In the last few months, tools like DALL-E and ChatGPT took over the world, and it seems like that’s the only thing people are talking about today.
But when it comes to innovation research, AI wasn’t really a brand-new thing. Market research pros have long been using AI tools across the innovation journey to generate ideas, analyze data and make decision-making easier.
In this article, we’re going to cover 4 ways to use AI to power up your innovation process. We’ll explain how you can use AI to:
Generate ideas
Collect and analyze data
Automate repetitive tasks
Make qualitative research quantifiable
#1. Generating Ideas
Artificial intelligence is perfect for brainstorming ideas based on existing data and helps to combine different data sets.
AI is trained on online resources and social media to understand trends and patterns. It learns about what’s already out there and creates recommendations for potential innovations. So, it’s basically a brainstorming treasure trove that you can turn to anytime you need inspiration.
ChatGPT is an excellent resource to start with. The platform runs on a language model architecture (Generative Pre-trained Transformer or GPT). Since this model is trained on vast amounts of information from the internet like books, websites, and articles, it can develop new ideas for basically anything (if done correctly). If you give it the proper prompts (i.e. examples), it can produce unique ideas.
And we have proof of that! We asked our team to use ChatGPT to come up with 20 brand-new milkshake flavor ideas and then tested them all on Upsiide. The idea was to see whose ideas would win out - those created by AI or those created by HI (human intelligence)?
ChatGPT did a great job creating creative flavors like “Blueberry Pancake Breakfast.” But humans thought about adding extra things into milkshakes like “Birthday Cake flavor with an actual cake slice on top.”
Oh, and in the end, humans won (*phew*). The “Chocolate + Baileys” flavor came first with an Idea Score of 68 - just 1 point above “White Chocolate & Raspberry Truffle, which was an AI-generated idea. So yeah, it was a tough race.
Curious about the study? Check it out on the platform now.
All this shows that AI can be a helpful tool to lean on in the early stages of innovation. Just make sure that you give the AI tool the right information to work with (e.g. examples and explanations), and it can deliver really creative ideas in a matter of seconds.
#2. Collecting and analyzing internal and external data
The beauty of AI technology is that it’s collecting data all the time. And that’s what makes it such a powerful tool in market research.
By studying large amounts of data from various external sources, AI algorithms can identify patterns and trends that might not be immediately apparent to human researchers. This can lead to the discovery of new insights and opportunities that can help businesses stay ahead of the competition.
It’s especially handy when you’re working on a project for a long time. You kind of start developing bias towards certain ideas. For example, if you’re renovating a QSR menu, you might know from your previous experience that chicken burgers are usually successful with your target market. So, you subconsciously lean towards new types of chicken burgers you’re testing.
In that case, AI can act like the devil’s advocate. “Even though chicken burgers are great, I see a trend toward lamb meat among your target audience, so why not try lamb burgers?” Because it is in the business of data collection, AI allows you to think outside the box and consider new options.
Plus, if we think about the various stages of the innovation process (beyond idea generation), AI can help conduct predictive analytics. By analyzing past sales data and other relevant metrics, AI algorithms can predict future trends and consumer behavior. This information can be invaluable for businesses looking to introduce new products or change their marketing strategies.
#3. Automating repetitive tasks
If you think about it, data isn’t that exciting. Few humans are eager to analyze large chunks of data - especially when we’re talking about survey data which contains heaps of unorganized responses. As a data or a research analyst, you might remember those long, boring Excel sheets that you have to go through to make a simple model or a report.
Well, an AI tool is more than happy to dig through that data for you. And it can do that much quicker (and probably more accurately) than you can. It’s an ideal partner in crime when it comes to repetitive tasks. And since you’ve saved time and money by automating this part of the innovation process, you can focus on the “bigger picture” and think about the strategy.
#4. Making qualitative research quantifiable
The innovation process in market research doesn’t just mean quantitative research. It’s also about going through tons and tons of information gathered from qualitative research. And AI has so many applications in this realm too.
One way that AI can make qual more manageable is by using its natural language processing algorithms. After you conduct focus group interviews or get results for open-ended questions from a survey, AI can analyze large amounts of survey responses quickly and accurately. So, what would usually take a whole qual team and weeks of hard work now only takes 1 tool and a few hours (or even minutes).
At Dig Insights (Upsiide’s parent company), we use AI tools all the time to summarize and quantify qualitative data. For example, when we conducted an inflation study, we used AI to analyze open-ended question responses to get a sense of the emotion underpinning them. Sentiment analysis involves analyzing customer feedback to determine how customers feel about something. With AI, Dig quickly identified positive and negative responses, making it easy to find overarching insights relatively quickly.
AI can be so useful for the innovation process...
…but it’ll never replace humans. Even though AI systems can save time and money on different types of market research tasks, they can’t replace the strategy behind the innovation project.
But that’s not a bad thing. If anything, it means that market researchers now don’t have to spend their efforts on tedious, repetitive tasks. Instead, they now have an opportunity to think about strategizing for the innovation process and business as a whole.
Want to start applying AI in your market research process? We have a great article that explains how to use AI to generate new ideas, so be sure to check it out.
