Understanding Secondary Research in the Age of AI

When it comes to business intelligence, secondary research is often seen as a supporting player to the star role of primary research. Thanks to AI and the ever-increasing abundance of data, however, secondary research is becoming more substantial, easier to access and more critical to decision-making. Organizations that fail to take advantage of AI’s ability to bolster the depth and breadth of secondary research risk missing out on critical market insights and playing catch-up to competitors. 

What Is Secondary Research?

To best understand secondary research, it helps to define primary research: new, original data gathered by an organization for a particular purpose. Brands that survey their audiences to uncover their needs and pain points, analyze customer data to pinpoint buying behavior among segments and oversee focus groups to assess purchasing drivers are all conducting primary research. 

An organization conducting secondary research uses data compiled and published by others, often for a different purpose than what it is using it for. Government statistics, industry reports, company filings and media coverage are common sources of secondary research. 

Benefits and Drawbacks of Secondary Research  

Each type of research comes with its own advantages and limitations. Primary research is tailored to an organization’s specific needs and audience, delivering highly customized insights. The trade-off is that designing, fielding and analyzing this bespoke research can require substantial time, labor and budget. 

Secondary research, by contrast, draws on existing data, making it faster and often more cost-effective. However, because the information wasn’t created with a particular brand or audience in mind, its relevance can vary. A study on parents’ purchasing behavior, for instance, may not reflect the attitudes or habits of the exact parent segments a brand aims to reach. And while secondary data is readily accessible, identifying the right sources – and analyzing and interpreting them effectively – still requires careful strategy, time and expertise.

When to Use Primary vs. Secondary Research

Before undertaking primary research, brands should consider conducting secondary research to understand the fundamentals of the market: its size, the competitive landscape and any white spaces or overlaps, the audience and historic and current trends. From this data, an organization can form relevant hypotheses. For example, after reviewing demographic data, trade publications and other existing sources, a home furnishings brand might hypothesize that a lower-priced bedding and decor line tailored to dorm living could meet an unmet need and attract a new customer segment.  

Primary research then tests and refines that hypothesis. The brand could survey potential customers, conduct focus groups, or employ other qualitative and quantitative methods to confirm whether the opportunity is viable or whether the audience is too limited to justify expansion. These primary findings may also highlight ways to fine-tune the concept or uncover additional unmet needs worth exploring. 

In essence, secondary research lays the foundation and focuses the direction of inquiry, while primary research digs deeper – validating assumptions, revealing audience-specific insights and guiding more confident decision-making.

When Is Secondary Research Especially Valuable? 

There are certain instances where the business intelligence yielded by secondary research can be especially beneficial. 

• Competitive landscape analysis. It’s all but impossible to identify an organization’s competitors, along with their strengths and weaknesses, without consulting secondary research data such as industry reports, company websites, filings and media coverage.  

• Market entry and expansion. How large are various markets? How mature? Who are the major players? What needs or audiences are underserved? What are potential barriers to entry? Secondary research via government reports, industry associations, market research studies and company filings can narrow down markets and initiatives worthy of further investigation. 

• Surfacing trends. Because “past is prologue,” analyzing existing market research reports, government data, media coverage and even patent filings can provide valuable context and reveal emerging market trends. Primary research can then help determine whether these potential trends are relevant to a brand and its audience. 

• Crisis and reputation management. Secondary research into past crises, crisis strategies and media narratives of competitors can help organizations establish and refine their own protocols so they can act quickly and effectively in high-stakes situations. 

How Is AI Transforming Secondary Research?

Conducting primary and secondary research has traditionally been both labor-intensive and time-consuming. Integrating AI into the process doesn’t replace the human element, but it can significantly accelerate the work while deepening the analysis. 

• Automation streamlines many of the most tedious aspects of secondary research. Large language models (LLMs) and other natural language processing (NLP) tools can scan vast amounts of unstructured content – such as articles, transcripts and reports – and extract relevant information far faster than a person can. These tools also help researchers synthesize, summarize and compare insights across multiple sources. Human judgment remains essential for defining search parameters and validating the accuracy of AI-generated output, but AI meaningfully amplifies researchers’ capabilities. 

• Trend detection leverages one of AI’s core strengths: spotting patterns, themes and relationships that humans might easily miss. AI can rapidly surface emerging trends, as well as contradictions or anomalies that become visible only when analyzing data across many sources.

• Insight generation builds on automated extraction and trend detection to provide richer context for decision-makers. Beyond offering predictive insights from secondary research, AI can also generate prescriptive recommendations, helping organizations determine what actions to take next. 

Why Secondary Research Matters for Organizations 

When a consumer technology brand sought to differentiate itself as a global leader in AI, it turned to Material for an in-depth map of the marketplace and recommendations on how to pull ahead of the competition. We leveraged proprietary and best-in-class AI tools to analyze public records, websites, marketing copy, articles and other data. From this competitive research, we developed a detailed landscape of the category, its major players and their positioning and value propositions — including how they were repositioning their AI offerings in an attempt to encroach on our client’s market share. This research surfaced valuable insights and paved the way for a differentiated, customer-centric vision for its future AI initiatives. 

Without this foundational research, the client would have lacked a robust, 360-degree view of the marketplace and the context in which to consider its future moves. And without AI powering the research, the process would have been less efficient and more time-consuming, preventing the client from making decisions at the speed its industry demands. 

AI in itself can’t yield transformative secondary research; human expertise and judgement is essential to ensuring the right questions are asked, the right answers are delivered and the right frameworks are developed. When these two elements come together, organizations enjoy maximum efficiency and strategic value. 

At Material, we align AI-powered tools with human expertise to deliver actionable consumer intelligence for the world’s leading brands. Contact us today to learn what our competitive intelligence services can do for your organization.