At a TMRE conference, a CPG executive made a provocative claim: that a $30,000 research study could now be completed for $1,000. While an attention-grabbing statement, it lacked specifics about what could actually be accomplished at that price point, what you would get, and how valuable those insights would be. Throughout the conference, many attendees expressed unease about such dramatic cost-reduction claims. Let’s take a moment to look at what a reasonable cost of market research is.
Market research is changing to meet new business realities. Companies want faster, and deeper insights despite tighter budgets, pushing research teams.
It all shows in the budgets, too. Marketing budgets have decreased significantly, down 30% since 2020. This pressure has pushed research teams to rethink how they invest their limited resources.
We know that budget constraints and unfulfilled AI promises can lead to bad research outcomes. That can include:
These hidden costs of inadequate research often far exceed the investment required for proper market research. Organizations must consider both the immediate costs and the long-term implications of their research investment decisions.
It’s possible to optimize research costs without compromising quality through several approaches. This includes clearly prioritizing research objectives to focus resources effectively, optimizing sample sizes and study scope based on based on statistical requirements and decision risk rather than arbitrary targets, leveraging existing research when appropriate, and utilizing AI-powered research tools to minimize the “toil” of repetitive tasks. Building partnerships across the organization can also help share costs for larger research initiatives.
Some organizations now embed approval processes into their financial systems, requiring senior leaders to sign-off on how insights will drive decision-making before releasing research funds.
Some teams are finding success by mapping research requests directly to strategic objectives. This approach helps ensure that limited budgets are spent only on research that directly supports key business priorities rather than “nice to have” projects.
Some are turning to AI for some projects but augment it with human intelligence to ensure everything is tracking for success.
The key to understanding appropriate research costs lies in the decision being informed. When making a $5 million decision, relying on a $1,000 AI study isn’t prudent. However, lower-cost approaches might make sense for simpler decisions like choosing between purple or red for package colors.
Having low-cost AI-powered research solutions to inform more low-risk decisions could be a great way to prevent wasted resources. However, it also has the risk of dragging teams into doing testing where judgement would have resulted in the same or better decision.
This pragmatic view suggests organizations should evaluate research investments based on the value of the insights needed. While some traditional research might be replaceable with lower-cost alternatives, many of these dramatic cost reductions remain more promise than reality today.
Quality concerns significantly impact research costs, particularly in B2B research. Some organizations report having to discard and replace a significant number of responses due to quality issues. This reality means that seemingly lower-cost approaches can actually become more expensive when accounting for the need to collect additional data.
Teams are addressing these challenges through multi-layered approaches:
Rather than completely automating research, organizations are finding strategic ways to incorporate AI while maintaining quality. One example comes from a conference session about conversational approaches to trade-off exercises. While the conversational interview approach cost more and took longer than traditional checkbox surveys, it delivered better results.
The team found that participants considered choices more realistically and thought more deeply about their responses, rather than just clicking boxes to complete the survey quickly. The extra cost and time yielded significantly better insights, making the additional investment worthwhile.
Modern market research can demand an investment in technical solutions. This includes licensing fees for specialized software (survey platforms, statistical analysis tools, data visualization programs), secure data storage solutions, and cybersecurity measures to protect sensitive information. Organizations must also budget for ongoing staff training to maintain proficiency with these tools and regular system updates to ensure data quality and security.
Research teams are evolving their approach to offer the most value within the changing demands they face. They’re exploring new technologies while maintaining core research principles. The focus is increasingly on delivering clear, actionable insights that drive confident decision-making, even in resource-constrained environments.
AI will likely add the most value in situations where decisions don’t have enormous consequences. This suggests a nuanced approach where organizations carefully evaluate the relationship between research costs, decision importance, and methodology choices.
For business leaders evaluating research investments, success lies in finding the right balance. The future of market research isn’t about choosing the cheapest option or completely replacing traditional methods with AI. Instead, it’s about finding the right combination of approaches that deliver reliable insights for specific business decisions while making smart use of limited resources.
Book a free strategic assessment to optimize your research investment now.
Many thanks to both Casey Mohan, VP of our Qualitative Practice and Richard Scionti, VP of Product & Innovation at CMB for their contribution to this blog.