Let’s Take a Walk Down Memory Lane…
A lot can change in a decade—and in the world of market research, everything has.
From clipboards and call centers to dashboards and Gen AI, the journey from 2015 to 2024 has reshaped how we understand people, brands, and behaviors. What was once a process that took months now happens in days—sometimes hours. But beyond the speed and tools, the real story is this: the why behind the numbers has become just as important as the numbers themselves.
So whether you're a seasoned researcher, a curious founder, or someone trying to keep up with the fast-moving insights game—here’s how we got here, and where we’re heading.
2015–2017: The Digital Door Opens
Let’s rewind to 2015.
Back then, traditional research still ruled. Long surveys, in-depth interviews, and physical focus groups were the norm. But under the surface, things were starting to shift.
What changed?
Smartphones were now in everyone's hands. Internet usage exploded globally. People started expecting everything—including surveys—to work on mobile. Tools like SurveyMonkey, Google Forms, and Typeform began empowering teams to run their own studies.
We also saw the early signs of automation: pre-qualified online panels, basic data cleaning scripts, and quicker ways to get a representative sample without hiring a giant agency.
📍 Real-world flashback: Remember those pop-up surveys at the bottom of web pages asking “How did we do?” That was early digital feedback at work!
The big unlock: Market research was no longer just for big brands. Smaller companies, startups, and even students started tapping into online tools to gather opinions and test ideas. It was the beginning of democratization.
2018–2020: Automation Meets Intelligence
The next few years brought more than just faster tools—they brought smarter ones.
This is when artificial intelligence (AI) started turning heads. Suddenly, machines could scan open-ended responses, detect sentiment, flag trends, and even write basic summaries. Manual tasks like coding responses or building charts? Mostly automated.
What stood out:
- Chatbots started conducting surveys in real-time, engaging users like a conversation—not a test.
- Data from multiple sources—social, mobile, purchase behavior—began blending into research workflows.
- Predictive models helped businesses anticipate consumer behavior rather than just react to it.
Then came 2020—and everything changed.
COVID-19 forced a hard pivot. No more in-person groups, no more intercept interviews. But the industry didn’t pause—it evolved. Overnight, Zoom became a focus group room, WhatsApp became a feedback channel, and researchers leaned into digital like never before.
⚡ Lesson learned: Flexibility wasn’t a nice-to-have—it became a survival skill.
2021–2022: Research Gets Agile (and a Little More Fun)
As life settled into the “new normal,” research teams didn’t just return to old ways—they built smarter, faster ones.
Welcome to the agile research era. Inspired by tech teams, market researchers embraced quick-turn projects, test-and-learn models, and smaller, more frequent studies that tracked real-time shifts in customer needs.
What did this look like?
- Shorter, mobile-optimized surveys (goodbye, 30-minute forms).
- Live feedback loops through platforms like Suzy and Remesh.
- Dashboards and data visualizations that let you spot trends as they happen.
Bonus? Teams across functions—marketing, UX, product, sales—could now access research in real time. It wasn’t just locked in PDF reports anymore.
🎯 Why it matters: Research became less about reports, and more about real-time decisions.
2023–2024: Gen AI, Ethics, and the Data Avalanche
Now, we’re in the thick of it—navigating Generative AI, privacy regulations, and an overwhelming amount of data.
Sound familiar?
AI tools can now write survey questions, summarize transcripts, and even simulate how a group might react to a new product—before you’ve spent a dollar on development.
But with power comes responsibility.
- Regulations like GDPR and CCPA remind us that just because you can collect data, doesn’t mean you should.
- Synthetic respondents are on the rise—but require validation to ensure relevance and integrity.
- Generative AI is a game-changer, but it needs human guidance to avoid bias or shallow results.
A big shift? Clients don’t just want data anymore. They want
