AI in Market Research: What it’s good for, and what it’s not
AI has changed Market Research from rigid to iterative, let's discuss the good and the bad.
Introduction
More complex than clear
Take a moment to reflect on the world surrounding your business today.
Is everything straightforward—just make, sell, and supply your products or services as quickly as possible? Or does it feel foggy, filled with uncertainties, and loaded with more questions than answers? Understanding your customers’ future needs can be challenging. Unless you run a unique monopoly, it’s probably the latter.
The days of solving business issues solely with best practices, copied templates, and off-the-shelf market studies are over.
Today, you need in-depth expertise, the ability to experiment, and the agility to act fast.
These new rules also apply to market research. In the past decade or two, market research has undergone significant changes.
Business moves faster, and decision-making is more complex. Market research must meet higher demands. Market research now operates on tighter schedules and faces greater pressure to deliver ROI. Technology, especially AI, is crucial for keeping up with business expectations. Market research must be agile and iterative, adjusting questions and methods as preliminary findings emerge.
Advanced, modern market intelligence teams consider AI as one key team member, which helps research at every stage – but let’s dive into what it’s good at right now – and what’s not quite there yet.
AI in market research: What it’s good for
Generating objectives, whittling down wish-lists
AI is fantastic at generating objectives and whittling down wish-lists.
When defining research objectives, businesses often face extensive wish-lists of desired outcomes. Scope creep becomes a real issue, making it difficult to prioritize multiple markets and opportunities. AI tools–like Valona’s Research App–pull from databases for insights you can use to determine which countries should be in scope for your market research.
For example, in chat-based tools with trusted source-sets, you can ask questions like:
“Which country has been more active in solar power investments over the past 12 months, France or Germany?”
Most AI market research tools can analyze the data and provide a clear answer, for instance: “Solar power investments have been more active in France than in Germany.” After whittling down your choices, your team can decide: “Let’s focus on France, where the solar power ecosystem is more vibrant and offers more opportunities.”
Of course, accuracy is highly dependent on what kind of sources your software uses. It’s important to use software that cites its sources, so you can double-check statements and where those are based.
Brainstorming research questions
Crafting precise research questions can be daunting.
Often, market researchers face a long list of questions from stakeholders that may only loosely relate to the original objective. The subject matter can quickly become overly complex. For example, a company might want to explore its key customers’ business needs. This objective can lead to a wide range of questions, making it hard to stay focused. To maintain clarity and scope, a generative AI tool can help researchers pinpoint the core issues.
AI is great at highlighting trends. For instance, you might interact with AI to review several reputable industry sources globally. Taking into account headlines, news releases, and public statements, your market research software can identify emerging trends in a second.
Chat with your AI research tool: based on huge amounts of industry sources it can browse during your conversation, what are the topics you should investigate? What is coming up the most? Is, for example, near-shoring clearly increasing among your top clients? Then investigate that topic further to gain an understanding of your clients’ needs!
Rethinking traditional methods
Instead of sticking to an “we’ve always done it this way” approach, AI-powered market intelligence encourages rethinking research methods. Traditional approaches, such as resource-intensive stakeholder interviews, might not be always necessary.
For example, when the objective is to understand the possibilities of the replacements for the raw materials used in your production, you could re-consider your research approach: do you really need to conduct interviews among industry experts?
Instead, you could turn to the tools you have available and collect internal insights among the company’s 10,000+ employees and triangulate the data with AI findings.
AI can prompt you to adopt more efficient and innovative research methods so you gather the most relevant data quickly and effectively. This shift can save both time and resources while still providing comprehensive insights.
Collecting and analyzing data faster
Collecting the data and analyzing it is a market research area that is enjoying the biggest efficiency benefits brought by AI.
AI can process inhumanly large amounts of data in seconds. AI sifts through vast amounts of information at depths otherwise impossible, uncovering findings likely missed by human researchers.
By crunching piles of data fast for your research purposes, AI is really making the impossible possible.
Take, for example, a research task of creating a comparable analysis of 25 client industry sectors. This project includes market size growth rate estimates, key drivers and limitations, and top-investment fields.
In the (slow) old days, you would have rolled up your sleeves, and ended up spending hours and hours analyzing the data. Now, with AI tools, you can boast to your colleagues data processing within seconds—and how you enhanced your industry analysis with your own insights.
This rapid processing capability allows you to stay ahead of trends and make data-driven decisions quickly. The ability to analyze large datasets in a short time frame ensures that your market research is both comprehensive and timely.
Or, you might be facing another very typical intelligence need to identify M&A targets for your company.
Without AI, target-listing can be a hugely time-consuming, manual data collection and analysis, where a researcher combs the internet, industry databases, etc. one by one. With sophisticated AI tools, you can validate in seconds whether a company fits the required criteria or not.
So, go ahead: hand your AI tool a list of 8,000 company names. Ask it to check which ones have ISO 14001. In seconds, you’ll have a target company longlist that you can work on.
This speed and accuracy in identifying potential targets mean you can focus your efforts on the most promising opportunities. AI streamlines the initial stages of target identification, allowing your team to spend more time on in-depth analysis and strategic planning.
AI in market research: What it’s not-so-good for
Human interpretation
Humans should firmly lead the interpretation of market research, even if AI leads data collection and analysis.
The classical “so what, what does this mean to us?” is a thought process where companies should use their own brainpower. AI can be used in interpretation as a sparring partner, but not the end-all-be-all; it’s best to use it to test and challenge assumptions.
Valona has a good tool for this: Val.
One thing we’ve seen our customers doing, for example, is chatting with Val, asking things like, “Val, what points can you raise up according to the recent data? What does this trend mean for our industry?”
Sure, Val can provide alternative perspectives and highlight potential implications that may not be immediately obvious, but what it is not going to do is interpret all of the information for you (unless you have incredible documentation). Think of the data provided by AI in the larger context: what sorts of conversations are you or your colleagues having with clients? What are some of the top-of-mind concerns in the news? Context is king in this matter.
Make sure to approach interpretation collaboratively to ensure that your final decisions are well-rounded and thoroughly considered.
Sharing results and learnings
Traditional methods of sharing research results, like PowerPoint decks, often result in the findings being overlooked and forgotten.
In the dynamic and complex business environment, it might be that when sharing the results, the world has already changed and there is a need to revisit some part of the research and update it. The optimal way to share and keep results alive is through dynamic, collaborative platforms.
… and sometimes, all you need is a custom dashboard.
They can take time, but dashboards are surefire ways for management and stakeholders to easily access results. You can amaze your market research colleagues by sharing your magic. Imagine a world where you can say that you’ve built a dashboard for your management team. In this dashboard, they can easily access the results, and AI is constantly updating the latest market insights related to M&A and investments in the sector.
Conclusion
Generative AI tools are changing the market research process: how we define objectives, formulate questions, analyze data, and share the results.
AI helps streamline wish-lists, ensuring precise and impactful research objectives.
It distills complex questions into clear, actionable topics based on vast data sources, and significantly enhances efficiency in data collection and analysis.
Still, while AI offers invaluable insights, human expertise remains crucial for interpreting findings and making strategic decisions.
Conduct market research using AI as a sparring partner to test and challenge assumptions. It will bring the superpowers needed to market research relevant in a constantly evolving business environment.
Disclaimer: All the tools mentioned in this article to enhance your way to do market research smarter and faster are available in Valona Intelligence’s toolbox.
