By Aneesh Dhawan
In the most apparent news of the day: The market research industry is undergoing a significant transformation.
Over the last decade, we’ve seen a space traditionally owned by agencies and full-service market research firms, blossom into a range of DIY research platforms – each specializing in a long-tail offering to help service the ever-growing niche needs of the industry.
Then 2023 hit, and with it, the rise of AI solutions within the MR sector. As an AI Research company, you’ll hear it from us first – we’ve loved the changes and the value that these solutions have brought to the space! From time savings to unexpected findings uncovered from formerly unexplored data, AI has already proven to be a staple player in every insights professional’s toolkit.
But the big question we pose: Is AI being used to the best of its ability? Is it really driving value in how it's being used across the industry?
We’ve got a thought on that – and an answer. So, let’s dive into it, and uncover what we believe is the next iteration of AI in Market Research: Researcher-Driven AI.
The dilemma in market research today
In the fast-paced world of market research, Insights professionals face mounting pressure to deliver insights that are both timely and comprehensive. You have a near endless number of stakeholders with a near endless number of questions to solve. And yes, ideally, you’re able to answer them all, and answer them all asap.
And yes, you have a set of tools and resources at your disposal to answer them – including DIY tools and full-service vendors. However, whichever route you go often requires making difficult choices:
- Speed vs. quality: The need for rapid results often leads to compromises in the depth and accuracy of insights. So, is a DIY platform that’s offering “answers within hours” truly the answer?
- Time savings vs. cost savings: DIY platforms may save money, but they require significant manual effort, which can offset any initial cost advantages. And as great as the final deliverables that you’ll get from a full-service vendor may be, your budgets may be eaten up quite quickly – lessening the total number of projects you can feasibly service.
- Quantitative vs. qualitative: The depth of qualitative research is often sacrificed for the broader reach and speed of quantitative methods, leading to an incomplete understanding of consumer behavior. In an ideal world, shouldn’t you want both?
How the limitations of current solutions led to the need for AI
If it’s not apparent by now, market research means compromising - insights professionals are forced to choose. Do I go the DIY route for this project, or hand it off to my go-to agency?
And as mentioned above, that decision highlights the many tradeoffs: Speed or quality? Time savings or cost savings? Quantitative capabilities or qualitative capabilities?
Too often, the solutions you're most familiar with don't have the answers. The agencies you've partnered with are likely expensive, and frequently take a bit too long to wrap your project. And while DIY tools might be quick, you're likely still racking up hours of your own time to drive meaningful insights.
That’s where the promise of AI came in. A near miracle elixir granting you instant insights, with “speed” being the first area AI proved to aid in. And in recent years, almost every solution has tacked on Al functionality to keep pace.
But is speed alone enough to drive actionable insights and effective strategies for your org? From what we’ve heard in countless conversations, the AI promises aren’t exactly living up to expectations and the tradeoffs keep coming. Here’s just a few we’ve heard:
- The AI is "out-of-the-box": the outputs rarely feel useful or custom to your needs.
- The technology isn't intelligent: you still have to dig for insights.
- The user experience isn't intuitive: you have to learn new workflows and navigate a subpar experience.
- The efficiency isn't there: you're spending too much time editing deliverables.
When solutions complicate your work, they're not solutions at all. And that’s why we believe in a different approach. One that’s truly researcher driven.
The emergence of Researcher-Driven AI
What is Researcher-Driven AI?
Researcher-Driven AI is a new approach to market research that puts the researcher at the center of the process. Unlike traditional AI tools that offer pre-set functions and outputs, Researcher-Driven AI adapts to the specific needs of each researcher and project. This approach ensures that the insights generated are not only accurate but also highly relevant to the unique goals and context of each study.
The philosophy behind Researcher-Driven AI
At Knit, we believe that researchers should be in control of their tools, not the other way around. Researcher-Driven AI is designed to empower insights professionals by allowing them to customize the platform to suit their specific needs. This means that every aspect of the research process – from designing the questionnaire to analyzing the data and generating reports – can be tailored to align with your organization's goals.
Key components of Researcher-Driven AI
Customization
One of the standout beliefs behind Researcher-Driven AI (and of Knit’s platform) is its high level of customization. Researchers can tailor the platform to fit their specific needs, ensuring that the outputs are aligned with the goals of their study. For instance, the Innovation team within your org runs research completely differently (and for completely different reasons) than the shopper insights team. So why shouldn’t the AI you use within your research adapt to this? Custom use cases. Custom questionnaires. Custom reporting. Customized AI for your unique research needs.
Control
Customization is just the starting point to having your AI work for you. It gets you to level 0 – a healthy baseline. To make AI even more effective for research, it must also be controllable. Your AI shouldn’t be a black box, with a lack of understanding into how it derives its “insights.” Nor should it provide you with takeaways that you should take at face value without the ability to manipulate and further contextualize for your organization’s needs. Researcher-Driven AI backs the belief that AI should work for you – answering your questions at a moment’s notice, citing its sources, and learning your preferences for the next study.
Comfortable
The last of the “3 C’s” guiding Researcher-Driven AI is comfort. Let’s face it, you have enough new tech to deal with on the day-to-day. To truly be researcher-driven, AI and the tools that it’s applied within should feel instantly familiar and intuitive for you. It should work within how you already operate day in and day out. Why shouldn’t your questionnaire be presented in a doc-like editor? Why shouldn’t your presentation look and feel like PowerPoint? We think it should (and it does within Knit)!
Benefits of Researcher-Driven AI
No compromises, no tradeoffs
By truly tapping into Researcher-Driven AI, you no longer have to choose between speed and quality, or time savings vs cost savings. The efficiency of the AI, guided by the customization of how you run research, allows you to conduct comprehensive research in days, not weeks, without sacrificing depth or rigor. This means you can meet tight deadlines while still delivering high-quality insights that drive meaningful action. And with the research’s most time-intensive tasks now covered by AI, you can accomplish projects on DIY budgets, but as hands-free as you would a full-service project.
Amplifying institutional knowledge
Your expertise is invaluable, and Researcher-Driven AI means you can amplify it. By allowing you to customize every aspect of the research process, Researcher-Driven AI ensures that the insights generated are not only accurate but also deeply relevant to your organization’s needs. This approach allows you to leverage your institutional knowledge to its fullest extent, and ultimately ensures every report you get is usable at first pass.
Enhanced reporting and presentations
By applying Researcher-Driven AI end-to-end throughout the research workflow, you can streamline the process all the way to the creation of powerful, actionable reports. By tying research objectives to a questionnaire and even an analysis plan through AI, you can get to a final report with ease to bring your findings to life in no time. This means you can spend less time digging into raw data or formatting reports and more time driving action with your insights.
Faster, more accurate insights
With Researcher-Driven AI, you arrive at actionable answers faster, the first time, every time. You’re not just getting raw AI outputs that spit out summaries that often don’t align to your goals. The outputs are more actionable and more contextually relevant – built to minimize errors and maximize efficiency, ensuring that your research delivers the insights you need, when you need them.
The future of market research with Researcher-Driven AI
A new paradigm for the industry
Researcher-Driven AI is not just a tool; it’s a movement. As the market research industry continues to evolve, this approach is poised to become the standard, offering a new level of efficiency, accuracy, and customization. By empowering researchers to take full control of their projects, Researcher-Driven AI is set to redefine how insights are generated and utilized. The flexibility and power of this approach mean that it can be adapted to a wide range of research needs, from quick, iterative studies to deep, comprehensive analyses.
Commitment to innovation
At Knit, we are committed to continuous innovation. We believe that the future of market research lies in the hands of the researchers who know their audience and objectives best. As such, we will continue to refine our platform, adding new features and capabilities that enhance your ability to deliver actionable insights. Our focus will always be on providing a tool that not only meets your current needs but also anticipates the challenges you will face in the future.
Leveraging the power of AI
Researcher-Driven AI represents a fundamental shift in how market research can be conducted and how AI should be applied. By placing the researcher at the center of the process and leveraging the power of AI, Knit enables you to conduct faster, more comprehensive research without the typical tradeoffs. This approach not only saves time and money but also enhances the quality of insights, making it possible to deliver deeper, more actionable findings that drive meaningful business decisions. The future of market research is here, and it’s researcher driven.
Bonus: What about quant vs. qual?
You may have wondered where we were going with the whole “quant vs. qual tradeoff” debate. For a few bonus points, we thought we’d highlight that though most platforms lean one way or the other – Knit allows you to run both quant and qual together in a single study! We pair traditional quantitative surveys supporting over 100 question varieties, with asynchronous voice of the customer video responses. This means you can get the hard numbers and scale through quant for the “what,” with the “why,” reasoning, and emotions of qual in a single study.
Why not try Knit?
With that, I’ve just got one ask: Experience the power of Researcher-Driven AI for yourself.
Find out how Knit can transform your research process and help you deliver deeper, more actionable insights in a matter of days. Whether you’re looking to streamline your existing processes, enhance the quality of your insights, or simply reduce the time and cost associated with market research, Knit is here to help. Join the movement toward Researcher-Driven AI and take your market research to the next level.