By Kentley Insights | Industry Intelligence & Benchmarking Research
Every few years, the same conversation resurfaces in boardrooms and finance meetings: is this market research spend actually worth it? The question is reasonable. Research budgets aren’t cheap, and the connection between a report and a measurable business outcome isn’t always obvious.
But the more useful question is usually the inverse one. What does it cost when you make strategic decisions without reliable data?
The answer tends to be considerably more than the cost of the research. Pricing decisions that erode margins, expansion moves into saturated markets, operational structures that quietly underperform industry peers, and forecasts built on stale inputs. These aren’t hypothetical risks. They happen regularly, and they’re rarely traced back to the intelligence gap that caused them.
In 2026, the stakes are higher than they’ve been in a while. Inflation has restructured cost bases across industries. AI is changing labor economics in ways that are still working themselves out. Demand patterns are shifting unevenly by sector and geography. In that kind of environment, running a strategy on guesswork or outdated secondary data carries real financial risk.
This article looks at the practical ROI case for market research: where it creates measurable value, what poor-quality intelligence actually costs organizations, and how the most analytically sophisticated companies are using benchmarking and forecasting data in their day-to-day planning.
Why Businesses Underestimate the Cost of Poor Market Intelligence
The costs of weak market intelligence are mostly invisible. They don’t show up as a line item. They show up as decisions that looked reasonable at the time.
Take pricing. Companies that set prices without understanding how competitors are positioned, what the industry’s margin structure looks like, or how input cost pressures have shifted across the sector are essentially working from incomplete information. Sometimes they leave money on the table. Sometimes they price themselves out of deals. The underlying issue, in both cases, is the same.
Geographic expansion is another area where the costs are high and often irreversible. A company evaluating a new market without understanding sector concentration — how fragmented or consolidated the competitive landscape is in that region — may be committing capital to a market where the unit economics don’t work. Discovering that after the fact is expensive.
Forecasting errors tend to be the most far-reaching. Revenue models built on inaccurate or outdated market data produce cascading errors downstream: capital allocation, hiring plans, supplier commitments, and inventory levels. A structurally flawed forecast, based on weak inputs, can take quarters to unwind.
And then there’s the operational piece, which is probably the least-discussed category. Without visibility into industry benchmarking metrics, management teams have no external reference point for their own performance. They optimize against their own history. That’s a reasonable approach in stable conditions, but it means they won’t notice if the industry has shifted around them.
The Real ROI of Market Research
The ROI of market intelligence is not always a direct revenue figure. More often, it’s risk-adjusted value: the difference between a good decision and a costly one.
That framing is harder to model, which is partly why research budgets get cut before other things. But the underlying logic is solid. Better inputs to decisions produce better outcomes. That’s especially true in complex markets where the available information is inconsistent, and the cost of being wrong is high.
Operational optimization
When a management team can see where their operating cost breakdown sits relative to industry peers, they have a concrete starting point. If the sector average for payroll as a percentage of revenue is 38% and the company is running at 47%, that gap has a name and a dollar amount. It can be investigated. That’s not abstract analysis. That’s an efficiency target.
Pricing strategy
Industry revenue and margin data let companies pressure-test their own pricing assumptions against what the sector actually looks like. If the average EBITDA margin in a sector runs at 14% and a business is producing 9%, the question is whether that gap is a pricing problem, a cost problem, or a product mix problem. The external data point doesn’t answer that question on its own, but it raises it. That’s the starting point for a useful internal conversation.
Investment targeting
Investors and operators evaluating industry entry need more than general sector awareness. Identifying the most profitable industries with an acceptable risk profile requires structured data on growth trajectory, margin characteristics, and competitive concentration. General awareness of a sector is not the same as understanding its financial structure.
Strategic planning
Planning cycles that incorporate industry revenue projections, demand drivers, and labor cost forecasts produce plans that hold up better under scrutiny. When assumptions can be grounded in documented external data rather than internal estimates alone, the logic is more defensible. That matters internally, and it matters when presenting to a board or investor group.
What High-Performing Companies Actually Use Market Research For
The most analytically sophisticated companies aren’t using market research to confirm what they already think. They’re using it to stress-test assumptions, find gaps, and avoid the kind of expensive trial and error that comes from operating on incomplete information.
M&A due diligence
Before acquiring a business in an unfamiliar sector, buyers want to understand the industry’s margin norms, typical capital structures, and cost benchmarks. Indexed balance sheets and operating cost breakdowns give acquirers a reference point for evaluating the target. Is this company performing in line with its peers? Are there operational inefficiencies the deal price doesn’t account for? These questions are easier to answer with sector-level benchmarks in hand.
Operational benchmarking
Span of control analysis has become a board-level conversation in a lot of organizations. Companies want to know how their ratio of managers to employees compares to industry norms. They want to understand their functional role breakdown, specifically whether the distribution of headcount across management, operational, and administrative functions is structurally sound.
In the management consulting industry, which generates around $403.5 billion in annual revenue across roughly 164,006 companies, employee productivity benchmarks shift considerably by firm size and service focus. A consulting firm benchmarking its revenue per employee against an industry-wide average without segmenting by firm type is probably drawing the wrong conclusions.
Geographic expansion
National-level data often masks significant regional variance. MSA-level segmentation across 384 metropolitan areas lets operators identify where demand is concentrated, where competition is thinnest, and where a new location or service expansion is most likely to work. That level of geographic specificity isn’t available in most generic market summaries.
Revenue mix and product line analysis
Revenue index data and product line penetration benchmarks help operators understand how their revenue mix compares to industry norms. Heavy concentration in a single service category might be a competitive strength or a structural vulnerability, depending on how the sector typically distributes revenue across lines. Without external data, it’s hard to know which.
In the strategy management consulting segment, a $217 billion market across 78,036 companies, revenue mix and concentration patterns vary substantially between boutique firms and large generalists. Comparing against sector-wide averages without that segmentation produces misleading conclusions more often than not.
Investor reporting
Institutional investors are better at benchmarking than they used to be. Management teams that can contextualize their performance against external industry data present a more credible story. Here is where our margins sit relative to sector medians. Here is how our revenue per employee compares to the benchmark. That kind of framing requires the external data point. It’s difficult to replicate with internal metrics alone.
The Cost of Generic or Low-Quality Reports
Not all market research is useful. That seems obvious, but it’s worth stating clearly, because the proliferation of AI-generated content has dramatically widened the quality gap in research products over the past two years.
The specific risk with generic reports isn’t that they provide no information. It’s that they provide the appearance of due diligence without the substance. A decision-maker who reads a low-quality report and feels informed is in a worse position than one who knows their data is limited. False confidence is a real problem.
The most common quality failures look like this:
- Outdated datasets: Industry data with a two or three-year lag is often structurally wrong. Sector margins, competitive concentration, and cost structures can shift substantially in that window.
- Vague methodology: Reports that don’t disclose sourcing, modeling assumptions, or confidence intervals provide no basis for evaluating reliability.
- No benchmarking framework: Raw statistics without peer comparisons or industry baselines can’t be operationalized. A revenue figure in isolation doesn’t tell you anything about whether a business is performing well or poorly.
- Weak segmentation: Aggregate statistics that don’t break down by company size, geography, or business model often mask more than they reveal.
- No forecasting depth: Point-in-time data doesn’t support planning. Directional forecasts, even imperfect ones, are necessary for defensible strategic plans.
Knowing how to evaluate market research reports before purchasing is itself a useful skill. The relevant questions: What is the data source? How current is it? Does the report include benchmarking frameworks or just descriptive statistics? Is the forecasting methodology documented and transparent? A report that answers those questions clearly is doing real analytical work. One that doesn’t is probably generating more noise than signal.
Metrics That Create Strategic Value
The metrics that matter most aren’t necessarily complex. They’re the ones that give decision-makers an external reference point for internal performance. They answer the question: how are we actually doing, relative to the industry?
Kentley Insights structures its research around six core benchmark categories. Each one maps to a different set of business decisions.
1. Revenue Growth
CAGR and year-over-year revenue growth benchmarks give operators and investors a read on sector momentum. The IT Systems Design industry generates $700.2 billion annually across 121,407 companies, with average sales per location around $4.8 million. Understanding where a given business sits relative to those figures is a reasonable starting point for positioning conversations.
2. Profitability
EBITDA margins, operating margins, and profitability benchmarks let companies evaluate whether their cost structure is competitively viable. The management consulting industry runs at a roughly 11.2% average profit margin. A firm sitting well below that needs to understand whether the gap is structural or operational before it can address it.
For operators evaluating the fastest-growing service industries, the profitability context matters a lot. High-growth sectors don’t always produce high margins, and knowing the margin profile before entry changes the analysis.
3. Product Line Breakdown
Product line penetration data shows how revenue is typically distributed across service and product categories within an industry. It’s useful for evaluating your own revenue mix and for assessing concentration risk in a potential acquisition or investment target.
4. Employee Productivity
Revenue per employee and payroll per employee are among the most operationally actionable metrics available. When a company’s revenue per employee is well below the industry median, the natural follow-up questions are: Is this a pricing problem? A utilization problem? A workforce structure problem? The benchmark doesn’t answer that, but it asks the question precisely. That’s the value.
For service businesses, like those in janitorial and facility services, labor is the dominant cost driver. Productivity benchmarks in those sectors are especially consequential.
5. Functional Role Breakdown
How headcount is distributed across management, operational, administrative, and support functions tells a story about organizational efficiency. A company that’s significantly management-heavy relative to peers is carrying overhead that may not be generating proportional output. Span of control analysis is a direct extension of this: how many direct reports each manager oversees, relative to industry norms.
6. Operating Cost Breakdown
Understanding how industry peers allocate costs across labor, facilities, materials, and overhead gives management teams a template for evaluating their own cost structure. It also supports scenario planning. If labor costs increase 8% across the sector, what does that do to average margins? Companies that have already mapped their cost structure against industry benchmarks can answer that question quickly.
Accounts receivable benchmarks are part of this picture. An industry benchmark of 60.6 days in AR becomes a reference point for working capital planning. A company running at 85 days, without realizing the sector standard is 60, is carrying a liquidity cost it may not have intentionally accepted.
Why Benchmarking and Forecasting Matter More in Volatile Markets
The value of market intelligence tends to go up when conditions are unstable. This is somewhat counterintuitive, since uncertain markets produce uncertain data. But benchmarking serves a different function under volatility. It helps companies determine whether what they’re experiencing is sector-wide or specific to them. That distinction shapes everything about how they respond.
In stable conditions, a company can largely rely on its own historical performance as a guide. When structural forces are disrupting those trends, historical patterns lose predictive value. External benchmarking fills part of that gap.
Labor costs are a good example. Payroll as a share of revenue has risen materially across most service industries over the past few years. A company benchmarking its payroll intensity against industry data can quickly see whether its experience is typical or an outlier. If it’s sector-wide, the response is about pricing and productivity. If it’s specific to them, the response is about internal cost controls. That’s a meaningful difference in strategy, and the benchmark is what makes the distinction possible.
AI disruption is creating a different kind of divergence. In sectors like management consulting and IT services, firms that have integrated AI-assisted workflows are reporting different productivity profiles than those that haven’t. Employee productivity benchmarks are starting to reflect that split. Companies that aren’t tracking this through external data may not notice the gap widening until it’s already significant.
Forecasting in this context becomes a planning discipline. The management consulting industry’s 8.7% five-year annual growth rate is a useful planning input, but the distribution of that growth across firm types, geographies, and service lines is what drives real allocation decisions. For businesses in adjacent sectors, like food manufacturing, evaluating service expansion, industry-level forecasting with 30-plus underlying metrics, and coverage across 195 countries provides the kind of dimensional depth that supports real planning rather than directional guesses.
Econometric models that integrate macroeconomic drivers with sector-specific variables give planners something more useful than a point estimate: a structured view of how the forecast changes under different conditions. That scenario-planning function is particularly valuable when the broader macro environment is uncertain.
Market Research as Strategic Infrastructure
Framing market research as a cost to be minimized reflects a misunderstanding of what it actually produces. The most direct return isn’t revenue. It’s decision quality. And decision quality compounds: better inputs to a pricing strategy, a workforce model, an expansion decision, or a capital allocation framework produce better outcomes across all of them.
The ROI argument for market intelligence is ultimately risk-adjusted. The cost of a well-sourced, benchmarking-oriented industry report is fixed and known. The cost of the decisions it informs, or the mistakes it prevents, is neither fixed nor small.
High-performing organizations have largely figured this out. They treat industry benchmarking data, forecasting models, and operational comparables as regular inputs to planning cycles, not one-time research exercises. The question isn’t whether to use market intelligence. It’s about whether the data they’re using is current, granular, and well-structured enough to actually inform decisions.
For companies evaluating their research and planning infrastructure in 2026, that’s the right standard. Not more data. Better data, organized around the decisions that matter: where to compete, how to price, how to staff, where to invest, and what to expect from the markets they operate in.