The pest control industry experienced steady top-line growth between 2023 and 2025. However, growth alone did not guarantee stronger profitability. Labor costs, indirect overhead, vehicle expenses, and growth-related investments have shifted differently across operators and company sizes. As a result, profitability increasingly depends not just on growth, but on how effectively operators manage their cost structure as they scale.

This study examines where profitability pressure is building within the pest control industry and how those pressures vary by revenue tier and operating model. By comparing performance across segmented peer groups, the benchmarks in this report help operators see where their business stands relative to industry norms and identify the operational drivers most responsible for Margin pressure.

Rather than relying on simple averages, the analysis focuses on performance distributions, highlighting the gap between median performance and best-quartile execution. Segmenting operators by revenue tier and region enables more meaningful comparisons that reflect differences in scale, labor markets, and operating environments. For example, operators generating less than $1M in revenue often face fundamentally different operational constraints than companies operating above $3M in annual revenue, where staffing structures, overhead allocation, and growth investments tend to evolve significantly.

The objective is not simply to measure performance, but to clarify the structural factors separating disciplined operators from those experiencing Margin compression heading into 2026. The analysis also highlights how operators achieving both strong growth and strong Margins manage these pressures differently, offering practical insights for businesses seeking to improve profitability without sacrificing growth.

FRAXN's role is to translate financial data into clear, consistent reporting structures that reflect how the business has actually performed. This includes analyzing what the levers behind those numbers are, identifying where performance is stable or volatile, and clarifying what the data supports today. The objective is not to prescribe actions, but to make the financials transparent and defensible so operators can make informed decisions grounded in reality.

Unlike traditional industry surveys that rely on blended averages, this study emphasizes structural dispersion, meaningful segmentation, and multi-year performance trends.

This study is built from operator-level financial data provided by participating pest control companies. To ensure comparability across businesses with different bookkeeping practices, financial entries were standardized into a consistent chart-of-accounts (COA) structure, enabling true apples-to-apples benchmarking.

Because accounting treatment varies across operators, particularly for subcontracted labor, fleet costs, and employee-related expenses, accounts are mapped based on economic function rather than company-specific chart-of-accounts structures. For example, indirect payroll is separated to exclude compensation attributable to Sales and Marketing activities when those costs are recorded within broader payroll accounts. This ensures benchmarks reflect operational reality rather than accounting classification differences.

Two cohorts are referenced throughout this study:

To preserve true comparability and avoid distortion from partial-year ramp-up effects, FRAXN did not annualize their 2024 financial records to account for missing months. Where full-year comparability is required for 2024–2025 analysis, the study uses a subset of operators with complete operating periods, referred to as the comparable cohort (n = 116). The full benchmark cohort (n = 125) is retained for structural benchmarking analyses. Unless otherwise noted, all structural benchmarking results reference Cohort B (n = 125).

Each operator's financials have been translated into FRAXN's uniform chart of accounts providing a standardized benchmarking framework. A full chart-of-accounts (COA) mapping used in this study is provided in the Appendix for reference.

The guiding principle is straightforward:

Unless otherwise noted, benchmarks are expressed as a percentage of revenue for the applicable year of analysis.

Year-over-year changes in cost structure are measured as percentage-point (pp) differences relative to revenue, not as dollar growth rates. For example, if Cost of Service increases from 45% of revenue in 2024 to 48% in 2025, the change is reported as a +3 percentage-point increase, meaning $0.48 of every revenue dollar is now spent performing the work, compared with $0.45 the prior year.

Comparative revenue tiers are based on 2024 baseline revenue and remain fixed across the 2023–2024 and 2024–2025 comparisons to avoid artificial tier migration.

All published benchmarks use company-weighted distribution statistics:

Company-weighted means each operator contributes equally to the distribution, regardless of company size or revenue scale.

Because benchmarks rely on medians and quartiles rather than arithmetic averages, extreme values have reduced influence on the published statistics. Observations are retained unless a clear data integrity issue is identified.

Distribution statistics are used instead of revenue-weighted averages to preserve comparability across operators of different sizes and operating scales.

Profitability in this report is evaluated through a sequential operating structure that reflects how revenue converts into operating profit. The benchmarks follow a simple progression:

Revenue → Cost of Service → Gross Margin →
→ Operating Expenses → EBITDA Margin %

This structure isolates where Margin pressure originates, whether within service delivery or within broader operating overhead.

The following metrics capture the primary performance outcomes of pest control operators, including Revenue Growth, Gross Margin, and operating profitability. These benchmarks reflect how effectively operators convert revenue into profit across different cost structures and operating models.

(Current Year Revenue − Prior Year Revenue) ÷ Prior Year Revenue

Measures year-over-year change in total revenue relative to the prior-year baseline. Revenue Growth reflects the pace at which operators expand through customer acquisition, pricing adjustments, and service volume.

(Revenue − Cost of Service) ÷ Revenue

Measures the portion of revenue remaining after direct service delivery costs are deducted.

Gross Margin provides an indicator of field-level operating efficiency before administrative overhead and growth investments are considered. It helps determine whether Margin pressure originates within service delivery or elsewhere in the cost structure.

(Revenue − Cost of Service − Operating Expenses) ÷ Revenue

Measures overall operating profitability after both service delivery costs and operating overhead are deducted.

For benchmarking consistency, accounts related to financing activities, income taxes, and other non-operating items are excluded from the analysis.

Because depreciation, amortization, and certain non-operating accounts are not consistently reported across operators, EBITDA in this study should be interpreted as an operating profitability benchmark rather than a strict accounting EBITDA measure.

This standardized definition provides a consistent basis for comparison across operators regardless of capital structure, ownership, or accounting treatment.

The following metrics decompose the operating cost structure underlying Gross Margin and operating profitability. Evaluating each component individually reveals the cost drivers behind the performance benchmarks.

Cost of Service ÷ Revenue

Cost of Service includes only direct costs required to deliver pest control services, including:

Note: Where operators record vehicle costs across multiple accounts, expenses are mapped based on their economic function to ensure costs associated with field service delivery are consistently included in Cost of Service.

Operating Expenses ÷ Revenue

Operating Expenses represent all indirect costs required to support the business beyond direct service delivery.

OPEX is not benchmarked as a single aggregate percentage in this study. Instead, analysis is performed at the component level to improve diagnostic precision and allow operators to isolate specific cost drivers rather than reacting to blended overhead totals.

Primary OPEX components include:

Note: Where payroll accounts combine multiple functions, compensation is reclassified based on job function to ensure administrative labor is separated from field labor and Sales & Marketing activities.

Note: Where sales-related payroll or marketing expenses are embedded within broader payroll or administrative accounts, costs are mapped to Sales & Marketing based on their underlying economic function.

Note on Interpretation: All cost metrics are expressed as a percentage of revenue at the company level and summarized using the median across operators. Because each component is calculated and aggregated independently, the sum of median cost components will not equal total Cost of Service or Operating Expenses and will not reconcile to 100%.

This approach is intentional and provides a more accurate view of typical cost allocation across operators, avoiding distortion from outliers or differences in company size.

Benchmarks are segmented to avoid blended averages that may obscure scale effects.

After careful consideration and participant analysis, FRAXN categorized participants into three revenue segments based on their annualized revenue.

For a more detailed overview of participant distribution by Revenue Tiers, please refer to Chapter 4.2 Revenue Tier Distribution.

Operators should benchmark primarily against their revenue tier and then reference regional comparisons where applicable.

The regions included in this report are:

Mid-Atlantic North Central North East Pacific North West
South Central South East South West

For a more detailed overview of participant distribution by Region, including state-level representation, please refer to Chapter 4.3 Geographic Representation.

Cohort A (2023–2025 trend cohort) has limited participation in certain regions. No North Central operators currently meet full three-year data requirements. As a result, three-year trend analysis for that region is not presented.

In regions with smaller sample sizes, benchmark results should be interpreted with appropriate caution, as thinner cohorts may be more sensitive to the performance of a small number of operators. As participation expands over time, regional benchmarks are expected to become more stable and representative.

Within the <$1M revenue tier, percentage changes in growth and Margin metrics may appear amplified due to smaller revenue bases. These observations are retained in the dataset. However, the use of company-weighted medians and quartiles reduces the influence of extreme values. Results should be interpreted with appropriate consideration of scale sensitivity.

Financial data used in this study is derived from a combination of operator-submitted bookkeeping records and FRAXN-managed accounting records. While FRAXN standardizes accounts into a consistent benchmarking framework, underlying transaction classification generally follows each participating company's internal reporting practices.

FRAXN performs structural mapping to a standardized chart-of-accounts to ensure comparability across operators. However, no forensic reclassification, audit procedures, or reconstruction of financial statements were performed beyond this standardization process.

As a result, the benchmarks presented in this report reflect reported operating performance rather than audited or restated financial results.

Negative totals within Cost of Service (COS) or Operating Expense (OPEX) categories may occur due to accounting reversals, reclassifications, or one-time adjustments. Immaterial negative amounts are retained. Where negative category totals exceed a materiality threshold (e.g., 1% of revenue), they are capped at zero for benchmarking ratio calculations to preserve structural comparability across operators.

In certain visualizations, particularly scatter plots, extreme outlier observations may be omitted from the chart display to improve readability and highlight the primary distribution of results. These extreme cases represent a very small number of observations and do not materially affect the underlying benchmark calculations.

Unless otherwise noted, distribution statistics and benchmark values are calculated using the full dataset, with observations retained unless a clear data integrity issue is identified.

Some operators utilize door-to-door (D2D) or other high-investment sales models, which structurally increase sales payroll and marketing expense relative to peers. Benchmark comparisons should account for differences in go-to-market strategy, customer acquisition intensity, and service mix when interpreting cost structure and Margin outcomes. Results should be interpreted with appropriate consideration of this business model differences.

Several practical implications emerge from the benchmark analysis:

The benchmark dataset (Cohort B) includes 125 operators with complete financial data for 2024–2025, forming the primary benchmarking cohort used throughout the study. Nine operators began reporting financial data mid-year in 2024. While these operators are included in the benchmark cohort for structural cost comparisons, they are excluded from 2024–2025 comparative analysis (n=116) to ensure full-year comparability. Revenue figures were not annualized to avoid distortion from ramp-up periods.

A subset of 60 operators with complete financial data from 2023–2025 (Cohort A) forms the trend cohort, which is used for multi-year growth and volatility analysis.

Revenue growth, or top-line growth, is the starting point for evaluating operating performance and overall financial results. This chapter examines how operators expanded revenue from 2023 through 2025, first through a multi-year stability lens (Cohort A, n = 60), and then through the 2024–2025 comparable benchmark cohort (Cohort B, n = 116) to provide a clearer view of recent performance.

The objective is twofold: to assess growth consistency over time and to evaluate how current growth performance is distributed across comparable peers. All statistics are presented as company-weighted distributions.

To assess growth consistency and volatility, a subset of 60 operators with complete financial data from 2023 through 2025 was analyzed separately. Multi-year trend analysis uses this cohort only and should not be compared directly to the 125-company 2024–2025 benchmark dataset. Revenue tiers for multi-year analysis are fixed using 2024 baseline revenue to prevent tier shifting across years.

This snapshot provides a benchmarking view of revenue growth stability across operators over the past two periods. While the interquartile range (Q3–Q1) indicates a relatively concentrated band of growth outcomes within the sample, the median growth rate declined from 20.12% in 2023–2024 to 13.25% in 2024–2025, a decrease of 6.87 percentage points (approximately 34%). This indicates that median revenue growth slowed materially between the two periods, reflecting a broad moderation in industry growth momentum rather than isolated underperformance.

The elevated growth environment in 2023–2024 also created conditions where operational inefficiencies could be masked or overlooked, as strong top-line expansion offset underlying cost pressures. As growth normalizes in 2024–2025, these inefficiencies become more visible, placing greater emphasis on cost structures and operating discipline.

As growth rates normalize, operators can no longer rely on top-line expansion alone to sustain profitability. Businesses built around aggressive growth capital deployment may begin to experience margin pressure as revenue expansion slows while cost structures remain elevated.

Operators should evaluate whether their current cost structure, staffing levels, and growth investments remain appropriate under a slower growth environment, and identify where operational efficiency or pricing adjustments may be required to maintain profitability.

The growth band distribution provides a benchmark view of how revenue expansion is distributed across operators over the two-year period.

Most companies have steady growth band (0–30%), indicating that moderate, consistent expansion represents the typical growth pattern in the industry.

However, the data also shows a noticeable shift between periods, with the share of operators experiencing significant contraction increasing from 1.67% in 2023–2024 to 8.33% in 2024–2025, while the proportion of companies achieving large or hyper growth declined.

These patterns signal that while growth remains common across the cohort, outcomes are becoming more uneven and moderating in recent years, reinforcing the need to benchmark performance and establish clear guardrails to peers, rather than relying solely on top-line growth.

While industry expansion remains structurally intact, growth rates appear to be moderating. Approximately 68% of operators generated positive Revenue Growth in both periods, indicating sustained expansion across much of the sample. However, nearly one-third of companies experienced at least one contraction year. This pattern points out that growth stability is not universal across operators. Even within an expanding industry, individual operators may face volatility driven by local market conditions, customer churn, pricing pressure, or operational execution.

Operators should evaluate whether their growth model is resilient across multiple periods by examining customer retention, route density, and sales and marketing effectiveness to reduce the risk of intermittent revenue contraction.

This section shifts from the multi-year cohort to the broader benchmark cohort to evaluate the most recent year-over-year performance from 2024 to 2025.

While the benchmark dataset includes 125 operators, nine companies began operations midway through 2024 and therefore do not reflect a full baseline year. To maintain comparability, FRAXN did not annualize partial-year revenue for these operators. As a result, the growth analysis for 2024–2025 reflects 116 operators with complete full-year data.

The findings across the benchmark group remain consistent. Revenue growth remained broadly positive, with approximately 83% of operators generating non-negative growth between 2024 and 2025 (Growing, Large Growth, and Hyper Growth), reinforcing the recurring revenue model that characterizes pest control services. The majority of operators (58.6%) fell within the moderate growth band of 0% to 30%, indicating that steady, incremental expansion remains the most common outcome across the industry.

However, performance outcomes are not uniform. While most operators grew, approximately 17.3% experienced some level of revenue contraction, including 5.2% with more pronounced declines. This variation highlights differences in execution, pricing power, service mix, and growth investment intensity across operators. Overall, the distribution suggests that growth is widespread but not evenly experienced, emphasizing that revenue expansion alone does not fully capture performance differences across the cohort.

Revenue expansion remains common across the industry, with the typical operator growing at 15.4% and most falling within a 5.7% to 28.9% range. This relatively wide middle band highlights that while growth is widespread, outcomes still vary meaningfully across operators. At the outer ends, a small number of companies experienced sharp contraction or outsized expansion, indicating that while extreme outcomes exist, they are not representative of the typical operator.

For this reason, growth should be viewed as a baseline rather than a signal of exceptional performance. Operators should assess performance relative to peers within their revenue tier, rather than relying on broad industry averages when identifying improvement opportunities.

When segmented by revenue tier, growth outcomes diverge meaningfully across operators, indicating that scale influences both growth potential and volatility. Three structural distribution patterns emerge across the revenue tiers.

Smaller operators exhibit the fastest growth and the widest performance dispersion. Median growth reaches 20.6%, with upper-quartile outcomes approaching 40%, and the broadest interquartile range of 26.8 pp across tiers, highlighting the variability within this segment.

A meaningful portion of operators achieve very high growth rates, reflecting aggressive expansion from a smaller revenue baseline. At the same time, outcomes remain uneven as operators experiment with pricing strategies, marketing channels, and service mix.

This tier largely represents an expansion phase, where growth opportunities are abundant but operational structures are still developing.

Growth moderates materially in the $1–3M segment. Median revenue growth declines to 9.9%, the slowest median outcome across all revenue tiers. While the typical operator performance in this segment continues to expand, the pace of growth becomes noticeably more constrained than in both smaller and larger peer groups. This pattern is consistent with the report's broader finding that the $1–3M range functions as a structural transition zone, where increasing organizational complexity begins to weigh on momentum.

At this stage, operators are often building the infrastructure required for the next phase of scale. Management layers begin to form, technician teams expand, and investment in sales and marketing becomes more formalized. These additions are necessary to support future growth, but they also introduce friction as costs and coordination requirements rise faster than the benefits of scale are fully realized.

In practical terms, the $1–3M tier represents an inflection point. Early-stage growth remains present, but it is no longer driven by lean, founder-led expansion alone. Operators that navigate this transition successfully are typically those that strengthen operating discipline while scaling, allowing systems, staffing, and revenue growth to mature in step rather than fall out of balance.

Larger operators demonstrate more stabilized growth outcomes, with median revenue growth rebounding to a modest 15.8% and dispersion narrowing relative to smaller peers. The interquartile range compresses to 21.5 percentage points, the lowest across all tiers, indicating more consistent performance across operators.

At this scale, operators benefit from more developed operational systems, greater route density, and more established pricing structures. These advantages support steadier performance, even as year-over-year comparisons become more demanding.

While explosive growth becomes less common, expansion in this tier increasingly reflects operational discipline and execution rather than rapid scaling from a smaller base.

Assessing the 2024–2025 growth band distribution by revenue tier highlights how growth patterns evolve as companies scale. Several structural growth patterns emerge across revenue tiers.

Among companies below $1M, more than one-third achieve growth above 30%, and hyper-growth outcomes appear only within this tier. The smaller revenue base allows for rapid percentage expansion as operators scale routes, technicians, and marketing investment.

In the $1–3M segment, hyper-growth disappears and the share of companies achieving large growth declines by roughly half compared with the <$1M tier. At the same time, nearly one-fifth of operators report declining revenue, indicating that increasing organizational complexity begins to offset early-stage expansion momentum.

Companies above $3M cluster heavily within the moderate growth band (0–30%). While some contraction occurs as markets mature and year-over-year comparisons become more demanding, growth outcomes tend to be more stable and less volatile than those observed among smaller peers.

Overall, patterns in both the revenue growth distribution and growth band across revenue tiers signal a common scaling trajectory: rapid early expansion, a mid-market compression phase, and eventual stabilization as operational systems mature.

Revenue Growth outcomes vary meaningfully across regions, reflecting differences in local market dynamics, operator maturity, and competitive intensity. While all regions exhibit broadly positive growth, dispersion across operators remains significant.

Median growth ranges from 3.7% in North Central to 25.6% in the Mid-Atlantic, indicating that growth conditions vary across regional markets. However, variability within regions remains substantial, ranging from 3.5 pp to 35 pp, reinforcing that operator execution and local market positioning play a significant role in determining outcomes.

Across most regions, the Growing or Moderate Growth Band (0–30%) captures most operators, indicating steady expansion across the industry, regardless of Region. However, the presence of both high-growth and declining operators in several regions highlights meaningful performance dispersion.

The Mid-Atlantic region exhibits the strongest growth profile in the dataset, with median growth reaching 25.6% and the upper quartile approaching 47.8%. Over 44% of operators achieved growth above 30%, including 11.1% reaching hyper-growth levels. Notably, no operators in this region reported severe contraction.

South Central and South East demonstrate balanced growth distributions. Median growth reaches 19.3% and 15.3%, respectively, with strong representation in the moderate growth band. Large-growth outcomes are also common, with roughly 19–26% of operators exceeding 30% growth.

The South West shows greater variability in outcomes. While median growth remains solid at 17.5%, roughly 27% of operators experienced negative growth, alongside a meaningful share achieving strong expansion. This indicates a more polarized performance environment.

The North East demonstrates moderate but relatively stable growth, with median growth at 14.2% and most operators clustered in the moderate growth band.

The Pacific North West shows slower growth dynamics, with median growth of 7.0%. Most operators fall within the moderate growth range, indicating steady but more measured expansion.

The North Central region reports the lowest median growth at 3.7%, though the sample size is limited (n = 3). Results should therefore be interpreted cautiously.

Regional analysis is based on 116 operators with complete 2024–2025 revenue data. Participation varies significantly by region, with larger samples in the South East (n = 35) and South West (n = 30) providing more reliable signals.

Smaller samples particularly North Central (n = 3) and Pacific North West (n = 6) should be interpreted as directional rather than definitive.

Broadly, the regional analysis suggests that growth dispersion across operators is often greater than differences between regions themselves, indicating that operator execution, pricing strategy, and service mix likely play a larger role in growth outcomes than geography alone.

Growth across pest control operators does not progress linearly with revenue size. Instead, the data suggests a three-stage scaling pattern:

Scaling from <$1M to $3M+ therefore requires more than market demand. It typically requires operating redesign, including stronger technician management systems, clearer pricing discipline, route optimization, and more formalized sales infrastructure.

Operators that fail to adapt their operating structure during this transition often experience a prolonged mid-tier plateau, where growth slows despite continued market opportunity. The data indicates this inflection point is driven less by demand conditions and more by internal operational complexity.

Across both regional and revenue-tier views, the data shows that while geographic market conditions influence growth potential, structural factors, particularly scale, operating systems, and growth strategy, may play a more important role in shaping long-term performance outcomes.

Revenue Growth across the pest control industry has remained positive, though signs of moderation have emerged between 2024 and 2025. When growth begins to normalize, the relationship between expansion and profitability is increasingly determined by how effectively operators manage rising costs alongside revenue gains.

This chapter examines expense behavior across participating operators, focusing on multi-year cost structure trends, growth efficiency, and the cost drivers most closely linked to Margin performance.

Unless otherwise noted, analyses are based on Cohort B (n = 116) operators with full-year comparability between 2024 and 2025. Multi-year trend analysis references Cohort A (n = 60) operators with complete financial data from 2023–2025.

Revenue Growth across the benchmark cohort remained strong during the period analyzed, reinforcing the continued expansion of the pest control industry. However, this chapter shows that growth alone does not automatically translate into profitability. The more critical factor is how effectively operators manage the cost structure that supports that growth, ensuring expenses do not scale at the same rate as, or faster than, revenue.

Across the cohort, Revenue Growth and Expense Growth generally moved in tandem. As a result, median EBITDA Margin change was basically flat (+0.24%) between 2024 and 2025. While some operators achieved meaningful operating leverage, where revenue expanded faster than cost, others experienced Margin compression as expenses grew at a similar or faster pace than revenue.

Multi-year trend analysis indicates that the overall industry cost structure has remained relatively stable in composition. Operating Expenses continue to represent the largest portion of the cost structure. At the same time, improvements in Cost-of-Service efficiency were partially offset by rising overhead expenses, especially within administrative and support functions. As companies scale, the operational challenge gradually shifts from managing field-level efficiency to maintaining discipline in the growth of organizational infrastructure.

Segmented revenue analysis further shows that cost dynamics vary by company size. Smaller operators exhibit greater volatility in Margin outcomes, reflecting higher sensitivity to labor productivity, staffing changes, and overhead absorption. Mid-sized operators often demonstrate the strongest cost absorption as revenue scales, while larger operators face increasing pressure from the administrative complexity and commercial infrastructure required to support larger organizations.

Regional comparisons suggest that while labor markets, pricing environments, and operating conditions differ geographically, the core economics of pest control operations remain broadly consistent across markets. Differences in profitability appear to be driven less by geography and more by execution, particularly pricing discipline, technician productivity, and overhead management.

The findings reinforce a central benchmark insight: sustainable profitability in pest control is not simply a function of growth, but of disciplined scaling. Operators who successfully convert revenue expansion into operating leverage tend to maintain technician productivity, manage overhead expansion carefully, and ensure that cost growth remains aligned with revenue productivity. A key takeaway of this chapter is that the industry continues to grow, but the operators who benefit most are those whose cost structures scale more slowly than their revenue.

This benchmark study examined financial performance across 125 pest control operators, with 116 companies forming the comparable cohort for year-over-year growth analysis. The findings reveal several consistent structural patterns that characterize the economics of modern pest control businesses.

Across the cohort, median Revenue Growth between 2024 and 2025 was 15.4%, with the 50% of operators growing between 5.7% and 28.9%. This range reflects both strong expansion among many operators and meaningful variability in growth trajectories across the industry.

Profitability outcomes were more dispersed. Median EBITDA Margins reached 15.6% but ranged from 6.7% at the 25th percentile to 24.4% at the 75th percentile, highlighting the importance of operational efficiency and cost discipline in determining final profitability outcomes.

Cost structure benchmarks remained relatively consistent across the cohort. Median Gross Margins were approximately 61%, while total people costs consumed roughly 37% of revenue. Sales & Marketing spending averaged 11.2% of revenue, while Operating Expenses represented 43.9% of revenue at the median.

Taken together, these benchmarks confirm a central theme observed throughout the study: while Revenue Growth remains strong across the industry, profitability outcomes depend heavily on how effectively operators manage labor productivity, marketing investment, and overhead expansion as they scale.

Several shifts observed in the 2024–2025 data provide important context for operators planning their 2026 budgets.

These trends suggest that the most successful operators entering 2026 will be those that sustain growth while maintaining discipline in labor productivity, marketing efficiency, and overhead expansion.

The following benchmarks provide practical budgeting reference points for operators planning the upcoming year.

Smaller operators tend to grow faster but often face more variability in operating performance. Maintaining discipline around labor efficiency and marketing investment is particularly important at this stage of growth.

Operators in this range typically experience increasing route density and operational stability but also begin to absorb more overhead as the business scales.

Larger operators tend to demonstrate slightly stronger service-level Margins, although profitability ultimately depends on how effectively overhead and administrative complexity are managed.

To avoid gradual Margin erosion, operators should monitor several key metrics throughout the year.

The most important indicators include:

Tracking growth relative to plan ensures that fixed overhead expansion remains aligned with top-line performance.

Because labor represents the largest expense category, even small increases in staffing levels or wage pressure can materially impact profitability.

Monitoring revenue generated per marketing dollar helps ensure that customer acquisition investments remain productive.

Ultimately, EBITDA Margin reflects the combined impact of pricing discipline, labor productivity, marketing efficiency, and overhead control.

Regularly reviewing these metrics allows operators to identify potential Margin pressure early and adjust operating decisions before financial performance deteriorates.

* North East Materials Q3 shown as 8.3% (same as Median) — flagged in raw extraction as possible column alignment issue in source PDF; value retained verbatim pending source verification.

While regional differences exist, the underlying economic structure of pest control operations remains broadly consistent across markets. The largest variations tend to reflect differences in labor markets, route density, and customer acquisition strategies rather than fundamental changes in service economics.

Note: Regional comparisons are presented for descriptive purposes. Regions with smaller sample sizes should be interpreted cautiously, as individual operators may materially influence the reported medians.

This benchmark study provides a detailed view into the financial structure and operating performance of modern pest control businesses. The findings confirm that while industry growth remains strong, outcomes vary widely in how effectively that growth translates into profitability.

Across the cohort, the difference between high-performing operators and the rest is not simply growth itself, but how that growth is managed. Operators that consistently achieve strong results demonstrate discipline in cost structure, clarity in investment decisions, and the ability to convert revenue expansion into sustainable margin improvement.

Benchmarking, therefore, is not about replicating another operator's numbers. It is about understanding the underlying economics of your own business, identifying where performance diverges from peers, and taking deliberate action on the most impactful drivers.

As the industry continues to scale, the gap between efficient operators and those growing at a cost is likely to widen. Those who actively use benchmarking to guide decisions will be better positioned to adapt, allocate resources effectively, and build businesses that remain both competitive and profitable over the long term.

Two cohorts are referenced throughout this study:

To preserve true comparability and avoid distortion from partial-year ramp-up effects, FRAXN did not annualize their 2024 financial records to account for missing months. Where full-year comparability is required for 2024–2025 comparisons, the analysis uses the subset of operators with complete operating periods (n = 116), while the full benchmark cohort (n = 125) is retained for structural benchmarking analyses. Unless otherwise noted, all structural benchmarking results reference Cohort B (n = 125).

FRAXN translated diverse accounting terminology into a standardized benchmarking framework. Because operators use different bookkeeping structures, accounts are mapped based on economic function rather than label.

The guiding principle is straightforward:

Unless otherwise noted, benchmarks are expressed as a percentage of revenue for the applicable year of analysis.

Comparative revenue tiers are based on 2024 baseline revenue and remain fixed across the 2023–2024 and 2024–2025 comparisons to avoid artificial tier migration.

Note: Costs recorded under accounts 60101, 60102, 60104, 60105, 50110, 50106, 50107, 50108, and 60109 were split and allocated based on their economic function. A portion of these accounts relate to Sales and Marketing payroll and were classified accordingly.

Profitability in this report is evaluated through a sequential operating structure that reflects how revenue converts into operating profit. The benchmarks follow a simple progression:

Revenue → Cost of Service → Gross Margin
Operating Expenses → EBITDA Margin %

The following metrics capture the primary performance outcomes of pest control operators, including Revenue Growth, Gross Margin, and operating profitability. These benchmarks reflect how effectively operators convert revenue into profit across different cost structures and operating models.

(Current Year Revenue − Prior Year Revenue) ÷ Prior Year Revenue

Measures year-over-year change in total revenue relative to the prior-year baseline. Revenue Growth reflects the pace at which operators expand through customer acquisition, pricing adjustments, and service volume.

(Revenue − Cost of Service) ÷ Revenue

Measures the portion of revenue remaining after direct service delivery costs are deducted.

Gross Margin provides an indicator of field-level operating efficiency before administrative overhead and growth investments are considered. It helps determine whether Margin pressure originates within service delivery or elsewhere in the cost structure.

(Revenue − Cost of Service − Operating Expenses) ÷ Revenue

Measures overall operating profitability after both service delivery costs and operating overhead are deducted.

For benchmarking consistency, accounts related to financing activities, income taxes, and other non-operating items are excluded from the analysis.

Because depreciation, amortization, and certain non-operating accounts are not consistently reported across operators, EBITDA in this study should be interpreted as an operating profitability benchmark rather than a strict accounting EBITDA measure.

This standardized definition provides a consistent basis for comparison across operators regardless of capital structure, ownership, or accounting treatment.

The following metrics decompose the operating cost structure underlying Gross Margin and operating profitability. Evaluating each component individually reveals the cost drivers behind the performance benchmarks.

Cost of Service ÷ Revenue

Cost of Service includes only direct costs required to deliver pest control services, including:

Note: Where operators record vehicle costs across multiple accounts, expenses are mapped based on their economic function to ensure costs associated with field service delivery are consistently included in Cost of Service.

Operating Expenses ÷ Revenue

Operating Expenses represent all indirect costs required to support the business beyond direct service delivery.

OPEX is not benchmarked as a single aggregate percentage in this study. Instead, analysis is performed at the component level to improve diagnostic precision and allow operators to isolate specific cost drivers rather than reacting to blended overhead totals.

Primary OPEX components include:

Note: Where payroll accounts combine multiple functions, compensation is reclassified based on job function to ensure administrative labor is separated from field labor and Sales & Marketing activities.

Note: Where sales-related payroll or marketing expenses are embedded within broader payroll or administrative accounts, costs are mapped to Sales & Marketing based on their underlying economic function.

Cohort A (2023–2025 trend cohort) has limited participation in certain regions. No North Central operators currently meet full three-year data requirements. As a result, three-year trend analysis for that region is not presented.

In regions with smaller sample sizes, benchmark results should be interpreted with appropriate caution, as thinner cohorts may be more sensitive to the performance of a small number of operators. As participation expands over time, regional benchmarks are expected to become more stable and representative.

Within the <$1M revenue tier, percentage changes in growth and Margin metrics may appear amplified due to smaller revenue bases. These observations are retained in the dataset. However, the use of company-weighted medians and quartiles reduces the influence of extreme values. Results should be interpreted with appropriate consideration of scale sensitivity.

Financial data used in this study is derived from a combination of operator-submitted bookkeeping records and FRAXN-managed accounting records. While FRAXN standardizes accounts into a consistent benchmarking framework, underlying transaction classification generally follows each participating company's internal reporting practices.

FRAXN performs structural mapping to a standardized chart-of-accounts to ensure comparability across operators. However, no forensic reclassification, audit procedures, or reconstruction of financial statements were performed beyond this standardization process.

As a result, the benchmarks presented in this report reflect reported operating performance rather than audited or restated financial results.

Negative totals within Cost of Service (COS) or Operating Expense (OPEX) categories may arise due to accounting reversals, reclassifications, or one-time adjustments. Immaterial negative amounts are retained. However, where negative category totals exceed a defined materiality threshold (e.g., 1% of revenue), they are capped at zero for benchmarking ratio calculations to maintain structural comparability across operators.

Similarly, if the net total of a cost category is negative, it is excluded from the analysis — particularly for dynamic benchmarks (e.g., revenue per dollar spent on a specific category) — to avoid distortions in ratio interpretation.

In certain visualizations — particularly scatter plots — extreme outlier observations may be omitted from the chart display to improve readability and highlight the primary distribution of results. These extreme cases represent a very small number of observations and do not materially affect the underlying benchmark calculations.

Unless otherwise noted, distribution statistics and benchmark values are calculated using the full dataset, with observations retained unless a clear data integrity issue is identified.

Some operators utilize door-to-door (D2D) or other high-investment sales models, which structurally increase sales payroll and marketing expense relative to peers. Benchmark comparisons should account for differences in go-to-market strategy, customer acquisition intensity, and service mix when interpreting cost structure and Margin outcomes. Results should be interpreted with appropriate consideration of this business model differences.

This report would not exist without the pest control operators whose financial data made it possible. Every benchmark in this study is built from real books, maintained month after month by business owners who trusted FRAXN to keep their financials clean and consistent. That trust is the foundation of this work, and we do not take it lightly.

The operators who contributed to this dataset did not do so to be benchmarked. They did so because they were trying to run their businesses well. This report is our attempt to give something back: a clearer picture of the industry they are part of, built from the same data they already had.

The FRAXN team built the standardization framework that made apples-to-apples comparison possible across operators with different accounting structures, bookkeeping practices, and chart-of-accounts conventions. That work happens quietly, every month, for every client. It is what separates this report from a survey.

When I was running a pest control company, we were doing about $3.75 million in annual revenue when we sold. Our financial benchmarking at the time was pretty simple: did revenue go up? Is there more in the bank than last time I checked?

That was it. No external reference point. No way to know if our cost structure was tight or leaking. Most of our biggest decisions, hiring, trucks, marketing spend, were made on gut feel and last year's numbers with a little added. We had no idea if we were actually disciplined or just lucky.

I started FRAXN because I believed pest control operators deserved better than that. Clean books. Consistent reporting. A financial structure that actually tells you something. This benchmark is the next step in that direction. It is the report I would have wanted when I was the one running routes and writing checks.

We built this for operators who are running their businesses as carefully as they can but have never had a real external reference point. That is most of the industry. Hopefully this changes that.

Put your numbers next to the benchmarks. See where you stand. That is the whole point.

Patrick Baldwin
Co-Founder and CEO, FRAXN