The seven-input soiling-pressure model behind the cadence call, the five frequency bands and what each one means at the route economics level, the per-pane time math that turns frequency into hours and dollars, and the case-for-change language that lets a route operator move a contract cadence without losing the client. The commercial-route counterpart to the residential cleaning-schedule builder.
What the Storefront Frequency Calculator does, in five points:
The tool's job is to put a calibrated math under a number that often gets set by habit and never gets revisited. Sometimes the math confirms the cadence; sometimes it doesn't. Either answer is more useful than the absence of the math.
There is a number on a commercial window-cleaning contract that almost nobody talks about, and that number is the frequency. The contract says weekly, or biweekly, or twice-weekly, or daily, and the number stays the same year over year regardless of whether the storefront is still the same storefront. The tenant changes. The adjacent business changes. The foot traffic doubles or halves. The frequency does not change, because nobody on either side of the contract has a structured way to argue that it should.
The Storefront Frequency Calculator is the tool we built to put a math under that number. It takes seven inputs — foot-traffic level, sidewalk proximity, adjacent-business contamination, climate driver, business tier, time-of-day, and building age — and combines them into a soiling-pressure score that maps to one of five frequency bands. It also takes the current contract frequency and compares it to the math-derived cadence, generating the narrative an operator needs to either confirm the current cadence or make the case to the client for a change.
This piece is the methodology behind the tool. What each input is measuring, why the score is additive rather than multiplicative, how the per-pane time math is calculated separately from the frequency math, and what the case-for-change narrative is built to do.
The residential Cleaning Schedule Builder uses multiplicative interaction in its score: hard water times pollen times pollen-season-window times maintenance tier produces a stronger result than the sum would. That model is correct for residential because residential soiling factors interact — pollen on a hard-water storefront produces a worse mark than either alone, because the hard-water residue gives the pollen something to bond to.
Commercial storefronts are different. The factors that drive commercial soiling operate substantially independently. The grease aerosol from a restaurant kitchen vent within fifty feet of the glass doesn't make foot-traffic fingerprints worse; it adds a separate film on top of the fingerprint contamination. Salt aerosol on a coastal storefront doesn't multiply the effect of pollen during pollen season; it stacks on top. An additive model is correct for commercial because the soiling sources stack rather than interact.
The score range runs from roughly 14 (lowest plausible combination — very light foot traffic, enclosed entry, light retail adjacency, temperate baseline, commodity tier, pre-dawn cleaning, new construction) to roughly 95 (highest plausible — very heavy foot traffic, street-side, restaurant adjacency, pollen-heavy season, premium tier, midday cleaning, heritage building). The five bands divide the range cleanly: monthly under 20, biweekly 20–34, weekly 35–54, twice-weekly 55–74, daily 75 and up.
Foot traffic (4 to 20 points) is the largest single input, which reflects what working route operators know: pedestrian contamination dominates most urban storefront soiling. Every hand on the door pull, every shoulder brushing the glass at the entry, every parent lifting a stroller past the panel — each contact leaves a fingerprint or smear that accumulates visibly between cleanings. The five tiers from "very light" (under 50 visitors/day) to "very heavy" (1500+/day) span the full range of commercial route reality.
Sidewalk proximity (2 to 9 points) modulates how much of the broader environment reaches the glass. Street-side storefronts at 9 points carry the heaviest weight because the glass sits in the direct path of vehicle exhaust, kicked-up road grime, and pedestrian flow. The lighter end — enclosed vestibule entries at 2 points — gets only what pedestrians carry through on hands and clothing. The drive-up category at 4 points captures the petroleum-aerosol film that accumulates on drive-through glass at idle, which is a different contamination profile but a similar total pressure.
Adjacent business (4 to 14 points) is the input most often missed in inherited contracts. Most contracts were written when the adjacent businesses were different from what they are today. A row that has gained a restaurant or a mechanic or a bar in the intervening years has gained 10–14 points of soiling pressure that the contract frequency was not set for. Kitchen exhaust at 14 points is the highest weight because grease aerosol is the hardest commercial contamination after petroleum film — it requires a degreaser, it extends per-pane time, and it accumulates faster than fingerprint-only contamination.
Climate driver (4 to 11 points) covers the regional and seasonal factors operating on the route. The pollen-heavy season at 11 points is the highest weight because pollen accumulation is the single most visible and time-bounded contamination event in the route year — most operators temporarily compress cadence during pollen weeks. Dusty-arid climates at 10 points and humid climates at 8 points capture the more chronic regional drivers. Temperate baseline at 4 points is the friendly case where climate isn't driving the cadence.
Business tier (1 to 8 points) is the input that captures client expectation. A premium tier — luxury retail, fine dining, top-tier banking — sets the cleanliness bar high; the cost of an under-cleaned window in the brand-visible calculus is contract-meaningful. A commodity tier — discount retail, lower-cost food service — sets the bar at "acceptable" and supports a tighter pricing envelope. The tier multiplier is small in absolute terms but consequential because it pushes borderline scores across band boundaries in the high direction for premium and the low direction for commodity.
Time-of-day (0 to 4 points) captures the operational efficiency cost of cleaning during open hours. Midday cleaning at 4 points reflects what operators know: customer interference during open hours slows per-pane time and effectively reduces the cleaning value of each visit. The other three time-of-day options — pre-dawn, morning before open, and after-close — all run at 0 or 1 point because operator productivity is high in all of those windows.
Building / window age (0 to 4 points) captures both the soiling pressure of older glass-and-frame conditions and the per-pane time penalty of working older glass. The age input is the one input that affects both the frequency score and the per-pane time calculation; new construction at 0 added pressure and a 0.92 time multiplier, mid-life at 2 added pressure and 1.00 time multiplier, heritage at 4 added pressure and 1.15 time multiplier.
The frequency band tells the operator how often to clean. The per-pane time math tells them how long each visit will take. The two together determine the route economics — pricing, route density, and whether a frequency change is operationally feasible at the current contract price.
The per-pane time baseline is 75 seconds, calibrated against typical commercial cleaning of a standard storefront pane (4–6 feet wide, single-pane or sealed-IGU, no specialty coating or treatment). The baseline is then modulated by:
Building age, which multiplies the baseline by 0.92 (new), 1.00 (mid-life), or 1.15 (heritage). The heritage multiplier reflects what working operators experience on older commercial stock: frame contamination drips onto the cleanable glass during cleaning, mortar bleed shows up on glass adjacent to brick, and the per-pane time runs roughly 15% longer.
Adjacent business contamination, where four categories carry significant time penalties on top of the building-age math. Kitchen grease adjacency adds 20% to per-pane time because the grease film requires a degreaser and a longer dwell. Mechanic or gas-station adjacency adds 18% because petroleum film carries a similar dwell penalty. Bar or smoking adjacency adds 12% because nicotine film requires alcohol-cut or solvent rather than the house standard. Bakery flour adds 8% for the fine-particulate cleanup.
Midday cleaning adds 15% on top of any of the above, reflecting the customer-interference overhead during open hours.
The compounded penalties at the worst case — heritage building, kitchen grease adjacent, midday cleaning — produce a per-pane time of around 119 seconds, roughly 60% above the baseline. On a fifty-pane storefront, that compounds to 50 minutes versus 30 minutes at the baseline — a real operational cost that should be reflected in either the cadence math or the contract pricing.
The frequency band and the comparison to the current contract produce one of three outcomes: matched (no change needed), under-serviced (recommend increased cadence), or over-serviced (consider rolling back). The tool's case-for-change narrative is generated from this outcome plus the dominant pressure drivers.
The under-serviced narrative is the one most likely to matter to a working operator. Selling a contract client on increased cadence is hard, and the language matters. The tool's narrative leads with the brand-visibility argument because that is the argument that resonates with the client side of the conversation: a storefront in a premium or mid-tier business is the elevation customers see before they decide whether to come in, and the cost of an under-cleaned window in that calculus is contract-meaningful. The tool then provides the per-pane-time math and the pricing-change framing — a move from weekly to twice-weekly is roughly 1.9× the monthly rate, allowing for slightly faster per-visit time at the higher cadence — so the operator can present the change with the operational math in front of them.
The over-serviced narrative is harder for an operator to face because it points toward voluntarily reducing contract revenue. The tool's narrative is honest about this — the change builds client trust and often retains the contract longer than over-servicing does, but the decision is also legitimately the operator's. Sometimes the right move is to keep the higher cadence with the understanding that the operator's margin on the contract is correspondingly higher, particularly on commodity-tier contracts where price compression elsewhere may not leave room for it.
The matched narrative is the quietest and probably the most common. The current cadence matches the soiling pressure; no case to make; the contract is right where it should be. The tool's job in this case is to provide the documentation — the operator can show the client the math that confirms the contract is appropriately specified, which is its own form of value during a contract-renewal conversation.
The tool replaces the no-math-at-all default that governs most inherited commercial contracts. It does not replace the operator's judgment about the specific client relationship, the route economics, or the strategic decision of whether to push for a frequency change at this point in the contract life. The math provides the cadence and the case-for-change; the decision to act on either is the operator's.
For a route operator inheriting a contract from a retiring colleague, the tool is documentation — a way to confirm that the cadence the previous operator set is still the right cadence under current conditions. For a small-business owner bidding on a new commercial contract, the tool is the pricing framework — the frequency and the per-pane time together set the operational cost, and the contract price needs to clear that cost with margin. For a contract client who wants to understand why their bid came in at a particular price, the tool is the explanation — the inputs are visible, the math is visible, and the cadence recommendation falls out of both.
That is what the tool is for, and it is what most of the calculator-family tools on the site are for: a calibrated number under a question that working operators carry as a feel. Sometimes the feel and the math agree. Sometimes they don't. Either way, the math gives the conversation something to start with.
Jan Davenport is part of the Giordano Inc. editorial team and covers the Midwest and Great Lakes editorial beat for Window Washing Guide, with particular focus on pricing and small-business operations. Editorial content is researched and reviewed in collaboration with the Giordano Inc. editorial team and informed by interviews with practicing window-washing operators in the region, plus published trade and small-business references.