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How to Use AI For Market Research (and FAQs)

Discover practical AI applications for market research, plus answers to common questions.

10 mins read

AI for market research promises to revolutionize the UX process. Let’s explore how.

Did you know… 

Two tech giants once forayed into the smartphone market?

Amazon created the ‘Fire Phone’, a device that famously didn’t cater to many user needs. Facebook teamed up with HTC to create ‘First’. The device used Facebook’s interface as the home screen. Yikes. 

After poor sales and massive sunk costs, both companies pulled the plug.

They didn’t adequately test products with users and gather feedback before launch. 

The story might have been different had they conducted thorough market research.

Winning products are the result of meticulous market research. And AI is here to help. 

TL;DR – How to Use AI For Market Research

In this article, we examine how to integrate AI into the market research process. 

First, we’ll introduce market research and highlight its importance. We’ll dive into how AI can supercharge your research efforts. 

Finally, we’ll outline some benefits and limitations of using AI in market research.

See how Marvin brings all your research data into one place, enabling deeper analysis and company-wide collaboration along the way.

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What is Market Research?

Market research is a tool for collecting and analyzing data to better understand customers and the competitive landscape. Companies use this information to support strategic decision-making.

Researchers focus on gathering information about a company’s:

  • Target audience. Unearth customer insights and preferences. Segment customers based on psychographic and demographic information.
  • Competitors. Keep abreast with developments and stay ahead of the competition.
  • Product demand. Ask the important question, “Does the product appeal to customers?”
  • Broader market trends. Understand the environment, manage risk, and make informed decisions.

Market research is a combination of primary and secondary data. Companies that conduct their own studies collect primary research data. Secondary research is published data from external sources.

Data collected is either quantitative or qualitative research. As its name suggests, quantitative data is numerical and applies to a large population or sample.

The smartphone industry sold 1.14 billion units in 2023, for example. 

Quantitative data represents ‘what happened?’ It’s used to lay the foundations for testing hypotheses. 

What is Qualitative Market Research?

Qualitative market research reveals the ‘why’ behind the numbers. The underlying reasons behind consumer behavior and market trends. 

Non-numerical in nature, qualitative data comes in many different formats — images, text, audio and video. Researchers use several open-ended questions to try and understand more about a user’s thought process. Data collection involves conducting surveys, interviews, and focus groups. 

Qualitative research provides deep, rich insights into customer behavior and market dynamics.

If Amazon and Facebook had collected qualitative feedback, their smartphone fortunes might’ve done a complete 180. Customer feedback would have revealed why their concepts were far off the mark. 

Instead, it was back to the drawing board.

Learn why Qualitative research matters.

User Research Software Marvin is a Game-Changer

Why is Market Research Important?

Traditionally, business decisions used to rely on gut feelings, hunches, or plain guesswork. 

The data revolution changed that.

Since everyone loves to drop the phrase ‘data-driven decision making,’ we will too.

Market research has created an era of…you know. It helps:

  • Inform Decision-Making. Market research provides direction. Business leaders rely on a consistent stream of data to choose the path ahead. Apple discontinued the ‘mini’ iPhones once they learned that only a sliver of the market were potential buyers.
  • Identify Opportunities. Look for new niches and trends in the market to see where potential opportunities lie. To mitigate risk, use research to validate the demand for a new product before launch. 
  • Maintain Customer Obsession. Surveys, focus groups, and user testing help researchers understand the customer experience. What do customers want and expect from a product? Constant feedback aids further development and improvements.
  • Enhance Brand Position. Stay informed about market trends and your competitors. Position your brand in the market by identifying your target customers and standing out from the pack. Gain a competitive edge. 

When it comes to market research, take a page out of Nike’s book:

JUST DO IT.

How Long Does Market Research Take? 

We’re going to use a UX researcher’s favorite response here — it depends. 

Marketing studies come in all shapes and sizes. Small, quick surveys can take less than a weekend to roll out and analyze. Large-scale, ongoing research has no end. 

It depends on a few factors. These include the objective, scale, complexity, methodology, and analysis required for the study. Below, we outline the sequence of events for a generic study:

  1. Design the study
  2. Recruit participants
  3. Collect data
  4. Analyze data
  5. Publish results

Such studies typically take 3-5 weeks to complete. Bear in mind that estimates can vary widely. 

When gathering primary data, the process is more time-consuming. Assimilating secondary data takes far less time. The caveat? Secondary data doesn’t address the research question exactly.

Marketers must carefully align their objectives with methodology. All the while keeping time and resource constraints in mind.

Can AI Help Expedite The Process?

You bet it can!

AI can glean data from various platforms, social media feeds, and publications. All in a fraction of the time that it’d take a human. 

Moreover, it’s capable of analyzing copious amounts of data. Use it to scour datasets, as it spots trends and patterns that humans might ordinarily miss.

With no human error to boot, AI brings efficiency and scale to the market research process.

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Role of AI in Improving Predictive Analysis

Future planning is critical to a business’s success. No one can predict the future with 100% certainty. 

Enter AI.

Businesses use predictive analytics to identify risks and opportunities in their evolving marketplace. Models are used to predict future sales, market demand, and customer preferences. 

Predictive analysis powered by AI leverages Machine Learning (ML) techniques. Machines perform advanced statistical analysis (regressions, time series forecasts & clusters) on past data. ML algorithms are adept at identifying complex patterns and trends.

Deep learning models identify correlations between variables. They use large historical datasets to forecast future outcomes, trends, and behaviors.

Businesses use predictive analytics in AI market research to:

  • Understand customer needs. Deeply analyze market trends and user data.  Companies can develop products tailored to emerging user preferences. Marketing and product strategies become more well-informed.
  • Stay competitive. AI levels the playing field. It opens the door for smaller businesses to ramp up their analytics and predictive modeling capabilities. Beware: Larger companies can leverage AI to retain their competitive advantage.

Remember, we’re at an early stage in AI development. It’ll improve in leaps and bounds. 

Advancements in AI and deep learning technologies will enhance AI’s predictive capabilities. In the future, companies will benefit from accurate predictions and ‘data-driven decision-making.’ 

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How to Use AI For Market Research

Let AI play a role in your market research process. Introduce AI to each of these stages:

Data Collection & Processing

ML helps streamline the data collection process. AI-powered systems automate data collection from a variety of sources in real time. These include social media platforms, online surveys, industry reports and customer reviews. 

Researchers can use this data to gain a comprehensive understanding of:

  • User Personas
  • Competitive Intelligence
  • Market Trends

Companies remain informed and can react to shifting trends and evolving user preferences.

The right qualitative data analysis tool (like HeyMarvin!) will help you change your data collection processes and literally change your work for the better.

Data Analytics

AI can sift through large swathes of structured and unstructured data. It can extract insights from large datasets in real-time. Moreover, it’s less prone to bias and error for more consistency. Use it for the following analyses:

  1. Sentiment Analysis
  2. Predictive Analytics
  3. Social Media Monitoring
  4. Chatbots
  5. Customer Journey Analysis
  6. Demand Forecasting
  7. Consumer Segmentation
  8. Image & Video Analysis

Lots to unpack here.

Before implementing AI, researchers must set clear objectives for their study. How can AI help the process? Select the right research methodology (just ask AI!). It’s essential to provide high-quality input data. Remember, your output is only as good as the input. 

Regularly update research to see how trends have changed over time.

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3 Benefits of Market Research

Below, we’ve outlined a few benefits of AI market research (in no particular order):

1. Marketing

Marketers use research to develop a strong understanding of the customer. They seek to understand user needs, pain points, and motivations. When using AI in market research, they benefit from:

  • Customer Segmentation. AI algorithms can identify unique user traits and create customer groups. Create varied marketing tactics for specific groups. 
  • Personalization. Align marketing with an individual’s preferences. Tailor content and recommendations to different types of users. 
  • Real-time Insights. Observe how consumer preferences and market trends are changing. Adapt and react.

2. Strategic

Business leaders use market research to understand the competitive landscape. Research findings influence the way forward:

  • Informed decision-making. AI’s ability to handle vast amounts of data means teams benefit from a host of information. Product and market teams can make data-driven decisions (we warned you!)
  • Predictive analytics. With the ability to look forward, predictive analytics helps businesses plan for the future. They can mitigate risks and anticipate future market trends and user preferences. 
  • Competitive intelligence. Understand what the competition offers and differentiate your product. Stay ahead of the curve. 

3. Operational

AI brings operational efficiencies to a business. We talk about efficiency in terms of the big three resources — time, money, and manpower. However, several other benefits include: 

  • Resource Efficiency. Automating mundane tasks using AI saves time, money, and effort. With AI, you need less manpower (and therefore money) to process more data. This frees up a researcher’s time to focus on more strategic pursuits. 
  • Future Forecasting. Demand forecasting enhances supply chain management. By anticipating market needs, companies can optimize inventory levels and reduce costs further. 
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Best AI for Market Research

Let’s walk through some AI technologies that make up a great market research tool. 

Take Marvin, for example. Here’s a list of our AI features and functionality that aid the market research process:

  1. Natural Language Processing (NLP). Allows researchers to evaluate large amounts of unstructured data. Distill hours of research into insights in seconds. Use Ask AI to interrogate your data and ask pointed questions. 
  2. Machine Learning (ML). ML algorithms identify trends and patterns in data with high accuracy. Upload surveys and let AI sort the responses into key insights in no time. 
  3. Deep Learning. A continuation of the point above, but Marvin is clever! The tool learns from the edits you make. So it doesn’t make the same mistakes again. 

AI assistant features make Marvin a standout tool for market research. Find out how Marvin can transform your research process. Set up a free demo today. 

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Frequently Asked Questions (FAQs)

We address some frequently asked questions about AI market research below:

What Are The Limitations Of Market Research?

The limitations of market research include, but aren’t limited to (wordplay):

  • Cost. Conducting market research studies is expensive. Studies both large and small require significant investment. We’re talking about the big three resources — time, money and manpower. This can make research prohibitive for smaller companies.
  • Bias or Errors. Can arise at any stage of the research process. From question design to sampling, the assumptions made through to interpreting results. We’re all human, after all (don’t forget – humans program the machines, too).
  • Accuracy. How is this a pro and a cons? Qualitative research is a humanitarian craft. Humans are complex. Understanding consumer psyche and market sentimentality is not an exact science. Research will never be 100% accurate or have ‘statistical significance’ as quantitative studies do.
  • Time consuming. Some market research studies involve lengthy initial setups and data collection. By the time research results are published, the market landscape might’ve already changed.
  • Privacy. Collecting personal user information introduces data security and privacy concerns. How do companies handle sensitive user data? What compliance must be adhered to? (Marvin is GDPR, HIPAA & SOC2 compliant – leave data security to us!)

How Can AI Contribute to Real-Time Market Trend Analysis?

Traditional analytics displays static data. Visualizations are usually pre-defined based on typical business queries. Slicing data to answer a new question or topic requires the skill and time of a data scientist.

AI allows any user to query live data dynamically.

Using AI, companies can stay up-to-date with emerging market trends. AI continuously monitors data sources such as social media, chatbots, and conversation transcripts. Mining large volumes of data helps answer the questions:

What do customers want? Do they like the product? Is the company seen in a favorable light? What would customers change about it? What do people feel strongly about?

Real-time market analysis makes companies agile. Up-to-date information helps them respond quickly to developments and changes in the environment.

AI keeps a business’ finger on the pulse of the customer in real-time.

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How Does AI Help in Quantifying Consumer Sentiment?

Social media can be daunting. People can be rude and outspoken — most of it is utter nonsense, of course. 

To businesses, though, social media is a goldmine of user insights. 

One man’s food is another man’s poison.

Problem is, social media, call transcripts, and customer feedback contain largely unstructured data. They’re made up of free-form text. Researchers have to pore over words in their thousands to understand the prevailing sentiment.

Not anymore. 

Sentiment analysis simplifies this process by allowing researchers to quantify qualitative data. NLP can scan text files and assign a number to each word or phrase (-1 for negative, 0 for neutral, +1 for positive). 

Let AI run a sentiment analysis on large bodies of text to gauge overall user sentiment. 

In the blink of an eye, you have a definitive handle on the prevailing emotion. Program the AI model to recognize specialist terms and results will get better over time.

WARNING: Don’t accept AI’s output as final. Run through its analysis to check for any errors. Make corrections and edits accordingly.

Can AI Improve the Accuracy of Market Forecasting?

Yes, it sure can!

Using AI in market research gives researchers an accurate basis to begin analysis. AI tools can identify patterns and trends and forecast the future with a high degree of accuracy.

When conducted by humans, forecasting can be biased and limited in its view. AI evaluates ALL the data and provides objective output after extensive testing. 

Now, AI models won’t be great from the get-go. ML algorithms are trained on an initially biased dataset. They’re capable of producing questionable output at times. A trained eye must correct this. With the right amount of oversight, you can help train models to display output of interest to you. 

The more (high-quality) data you feed AI, the better it gets.

Machines autonomously learn from historical data with no human intervention. This enhances the precision of AI’s predictive abilities over time.

Conclusion

AI is changing the way companies conduct market research. 

Internally, AI makes processes more efficient (think the big three). Machine processing power far outstrips human beings. Researchers can now analyze data at a scale previously unheard of. 

ML and data analytics enable researchers to anticipate customer needs with more precision. AI enables data-driven decision-making (yes, we did it again). 

Companies can look for new opportunities to gain a leg up on their competitors. They create more personalized and tailored marketing to capture user groups’ attention. Looking forward, they can identify and mitigate business risks. 
Using AI in market research brings innovation to the wider marketplace. Discover how to leverage the ever-expanding universe of market research tools to supercharge your market research efforts.

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