How AI Helps Automate the Tedious Tasks in User Research & Reporting
Updated: Jul 10
Embracing Transformative Tech and Eliminating the Guesswork
Our current preoccupation with AI (Artificial Intelligence) has been well-documented. The media dedicated to this subject alone would convince any faraway life-form that humans are obsessed with the idea of self-aware bots. Is our fascination warranted and is the majority-held impression of AI even adjacent to reality?
Even a simplified answer is dual-sided. In actuality, it’s not so much “Sentient robots taking over the world” as it is “algorithms using masses of data to learn and mimic.” While the terms are closely related, AI and Machine Learning are not actually interchangeable. AI is the overarching concept that attributes human-like intelligence to machines based on the ability to carry out tasks. And machine learning, a subset of AI, is a method of training and enabling machines to learn from great quantities of data in order to make accurate predictions. In essence, machine learning isn’t actually intelligent, rather, its power is attributed to algorithmic advancements, the rise in data, and the expansion in computer capacity. A perfect storm for technological proliferation.
Sounds great in theory. Still, what does this mean for me? What could it mean for companies? And why are certain industries so invested in machine learning? According to this high-level report from the data powerhouse, McKinsey, “AI is finally starting to deliver real-life benefits to early adopting companies”. These real-life benefits are hype-worthy, certainly for several industries. Since we’re focused on the category of customer and market research, let’s take a look at how machine learning will impact this field and those in it.
Here’s a scenario:
You’re a product researcher designated with the task of interviewing a sample of your customers or target audience for insight into the failure of a product line that your company has invested millions into. It’s pretty specific, but this hypothetical could be applied to anyone who works with research and data.
You aren’t interested in generating yet another survey to find out what you’re doing wrong from a series of lackluster responses. So you toss out the old research playbook. You’d like to actually engage with your customers, start a dialogue that leads to breakthrough insights. Of course, customer research interviews are nothing new. And there’s a reason many companies–large and small–are hesitant to plug more cash into avenues that may not yield any outcomes. Or rather, outcomes that can be used in a tangible way to move a brand, an idea, a product or service forward.
Time. Resources. Data. Effort.
For a long time, this has been the marrow of the argument weighed against conducting consumer research studies in the interview format. If you’re new to the research field, finding your feet as a reliable and credible interviewer has its challenges, however, the truly grueling task is yet to come. With all that interview data comes the responsibility of transcription and analysis.
Just imagine: as a researcher, you aim to conduct 30 customer interviews. Each interview is an hour long. That’s 30 hours of carefully listening for insights and perhaps a week of transcribing the speech to text. After which you still have to manually draw connections between interviews that share similar themes. We’re talking weeks of note-taking, gathering insights, finding patterns. Extremely inefficient, isn’t it?
So what has automation brought to qualitative research? Let’s break it down.
A sore-spot for many researchers is the necessity of transcribing speech. The industry standard for manual transcription time is 4 hours for every 1 hour of audio–with automation, it’s half the length of the audio.
AI has made remarkable strides in this area. Instead of funding the major expense of transcription services or having to wade through countless recordings, painstakingly typing out each word, automated transcription can turn speech to text in a matter of minutes. Not to overshadow Janice from reception and her speed typing capabilities–but machines are doing it faster and more accurately by the day.
Sentiment analysis may seem like a computational feature from the future, but due to advancements in Natural Language Processing–it’s very much an actuality.
With NLP, machines use computational techniques to analyse and synthesise human speech and language. During speech analysis, machines are able to scrutinise voice and decode human emotion, assigning sentiment to speech data. Market researchers can use the information to drill down into how people feel about ideas and products and brands.
If your participant is sad, happy or tentative during your research interviews–this will be noted in the transcription. This is particularly helpful when you’re researching a campaign or product, as it presents an opportunity for deeper insight by finding emotional patterns in the overall research campaign. So you’ll know exactly where to focus your problem-solving efforts.
Natural Language Processing (Topic Modelling and Clustering)
Ready to circumvent the legwork? Machine learning algorithms can take the body of information–in this case, your consumer research cases that have been transcribed and assigned emotive value–and can find and highlight themes, topics and common keywords. Doing this manually will require a lot of effort and accuracy on the part of the researcher.
Once you have all your information, machine learning helps to create clusters based on the topic models.
AI-enhanced tools for researchers are able to code all the information for you by helping to find similar topics and themes throughout the data you’ve collected. As a researcher, think of the time you’ll have saved on grouping data that shares similar patterns. And the kicker? Having this data searchable so you don’t have to go back and forth finding related keywords or themes for your reports.
Unlike humans, machines don’t display preference or partiality. So you can rest easy with the knowledge that your research isn’t tainted.
Whether you’re a researcher or non-researcher, the advent of AI in market research technology promises to offer far-reaching effects in the industry. Machines won’t overtake humans any time soon, but they can help us work more efficiently.
Voyc has brought automation to qualitative research and it can help you turn your data into actionable insights. Interested in using our platform? Sign up for your Demo today!