Why GLG's Vast Network Often Leads to Irrelevant Subject Matter Experts

When conducting primary research, the quality of your insights depends entirely on the relevance of your experts. Traditional expert networks like GLG (Gerson Lehrman Group) pride themselves on their massive consultant databases—often advertising networks of over 1 million experts. But size isn't everything in research. In fact, it can be the very thing that stands between you and the specific insights you need.

The Pool-First Problem

GLG and similar traditional expert networks operate on what we might call a "pool-first" model. Their fundamental approach is:

1. Build the largest possible database of consultants
2. Filter that database when a client has a need
3. Present candidates who are "close enough" to the target profile

This model works reasonably well for broad or common research targets. Need to speak with "healthcare administrators"? There are thousands in the pool. But as your criteria become more specific, the limitations quickly reveal themselves.

When Vast Networks Create Vast Irrelevance

The Needle in the Haystack Effect

Large networks inevitably create a signal-to-noise problem. According to research from the Qualitative Research Consultants Association, research projects with highly specific criteria can experience "expert match rates" below 20% when working with large panel providers. This means that for every relevant expert you find, you may review four or more irrelevant candidates.

The Outdated Profile Challenge

With millions of profiles to maintain, database accuracy becomes nearly impossible. A 2022 survey of research professionals found that 64% had experienced situations where an expert's actual experience didn't match their profile description.

Jane Martinez, Director of Product Research at a leading SaaS company, shares: "We needed to interview specific technical decision-makers who had evaluated both our product and a competitor within the last six months. GLG sent us 12 candidates, but after screening, only two actually met our criteria. The rest had outdated information or were only tangentially related to our target."

The Hidden Costs of Irrelevance

Time Drain of Screening

When you receive a list of potential experts who don't precisely match your needs, the burden of detailed screening falls on you. According to data from the Market Research Society, research teams spend an average of 7.4 hours per project screening and qualifying experts when using traditional expert networks.

Budget Waste

With traditional firms charging $1,000+ per hour for expert calls, investing in conversations with the wrong experts is costly. One study estimated that companies waste approximately 22% of their expert network budget on irrelevant or low-value conversations.

Delayed Insights

Perhaps most damaging is the time-to-insight delay. When markets shift rapidly, waiting weeks to find the right experts puts you behind competitors who can move faster.

The Target-First Alternative

The fundamental flaw in the GLG approach isn't the size of their network—it's starting with the pool rather than the target.

A more effective approach inverts this model:

1. Define exactly who you need to speak with (role, industry, experience qualifiers)
2. Go directly to where those specific people are
3. Recruit precisely rather than filtering broadly

This target-first approach requires different technology and methodology than traditional expert networks provide.

Building Your Own Network vs. Renting Access

Traditional expert networks operate on a rental model—they own the relationships and rent them to you at a premium. This creates misaligned incentives where matching you with someone already in their database (regardless of fit) becomes the priority.

Increasingly, research teams are finding greater success by:

• Building their own expert networks through direct outreach
• Maintaining ownership of the connections they create
• Using technology to scale outreach rather than relying on pre-built databases

The Cost of the Middle Layer

When you work with GLG, you're not just paying for expert time—you're paying for:

• The broker layer (account managers, recruiters)
• Network maintenance costs
• Significant markups (often 100%+)
• Complex compliance systems

According to industry analysis, approximately 65-70% of what clients pay goes to these operational costs rather than to the experts themselves.

Real-World Comparison: Pool-First vs. Target-First

A financial services firm recently conducted parallel research projects:

• Project A used GLG to find 10 experts on a specialized fintech implementation
• Project B used a target-first, direct outreach approach for the same profile

The results were telling:

• GLG delivered their experts in 3.2 weeks and required screening 28 candidates
• The direct approach delivered in 1.8 weeks and required screening only 13 candidates
• The direct approach cost 42% less per completed interview

When Does GLG's Network Make Sense?

To be fair, GLG's model works well in certain scenarios:

• When your target is common and easily found in existing networks
• When compliance and legal separation are the highest priority
• When you need minimal involvement in the recruiting process

However, for teams with specific research needs who value precision, speed, and cost-efficiency, the pool-first model shows significant limitations.

Moving Beyond the Pool

Forward-thinking research teams are now adopting technologies that help them:

• Turn their existing professional networks into recruitment engines
• Conduct precise, targeted outreach at scale
• Own the relationships they build through research
• Cut out the middle layer and its associated costs

This shift fundamentally changes research from a rental expense to a network-building asset.

Conclusion: From Pool-First to Purpose-Built

GLG's vast network represents the old game of primary research—renting access from middlemen who own the supply. While impressive in size, this approach inevitably leads to expert irrelevance when precision matters.

The new game is about ownership—owning your research network, building direct relationships, and using technology to reach exactly who you need. Rather than filtering down from millions, successful research now starts with exact targets and builds up.

For your next research project, consider: Do you want to rent access to a pool where relevance is a constant challenge? Or would you rather build your own network asset while finding precisely who you need?