Innovating Insights: An Overview of AI Tools for Market Research & UXR

Post #3 of the AI for Market and UX Research Series

Hey Friends! Welcome to the third post in my series about AI for market and UX researchers where I’m exploring various ways we can incorporate AI into our work. As I mentioned in the first few articles, I’m trying a different AI writing tool for each post and the rest of this article will be written with the help of Jasper.

A quick bit of shameless self-promotion before we begin: I published an online course that provides an intro to AI for market research that (fortunately) has been receiving great feedback. You can use this link to register if you’re interested!

The Evolution of Research: How AI is Revolutionizing the Industry

The fields of market and UX research have seen a significant evolution over the years. Traditional methods of research involved manual collection of data through in-person or phone-based surveys, interviews, and observations, which were both time-consuming and prone to human error. With the advent of digital technology, research shifted towards more sophisticated methods like online surveys and social media analytics, leading to an increase in both the speed and scope of data collection. The newest shift into AI marks another massive stage in this evolution. Today, AI-powered tools are increasingly being adopted for their ability to analyze enormous amounts of data in real-time, predict patterns and trends, and generate insights.

With AI-powered tools at our disposal, there’s the opportunity for businesses to gain a deeper understanding of consumer behavior, identify emerging trends, and predict future customer preferences with unprecedented accuracy and speed. Even still, it’s important to remember that while AI can play a pivotal role in enhancing research, it does NOT replace human expertise. Rather than replacing skilled professionals, AI tools can complement our abilities by automating repetitive tasks and providing insights that can be further analyzed and interpreted by experienced researchers. You can read more about the pros and cons of using AI for research here.

Introducing Some Innovative AI Tools for Research

Most people by now know at least a few of the main players in the AI space. There is of course ChatGPT, Claude, and Bard, but there are also some major companies offering AI tools specifically for research like Remesh, UserTesting, Maze, and Typeform. In addition to these, I want to highlight a few innovative new tools being built that are worth monitoring.

UX Squid

UX Squid is, perhaps not surprisingly given its name, focusing on UX research. It focuses more on the interview and analysis process, but also offers a library of interview questions that can be helpful. Gathering feedback from user interviews and gaining quick insights and recommendations for how to improve your product becomes a lot easier. It has a free plan that allows you to get started and see if it’s something you’d want to consider adding to your toolkit.

Poll The People

Rather than using AI to replace research participants (which we’ll touch on below), Poll The People has a panel of real humans but uses ChatGPT to automate analysis and insights generation. The results have been really promising and the combination of AI plus human intelligence is something many want to see. It has the potential over time to become an all-in-one, quick solution for researchers.

Crayon

This is a fascinating market intelligence tool that utilizes AI to track and analyze competitors and market trends. Crayon also offers the ability to track competitors in terms of messaging, pricing, publications, and more. While this type of work doesn’t always fall under the same umbrella as market or UX research, it can be incredibly useful for any team to understand.

Now for a few tools that are a bit more controversial within the research community as they involve synthetic data. On one hand, synthetic data can help overcome issues of privacy and consent as it doesn’t involve real users or their information. It also offers scalability and cost-effectiveness, which is obviously attractive to researchers and stakeholders alike. However, synthetic data may not capture the nuances of the real world, bringing into question the validity and applicability of the results.

Is some data, even if we don’t know where it comes from or how accurate it is, better than nothing? That’s a debate many are having right now and is worth considering for yourself. I’ve seen a few researchers and designers rail against these tools and outright dismiss them which, personally, I don’t think is the right approach. We shouldn’t immediately flip the switch and shift everything we do to AI, but we also shouldn’t pretend there is no potential value here. So with all that in mind, there are some companies innovating in this space that I want to highlight:

Synthetic Users

The first is one of the most on-the-nose names I’ve seen: Synthetic Users. This new tool creates synthetic user profiles for the purposes of user research. Rather than going through time-consuming and often expensive participant recruitment, you can get started on your study nearly immediately with something like this.

User Persona Generator

Personas are incredibly useful tools for product and marketing teams, and can bring research to life in a way that numbers simply cannot. This is where the User Persona Generator from FounderPal comes into play. It offers entrepreneurs the chance to “understand your ideal customer without running 50 interviews.”

VisualEyes

The last tool I want to highlight is VisualEyes, which uses AI to approximate eye tracking — predicting where users would pay attention and how they would interact with your designs. Eye tracking has always been one of the methodologies that can captivate stakeholders and boost buy-in given its visual nature, which makes a tool like this all the more exciting to trial.

So, Should We Actually Use These?

There is no 100% yes or 100% no answer for this question. Ultimately, the adoption of any AI tool presents a mix of both opportunities and risks. These tools can offer efficiency and scalability in a way that hasn’t previously been possible, and can automate many labor-intensive tasks.

However, overreliance on AI poses some significant drawbacks as well. One of the major concerns is the risk of inaccuracy or misinterpretation of data. These tools may not be able to understand the nuances that can be discovered with human-led research, potentially leading to misleading conclusions and ineffective strategies. And while any research (including human-led) has the risk of introducing bias, it becomes nearly impossible to discover what biases are included in these tools.

My advice? Start learning and start experimenting! There are a ton of resources available to help you get started with AI (including my Udemy course), and we should all share what we learn to benefit the research community. Have you found a new tool, read something of interest, or simply want to connect? Please let me know!