treatment response, and heterogeneity by demographic. These insights enable life sciences companies to segment target populations more effectively, optimize product positioning, and make better choices in early development, well before they gather conventional real-world data.

In this way, Unlearn.AI’s approach bridges the gap between clinical R&D and AI in market research, showing how AI can uncover deep, predictive insights about healthcare consumers, all while reducing time, cost, and trial burden.

Considerations when using AI in market research

As exciting as these examples are, implementing AI in market research isn’t without its challenges. Businesses need to be mindful of several key considerations to ensure that AI-driven insights are trustworthy, ethical, and truly useful.

Data quality and bias

AI is only as good as the data that feeds it. If the input data is biased or unrepresentative of the true customer base, the insights will be skewed. This is a classic “garbage in, garbage out” scenario. For instance, if an AI tool for sentiment analysis is trained mostly on young social media users’ language, it might misinterpret or miss sentiments expressed by older customers in formal feedback.

Ensuring data quality means gathering data from diverse, relevant sources and continuously checking for bias. Researchers often need to clean and preprocess data, removing noise and correcting errors, before letting the AI loose on it. To overcome bias in AI in market research models, this might involve weighting data, introducing additional training examples for under-represented groups, or regularly auditing the AI’s outputs for signs of systemic bias.

Ethics and privacy

With great data power comes great responsibility. AI in market research often involves collecting and analyzing personal data, whether it’s customers’ purchase histories, social media comments, or voice calls. Companies must navigate the ethical and legal aspects of using this data. Privacy regulations like GDPR (in Europe) or CCPA (in California) set boundaries on what data can be collected and how it can be used.

Ethical considerations also include transparency. Consumers might not even realize their public tweet is being analyzed as part of some company’s market research, so companies have to tread carefully and respect privacy norms. AI in market research can inadvertently cross lines if not properly guided. For example, an AI scraping social media for consumer opinions needs to avoid collecting personal identifiers or sensitive information that isn’t relevant to the research question.

Human oversight is crucial here to ensure that the AI’s hunger for data doesn’t lead to a privacy breach or unethical profiling. As experts point out, AI in market research lacks a moral compass. It will relentlessly chase patterns in data without regard for privacy or fairness, which is why humans must make the ethical judgments.

In market research, that means putting strict data governance rules in place, anonymizing data whenever possible, and being upfront about data use. It’s also wise to have an ethics review whenever launching a new AI-driven research initiative: Are we respecting consumer consent? Could this analysis unintentionally discriminate or harm any group? Such questions need clear answers before trusting the AI’s output.

Human oversight and expertise

No matter how advanced AI in market research becomes, the human element remains irreplaceable in many ways. AI can crunch numbers and text at superhuman speed, but it doesn’t truly understand context, culture, or emotion like a human would.

We saw that in several examples, the final mile involved human interpretation, whether it was adjusting an insight to align with brand strategy or probing deeper into a trend that AI identified. Human oversight is not about distrusting AI; it’s about combining strengths. Humans set the objectives for AI in market research, ensure the questions asked are the right ones, and interpret the “why” behind AI’s findings.

They also catch the things AI might miss, those subtle nuances or a shift in consumer mood that isn’t evident in the data yet. Moreover, humans are needed to validate AI findings against reality. If an AI model predicts a certain product concept will succeed, an experienced researcher or manager will still apply a reality check: Does this make sense given what we know of our customers?

Towards an augmented strategy

Often, the best practice is an augmented approach. AI does the heavy lifting and presents initial findings, and human experts refine and decide how to act on those findings. This partnership prevents over-reliance on automation. We must remember, AI doesn’t get intuition or creative inspiration; those spark moments of insight often come from people. As such, companies should treat AI as a powerful tool for market researchers, not a replacement.

Keeping humans in the loop also ensures accountability. Someone needs to be responsible for decisions, especially since AI can sometimes err or produce misleading correlations. To put it simply, AI in market research works best when it’s the engine, and humans are at the steering wheel, making sure the journey stays on the right path.

AI in market research offers incredible capabilities to enhance consumer insights, but its success depends on how thoughtfully organizations implement it. Ensuring high-quality, unbiased data, upholding ethical standards with respect to consumer privacy, and maintaining strong human oversight are all vital. With these considerations in mind, companies can confidently use AI to augment their market research, knowing the insights they get are both accurate and responsibly obtained.

Conclusion: Bridging the gap with AI

Throughout this exploration, we’ve seen how AI in market research is truly bridging the gap between data and decision-making by enhancing consumer insights across industries. From a retailer reimagining its store layout based on AI-unearthed shopping patterns, to a bank listening better to its customers, the impact of AI in market research is broad and significant. It allows businesses to understand not just what consumers are doing or saying, but often why, and to do so faster and more accurately than traditional methods.

In summary, AI doesn’t replace the fundamentals of good market research, it amplifies them. It provides scale, speed, and analytical depth, while freeing human experts to focus on strategy, creativity, and ethical considerations. When implemented thoughtfully, AI in market research enhances consumer insights by revealing patterns and preferences that would otherwise remain hidden, and by doing so in real time, it helps companies respond to consumer needs more effectively.

However, success comes from a balanced approach: combining AI’s power with human insight and maintaining a strong focus on data integrity and ethics. Netscribes’ AI-powered solutions for market research can help organizations tap into advanced analytics and consumer insights without having to build everything in-house. Our growth consulting solutions combine the best of technology and human expertise, ensuring that the insights you gain are not only data-rich but also actionable and aligned with your business context.

AI in market research is here to stay, and as we’ve explored, it offers a powerful way to enhance consumer insights. With the right balance of technology, ethics, and human touch, companies can fully unlock the potential of AI to understand and serve their customers better than ever.