AI-Driven Market Research: AI's 10x Impact & How to Overcome Challenges

Is there an AI for market research? Most business leaders and market researchers know the inefficiencies in traditional market research methodologies. Incorrect sampling, inaccurate data, and outdated insights are common problems practitioners face in every project.

Inaccurate data alone cost US businesses a whopping $3 trillion per year.

AI can change that—forever.

AI market research can change how researchers work and help them deliver 10x results. In this article, we’ll explore how leading companies already leverage AImarket research and one common challenge they face.

Market Research Then and Now

Formalized market research has a long history dating back to the 1920s.

It came from an advertising man named “Daniel Starch” who postulated that “for an advertisement to be considered effective, it has to be “seen, read, believed, remembered, and acted upon.”

So, to measure the ad's effectiveness, Starch and his team would go door to door, approach people on the street, and ask questions. This paved the way for modern market research.

Fast forward to today, methodologies like surveys, focus groups, one-on-one sessions, reviews, and feedback dominate the scene.

While the methods are still effective, some inherent flaws are:

• Time-Consuming: Conventional approaches, including survey design, interviews, and data analysis, are often time-consuming.
• Limited Scope: These methods may miss real-time trends and hidden online customer insights.
• Potential for Bias: Human bias can creep into questionnaire design, focus group moderation, and data interpretation.

So, what does AI bring to the table? Among other things, speed and agility.

Custom market research takes anywhere from 6-12 weeks. Certain parts of the research are necessary and unavoidable. One-to-one discussions, for example, are needed for qualitative analysis. But what you do after the discussions (or the speed with which you do it) matters. AI market research can speed up post-processing activities to give you faster insights.

A subset of AI, NLP or Natural Language Processing, is designed to understand human language and draw valuable insights—almost instantly, depending on the size. Manual processing often takes days, if not weeks. This speed was unheard of before the advent of powerful NLP models, demonstrating the clear advantage of AI market research.

One such recent NLP model that you must have heard of is ChatGPT.

The magical part is that researchers use ChatGPT to get responses without surveys or focus groups!

Two Microsoft researchers and one at Harvard Business School teamed up for a working paper on AI market research: "Using ChatGPT for Market Research." The paper aimed to utilize ChatGPT and get responses to a set of survey questions from the model. In the paper, they showed two things:

• ChatGPT-generated AI market research was consistent with economic theories and aligned with consumer behavior patterns.
• The willingness to pay responses by ChatGPT are of realistic magnitudes and match estimates from a recent study that elicited preferences from human consumers.

The study also laid out some guidelines on how to best query ChatGPT for AI market research. In other words, it's groundbreaking and only the tip of the iceberg of what lies ahead.

And ChatGPT’s CEO, Sam Altman, even claimed that their models will be better than humans in the next 10 years. He said, “Given the picture as we see it now, it’s conceivable that within the next ten years, AI systems will exceed expert skill level in most domains, and carry out as much productive activity as one of today’s largest corporations.”

Not only that. Tools like ChatGPT also help with the creation of AI market research materials.

ChatGPT, a generative AI tool, can be a brainstorming partner. The initial analysis of market research data suggests potential research questions and hypotheses. This can be particularly helpful for identifying new areas of inquiry or refining existing research objectives.

Amazon’s Data-Driven Approach to Understand Customer Behavior

Deep AI market research leads to a gold mine about customers. And no one understands it better than Amazon.

The e-commerce giant doesn’t sell items directly (except for branded goods) but facilitates the transaction through third-party sellers. But, it has AI market research tools to aid those sellers.

Amazon unveiled a market research tool called “Customer Sentiment Insights.” It provides category-level customer feedback that helps sellers develop products.

In the post, Ben Hartman, VP of Marketplace, NA, commented, “New product development can pose a daunting challenge for brand owners. To rise above these obstacles, specific customer insights become one vital key, unlocking critical information about the target audience.”

The tool has a “Customer Loyalty Dashboard” that allows sellers to segment users and provide loyalty benefits to some that meet the criteria.

Notice there was no focus group, one-on-one conversation, or minimal surveys? That’s the possibility of AI market research, which could benefit even small businesses (Amazon has more than 2 million SMBs).

The company is also pioneering other innovative methodologies, including Generative AI. Amazon has rolled out a Gen-AI feature to quickly summarize a product based on the description, reviews, and FAQs as part of its review innovations. This helps buyers quickly judge a product.

Now you know how AI market research helps Amazon become the most customer-centric company.

Amazon is not alone. Companies like Salesforce are equally adept at utilizing AI for market research.

Salesforce leverages its Customer Data Platform (CDP) to unify customer data from various sources, such as marketing campaigns, sales interactions, and support tickets. This provides a holistic view of customer behavior and preferences that informs AI market research.

The Data Cloud has 1.2 billion customer records from over 60 data streams. This includes transactions, service requests, and communication records. With this data in hand, it can segment and target each customer at a granular level. It can even predict when a random user is likely to turn into a customer by using predictive analysis. On this, Salesforce’s former chief scientist, Richard Socher, said:

“Companies don’t want to spend time calling or emailing folks who don’t want to buy their products.”

Again, notice how Salesforce is light on focus groups and surveys and prioritizes AI market research to gain insight into its customers.

Implementing AI Market Research in Your Business

By now, you must have been convinced that AI market research is more than a buzzword and has untapped market research potential.

So, how do you get started with implementing AI market research in your business? While there's no one-size-fits-all approach, there's a framework you can follow:

• Adopt digital
• Ensure data quality
• Utilize data analytics tools

Adopting Digital

What allows Amazon to gain consumer insight at such a rapid pace? Salesforce, Meta, or any other company? All of them are digital-first businesses that are rapidly becoming AI-first companies.

So, the first step is to digitize your business to generate data for market research. In 2023, 59% of marketers claim they need more data to feel confident about their marketing campaigns.

As this HBR article highlights, overarching projects aiming for the moon often need to be revised. It’s the simple AI projects that produce the expected results.

According to IBM, the most valuable AI use case currently is AI-assisted customer support via chatbots. Technologies like NLP, sentiment analysis, and speech recognition produce proven results, so you can invest in chatbots.

There are two benefits to it. First, the chatbots can partially or fully automate your customer service. Second, they can help you gather high-quality structured data.

Ensure Data Quality

Janani Narayana, Senior Director of Product Management at Salesforce, says

“AI is the most important technology of our lifetime. No Question. However, AI is only as good as the data that fuels it.”

Nokia's fall from grace in the telecom industry is partly attributed to misleading data that overestimated its brand strength and superior hardware design. The bottom line is poor data leads researchers and executives to make bad and even deadly decisions.

Depending on your data collection process, you may have to spend some time cleaning the data by removing inconsistencies, formatting issues, and irrelevant information (another area where AI can help).

With the right data, you can take your first step toward AI market research. You’ll be able to learn more about users, segment them, and provide personalized services.

Utilize Data Analysis Tools

Next, you’d have to choose the right tools. Various tools, such as Amazon Comprehend and Google Cloud Natural Language, exist for NLP. Choosing between the two is not easy, as multiple factors are involved.

Likewise, you must choose between IBM Watson, Microsoft Azure ML, AWS Machine Learning, and other specialized tools for machine learning. Choosing the right AI market research tool depends on your research goals and the data type you’re working with.

You can speed up AI market research like Amazon and Salesforce with quality data and the right tools.

Privacy Concerns in AI Market Research

AI is a holy grail for businesses looking to improve their market research efforts. However, it also brings some ethical challenges.

AI market reserach thrives on data, and market research applications often collect vast customer data from online interactions, social media activity, and even loyalty programs. This raises concerns about who owns the data, how it’s secured, and for what purposes it might be used beyond market research.

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Pew Research revealed that 81% of consumers are uncomfortable with the amount of data AI companies collect.

Shoshana Zuboff, author of The Age of Surveillance Capitalism, notes in her book, “As AI becomes more sophisticated, the potential for misuse of personal data grows exponentially. We need clear regulations and ethical guidelines to ensure that AI is used for good, not for manipulating and exploiting people.”

End-to-end encryption is at the core of ensuring digital privacy. Almost all messaging platforms, social media platforms, and tech companies like Apple use this when facilitating digital experiences.

Meta explains end-to-end encryption for its messaging tool, Messenger: " End-to-end encryption helps protect your conversations by ensuring no one sees your messages except you and the person you’re chatting with."

Google goes a step further and implements "Homomorphic Encryption," which enables its developers to work with data while still in encrypted form. This ensures data is never leaked and stays encrypted, minimizing privacy risks.

So, at the bare minimum, you must implement end-to-end encryption when collecting, sharing, and distributing data for marketing research.

Conclusion

AI isn’t just a buzzword; it’s a game-changer in market research, offering a way to overcome the limitations of traditional methods. By embracing AI tools like natural language processing and machine learning, businesses can dive deep into customer insights and achieve results that rival industry leaders like Amazon and Salesforce.

The future of AI in market intelligence is exciting, with new technologies set to enhance insights even further. But as we move forward, it’s important to remember the ethical considerations and privacy concerns surrounding AI.

For executives, the message is clear: AI isn’t just for tech companies. It’s a strategic tool that can give your business a competitive edge. By integrating AI into your market research strategy, you can better understand your customers and stay ahead of the competition.

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