Market Research for Commercial Cleaning Companies

Most commercial cleaning companies set growth targets and pricing based on gut feel and whatever the last few RFPs looked like, without ever pulling the actual market data that would tell them whether their target segment is growing, shrinking, or already saturated with competitors chasing the same buildings. Market research sounds like something only larger companies with dedicated staff do, but a few hours spent with publicly available data changes decisions that matter, including which vertical to specialize in, which neighborhoods to prioritize for outreach, and what pricing actually clears in your specific market.

The U.S. janitorial services industry (NAICS 561720) is a sizable and growing market, though estimates vary by research firm and methodology, with figures from recent industry reports ranging from roughly $90 billion to over $110 billion depending on scope and vintage, with commercial work representing close to 89 percent of total industry revenue. The Bureau of Labor Statistics counts roughly 2.3 to 3.3 million janitors and building cleaners nationally, with a median hourly wage around $17.71 as of May 2025 (BLS Occupational Employment and Wage Statistics). That wage figure alone is worth checking against your own local labor cost assumptions, since national medians can be misleading in high-cost or low-cost regional markets.

Source: U.S. Bureau of Labor Statistics OEWS; industry market-size research (IBISWorld and comparable firms); Opora editorial analysis.

Local Data Beats National Averages for Actual Decisions

National market size figures are useful for understanding the broader trajectory of the industry, but they tell you almost nothing about whether your specific metro is oversaturated with cleaning companies bidding on the same office parks. Pull your county or city's business license registry to count how many cleaning-related businesses are actually registered in your service area, then cross-reference that against commercial vacancy data from local commercial real estate brokerages, which is often published quarterly and free to access. A market with rising office vacancy and a growing count of registered cleaning competitors is a genuinely different opportunity than a market with tight vacancy and few local competitors, even if both sit inside the same broad national growth trend.

Sizing Your Addressable Market by Building Type

Rather than trying to estimate "the market" broadly, size it by the specific building types you're equipped to serve. Count office buildings, medical facilities, retail centers, and industrial space in your target radius using county assessor data or commercial real estate listing platforms, then apply a realistic penetration assumption. Most local cleaning companies, even successful ones, service a small single-digit percentage of addressable buildings in their market, which should temper unrealistic growth projections built on the assumption you can eventually win "most" of a market.

• Pull assessor or GIS data for commercial parcels by building type in your target zip codes
• Estimate square footage bands (under 10,000, 10,000–50,000, 50,000+) to match your crew capacity and equipment
• Cross-reference with vacancy and lease activity to estimate near-term demand shifts
• Count registered competitors and estimate their approximate fleet/crew size from public job postings or LinkedIn headcount data

Reading Competitor Signals Without Paying for Expensive Tools

You don't need an expensive competitive intelligence subscription to learn a lot about local competitors. Job postings reveal approximate crew size and growth trajectory. LinkedIn company pages show headcount trends over time. Google reviews reveal service gaps competitors struggle with repeatedly, which is often the clearest signal of where you can differentiate. This kind of research pairs directly with the work covered in partnership marketing for cleaning businesses, since understanding which competitors are stretched thin or under-resourced can reveal partnership or acquisition opportunities rather than pure head-to-head competition.

Using Market Research to Set Realistic Growth Targets

A market research pass should directly inform your revenue targets, not just sit in a folder. If your addressable market in a specific vertical supports maybe 40 realistic target buildings and you already hold 12 of them, that changes how aggressively you should be investing in new client acquisition in that specific niche versus expanding geographically or into an adjacent vertical. Tie this analysis to your actual landing page strategy and retargeting spend, since knowing your addressable market size prevents overspending to chase leads in a segment that's already close to saturated for your current capacity.

Build the County Number Yourself in About Ninety Minutes

Everything above is directionally right and none of it produces a dollar figure you can budget against. Here is the build that does, using three free federal datasets and one assumption you control. Do it once a year, in a spreadsheet, and keep the spreadsheet.

Step one: count the demand side. The Bureau of Labor Statistics' Quarterly Census of Employment and Wages reports employment and average weekly wages by county and NAICS code, covering essentially every job subject to unemployment insurance, not a survey sample. Pull county employment in the office-using sectors: information (51), finance and insurance (52), real estate (53), professional and technical services (54), management of companies (55), and administrative services (56). That total is the closest free proxy there is for how much occupied office space exists in your county, because office square footage tracks headcount far more tightly than it tracks construction.

Step two: convert headcount to square feet. Multiply by square feet per office worker. This is your assumption and the whole model hinges on it, so write it down explicitly rather than burying it. Densification has been pushing this number down for fifteen years; 185 sq ft per worker is a defensible starting point for a mixed suburban market, lower in dense downtowns with newer fit-outs.

Step three: convert square feet to spend. Multiply by a monthly janitorial rate per square foot for your market and by twelve.

Model: Opora analysis. Inputs from BLS QCEW county tables; density and rate assumptions set by the operator.

Now put your own revenue against it. A shop doing $1.4M a year in office work in that county holds 11.9 percent of its addressable office market. That is a genuinely large share for a local BSC, and it changes the strategy conversation immediately: the next $500K is not going to come from working the same phone list harder. It comes from an adjacent vertical, an adjacent county, or from taking accounts off named competitors: three very different budgets.

The Sensitivity That Actually Threatens You

Run the model once and it looks like a market-size exercise. Run it twice with a different density assumption and it turns into a risk model, because the input that moves your addressable market most is not employment growth. It is square feet per worker.

Model: Opora analysis, holding employment at 38,000 and rate at $0.14/sq ft/month.

A county can add office jobs and shrink your market at the same time. Employment up four percent while density tightens from 185 to 150 still nets out to a market that is roughly 16 percent smaller than it was. Every operator who lost square footage on a renewal while the tenant kept the same headcount has already lived through this arithmetic without naming it. The defensive move is contract structure, not sales effort: price by task and frequency with a stated minimum monthly, so a tenant giving back one floor triggers a scope renegotiation rather than a straight proportional cut on a per-square-foot contract.

Use QCEW for Wages, Not the National Median

One more reason to open QCEW rather than stopping at a national figure: it publishes average weekly wages for NAICS 561720 in your specific county, quarter by quarter. That is a better labor-cost input than a national occupational median for two reasons. It is county-level, so it reflects the labor market you are actually hiring in. And because it comes off unemployment-insurance filings, it captures total covered wages including overtime and bonuses, which is what your competitors are actually paying, not what a base-rate table says they pay.

The operational use is concrete. Divide the county average weekly wage for 561720 by a typical weekly hours figure to get an implied hourly rate, then compare it to your own starting wage. Sitting more than about a dollar below the county figure predicts your turnover before your turnover report does, and turnover in this business is priced in recruiting hours, training hours, and the quality complaint that arrives in week three of a new cleaner on an account. Cross-check the occupational picture against the OEWS table for janitors and cleaners (37-2011) at the metro level, and pull establishment counts by employment-size class from the Census Bureau's County Business Patterns to see how many of your local competitors are actually large enough to bid the buildings you want. Most counties have far fewer real competitors above ten employees than the search results suggest.

Frequently Asked Questions

Where can I find free local commercial real estate vacancy data?
Many regional commercial real estate brokerages publish quarterly market reports free to the public, and local economic development offices often compile similar data for their jurisdictions.

How often should market research be updated?
An annual refresh is reasonable for most small operators, though a faster-changing local market, with new construction booms or major employer relocations, may justify a more frequent check.

Is national industry growth data useful at all for a local operator?
It's useful context for understanding broad direction (growing, flat, or shrinking industry) but should never substitute for local data when making specific pricing or expansion decisions.

How do I estimate a competitor's crew size without asking them directly?
Job posting history, LinkedIn employee counts, and observed vehicle fleet size at job sites all provide reasonable estimates without needing direct disclosure.