Redefining quantitative research with data, AI, and human insight

October 2024

Article by Day One Quant Experts: Hannah Tough Niki Kovonuk Emma Eden

Quantitative research is long overdue for an overhaul. Despite its known limitations, traditional methods have remained largely unchanged for the past 30 years even with the speed and acceleration of AI uptake in many other areas today. Relying heavily on self-reported data, which can be biased, and rigid survey designs that confine respondents to predefined categories, these methods can fall short of capturing the true complexity of human thoughts, feelings, and behaviours – leaving businesses without the full picture needed to make informed decisions. After all, we don’t ask friends to rate their weekend on a scale of 1-10—so why should we expect such tools to fully reveal meaningful insights in research?

Additionally, the data collection process can be very slow, leaving insights outdated by the time they’re delivered. For clients this can prevent them taking action on the findings.

With advancements in AI and real-time analytics, however, we are on the brink of a major shift. The future promises more agile, predictive, and engaging approaches that can deliver deeper and faster insights.

In a recent roundtable discussion, Day One’s quantitative experts—Hannah Tough, Emma Eden, and Niki Kovonuk—explored the key changes and opportunities shaping this evolution. Here are the trends they identified:

AI as the Force Behind Faster, Smarter Research

AI is set to be the driving force behind change in quantitative research, primarily by automating data collection, survey design, and analysis. Tasks that once took time and effort will now be streamlined, allowing more room for strategic thinking. While it’s still early days, we’re already seeing AI and automation take over some of the routine workload, allowing us to dedicate more time to deeper interpretation, crafting strategic summaries, and translating insights into actionable plans. AI-driven coding of open-ended responses within closed systems has allowed us to find the initial story faster while we wait for the human-led coding to be completed.

Unlocking the Power of Data Integration with AI

AI will unlock the full potential of data integration, allowing us to combine diverse data points in ways that to date have not been possible. Our clients often have data scattered across various sources—different types of research studies, secondary reports, syndicated data, behavioural data—and AI will help us bring these together.

Our vision is to become curators of insights, providing a single, cohesive version of the truth from multiple relevant sources. Allowing our clients to move forward with confidence, knowing their decisions are supported by insights that cover all angles. We imagine a future where AI can digest vast amounts of data and reports, summarise key themes, flag areas for deeper exploration, and identify emerging trends, enabling a competitive advantage for clients.

Real-Time Insights and Visualization at Your Fingertips

AI will change not just the speed of insights but how we visualise and interpret them in real time. Instead of waiting for data processing, live dashboards at our fingertips will be the norm, allowing us to instantly see and understand what’s happening. This means faster, more accurate decision-making, allowing clients to respond by adjusting tactics in response.  These real-time insights and accelerated visualisation will mean reporting time can be spent getting to the story faster, rather than spending time on charting.

In the future, we might engage with hundreds of people in real time, using shorter interviews and to quickly build massive datasets. AI will distil this information into clear, actionable stories, transforming the way we approach research.

From Reactive to Proactive with Predictive Analytics

AI’s ability to model and forecast behaviours could shift researchers from being reactive—describing what has already happened—to proactive, predicting what comes next. Imagine if we could predict with accuracy how a brand launch was likely to play out, within micro-segments of customers on a week-by-week basis, this would allow clients to course correct and improve their chances of success. While we’re not fully there yet, the ability to combine data sources and apply predictive models is an exciting possibility that could deliver significant ROI.

Blended Quant and Qual Research

The integration of qualitative and quantitative research is becoming easier. While auto-coding has made some progress, it’s still unable to surpass human interpretation. We’re pushing this further by combining short, 15-minute quantitative surveys with AI-moderated qualitative follow-ups to provide richer insights from larger samples. Intelligent AI chatbots can ask smart probes based on survey responses in real time, capturing deeper, more emotional reactions. This blurs the line between quant and qual, enabling us to capture the ‘what’ and the ‘why’, whilst moving us away from rating and ranking scales that fail to capture genuine responses. It’s an exciting innovation that has many clients intrigued, and it raises the question of whether the traditional distinction between qual and quant will continue to hold in the future.

We are on the cusp of major changes in the market research industry, which is likely to look very different in the next 3–5 years. It’s not about adopting new methods for the sake of innovation; it’s about empowering our clients to make real-time decisions grounded in the analysis of comprehensive data. So that we can provide our clients with a true competitive advantage, enabling them to respond to evolving market and customer dynamics.