We’re at the peak of the hype cycle with AI-augmented market research.
In a recent blog post, I commented on the sheer volume of vendors at Quirk’s New York who had become AI-enabled, seemingly overnight. They’re right to be bullish, but understanding what was real and what was vapour was hard to decipher. What is not hard to comprehend is the profound impact AI is going to have on our industry.
But here’s the rub: those familiar with Gartner’s Hype Cycle know that after the Peak of Inflated Expectations comes the Trough of Disillusionment. It’s a steep drop. So how can we smooth it out?
From my perspective, what seems to be overlooked is the vital importance of feeding AI with high-quality data. By high-quality data I mean deep, rich, authentic feedback from known customers. Yes, AI can help with fraud detection, management and mitigation to deliver clean data. But what it cannot do is turn water into wine. Garbage in, garbage out, as they say.
The phrase “garbage in, garbage out” isn’t just a catchy quip; it’s a sobering reminder of the stakes involved when it comes to AI-driven market research. The quality of the insights derived from AI is intricately tied to the quality of the data it ingests. Eliminating fraud and cleaning datasets are essential, but true data quality extends beyond that. Even seemingly pristine data can mislead if it lacks depth or authenticity, which can lead to misguided decisions.
The quality of the insights derived from AI is intricately tied to the quality of the data it ingests.
As Rival’s Head of Innovation Dale Evernden points out in our Quirk’s AI presentation, quality-assurance is key. AI can do amazing things, but it requires the oversight and approval of a skilled researcher. Ultimately, we are accountable for the quality of our work. AI can accelerate our output, but ensuing quality is, for the time being, the sole domain of the researcher.
Elevating data quality through research experience
One way to overcome the conundrum of "garbage in, garbage out" can be achieved by thinking beyond “surveys” and looking for ways to craft meaningful research experiences. The idea that research could be fun and engaging has largely been ignored by our industry. Taking that a step further, I will go so far as to say most surveys are a terrible experience. They are dull, long, impersonal and overly reliant on email. The result has been an erosion in the confidence stakeholders have in research in general and growing challenges against the integrity and authenticity of consumer feedback.
Most surveys are a terrible experience. They are dull, long, impersonal and overly reliant on email.
At Rival we have found that great experiences build trust with participants which in turn inspire more authentic, insightful feedback from customers. That means crafting personalized, relevant and meaningful customer engagements that invite customers to share their thoughts and experiences on the channels they prefer—like mobile messaging—in a communication style they use every day; casual language, emojis, video, and more. Asynchronous engagements that mimic the daily interactions people have on their mobile phones. An approach we call conversational research.
A conversational approach to research is not just a methodology—it's a strategic shift in the way we approach research from authoring to deployment, data collection, analysis, and storytelling.
At its very core, conversational research was created to improve data quality. Recognizing personal preferences and facilitating personalized engagements that make participants feel heard by demonstrating the value of their feedback. By designing research experiences that foster a genuine connection between brands and customers, researchers gather deeper richer insights. They have more impact. They inform better decision making.
A customer who feels heard and respected is more likely to provide feedback that transcends superficial responses, providing the rich data required for thorough AI-driven analysis.
Authentic customer feedback is the unrefined core that feeds AI algorithms and expedites the journey to insights. When armed with deep, genuine feedback, the potential for uncovering invaluable trends, preferences, and opportunities becomes boundless.
AI serves as an amplifier for authentic feedback, enabling rapid processing and analysis of colossal datasets. On the other side of the coin, AI will amplify errors and attempt to fill gaps with generalized language that it presents with extraordinary confidence. This makes it even more important to recognize that the quality of input data determines the caliber of output. Upholding a high standard for data quality isn't just prudent—it's a strategic necessity for any market researcher aiming to harness the full might of AI.
AI serves as an amplifier for authentic feedback, enabling rapid processing and analysis of colossal datasets.
Expanding our definition of high-quality data to include richness, depth and context is not a tick the box exercise. It requires creativity and commitment. Does that take time? Yes, it does. By the same token AI in its current state is extremely adept at saving researchers' time. Lots of time. For example, our AI Summarizer tool can reduce tasks that once took 30-40 hours down to 20 minutes.
The question is, what do we do with that found time?
My suggestion is that we redirect those resources into efforts that will elevate the role of the researcher. Focussing on ways to improve the quality of your inputs and working in concert with AI to manage the quality of the outputs. Creating sharable research experiences, unlocking insights in never-before-seen clusters of data, taking our understanding of human behavior to new depths and telling stories that inspire change. And that is, ultimately, why so many of us chose to be in this industry in the first place.
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