Dive into the cutting-edge world of on-demand desk research, where the speed and horsepower of AI combine with the agility, rigor, and savvy of humans to help brands stay ahead of the curve. In this conversation, we'll cover methodologies helping leading brands make smarter, faster decisions in an increasingly competitive "speed to insight wins" landscape.
I. The Current State of Research
For years, businesses have relied on traditional research methodologies such as quarterly brand trackers and annual surveys to inform their decision-making processes. While these approaches have their merits, they often fall short in providing the real-time insights necessary to navigate today's dynamic market conditions.
Victoria Sakal highlights the limitations of this approach:
"Typically it's about a quarterly scan of our competitors, or we do a refresh on the brand tracker. And some of those things don't need to happen more frequently. But if we go back to the very simple C's, which have survived the test of time for a reason. Are you getting always on insights on some of those things?"
The need for faster, more frequent insights is driven by several factors:
Rapidly changing market conditions
Increasing competitive pressures
The need for agile decision-making
As businesses strive to stay ahead of the curve, they're realizing that waiting for quarterly or annual insights is no longer sufficient. This realization has paved the way for more agile research methodologies.
II. The Rise of Desk Research and AI-Augmented Methodologies
Enter desk research and AI-augmented methodologies. Desk research, which involves leveraging existing data and information, offers several advantages over traditional primary research:
Faster turnaround times
Cost-effectiveness
Ability to leverage a vast array of existing data
Victoria explains the power of desk research:
"We are an on-demand desk research platform. So not the primary space, but more data research, information sentiments that exist already out there. And they just have to be, you know yours for the taking. If you can go and collect them and mine them."
The integration of AI into desk research has further amplified its benefits. AI can rapidly collect and analyze vast amounts of data, improving both accuracy and efficiency. Victoria shares an example of how AI enhances their research process: "We built a clarification engine that the way a human or a consultant might say, 'You sure you don't want to look at North America instead of just US? Are you looking at smaller events as well as conferences?'"
However, it's important to note that AI doesn't replace human expertise. Instead, it augments it. Victoria emphasizes this point: "There are certain pieces where AI can definitely help accelerate, make things faster cheaper... and there are certain steps where AI is certainly not there yet." The ideal approach is a human-AI collaboration model, where AI handles initial data gathering and analysis, while human experts focus on verification, contextualization, and insight generation.
III. The Value Proposition of Agile Research Methodologies
The adoption of agile research methodologies offers several key benefits:
Increased speed and frequency of insights: Moving from quarterly to continuous monitoring allows businesses to react quickly to market changes.
Improved decision-making capabilities: Victoria introduces the concept of "stupid tax" - the cost of making uninformed decisions. Agile research helps reduce this tax by providing timely, relevant insights.
Cost-effectiveness and resource optimization: By reducing time spent on manual research tasks, researchers can focus on high-value activities like synthesis and strategy.
Victoria sums up the value proposition: "We are replacing or delegating. Here's totally saying, AI, take this, do this. You know this for us? That's basically the initial scrape of whatever's out there instead of me opening up 50 tabs and then thinking of another permutation of the search term and opening up another 50 and going through and collating."
IV. Challenges and Considerations in Implementing Agile Research Methodologies
While the benefits are clear, implementing agile research methodologies comes with its own set of challenges:
Cultural and organizational shifts: Moving away from traditional research cadences requires a significant mindset change.
Data quality and reliability concerns: Ensuring the accuracy of AI-generated insights is crucial.
Skill development for researchers: Researchers need to learn to work effectively with AI tools and focus more on synthesis and strategic thinking.
Victoria acknowledges these challenges:
"There are going to be new skills needed. But it's not to say that we're going anywhere, because actually, the value of research has always been, what do I do with the information, what is the insight?"
V. The Future of Research: Trends and Predictions
Looking ahead, several trends are shaping the future of research:
The growing importance of qualitative insights: Adding "emotional texture" to quantitative data is becoming increasingly valuable.
The convergence of primary and secondary research: Integrating multiple data sources for comprehensive insights.
The evolving role of the researcher: From data collectors to strategic insight partners.
Victoria paints a picture of this future:
"We just move away from this end of the what and the, you know, commoditized just data collection. And we move into the synthesis and the analysis and the implications. And then the future thinking."
Conclusion
The research landscape is evolving rapidly, driven by technological advancements and changing business needs. Agile, AI-augmented research methodologies offer a powerful solution to the challenges of traditional research approaches. By embracing these innovations, organizations can gain faster, smarter insights, driving better business outcomes in an increasingly competitive landscape.
As Victoria aptly puts it, "Don't let yourself be taxed." The future of research is here, and it's agile, AI-powered, and ready to transform how we understand and respond to market dynamics.
