How Crown saved $1M in cleaning costs by adjusting labor hours to reflect real-time occupancy
Impact Cleaning Services
Crown needed to adjust to near-zero occupancy while maintaining the highest level of cleanliness. They wanted to know how many people were using their buildings, how much labor should be allocated, and how much should be billed to avoid paying for unused labor.
Using Mero’s occupancy monitoring, Crown is allocating labor based on usage. In addition, they renegotiated contracts with cleaning partners - resulting in 1M$ in cleaning cost savings and an 11X return on investment.
“ By allowing us to see in real-time what’s being consumed, building traffic and the movement flows of our employees, it allows us to make smarter decisions and understand site-by-site the unique challenges of each building and adapt our workflows to be more efficient. ”
Vice President at Impact Cleaning Services
Crown, a Canadian-based commercial real estate property management company, cut through the noise.
Using Mero’s real-time occupancy monitoring, they got the data needed to allocate labor based on building usage.
With hard numbers backing them, they worked with cleaning partners to renegotiate contracts and adjust labor hours.
Before Mero, operations teams at Crown counted cars in the parking lot to understand how many people were in the buildings on any given day.This wasn't just time-consuming. It provided an unreliable proxy for actual occupancy data.
Plus, they couldn't understand how each washroom, lobby, or floor was used.
"A major problem was the discrepancy between the traffic reported by property managers and the traffic cleaners were seeing in the buildings," said Nathan, co-founder at Mero."
He adds: "There was no clear way for both parties to agree on how many people are actually using the building and, therefore, how much should be billed to the property."
Crown and its cleaning partners needed transparent and objective data to make informed decisions.
Since implementing Mero across 43 major commercial buildings, Crown saved over 1M$ in cleaning costs across its portfolio - an 11X return on investment in 9 months. In addition, they were able to cut supply waste by 24%.
Why Crown chose Mero
Any deployment couldn't involve building modifications, including drilling, cosmetic changes, or even minor adjustments.
Mero sensors are universal. They don't require you to be locked into a proprietary supplier that charges 2$-3$ extra for every refill.
They fit nearly every dispenser available on the market and can be installed by anyone in 30 seconds or less.
2. Required an IT architecture independent from the existing building Wi-Fi network
Crown didn't have any IoT devices in place at that point. So without any base-level network, Mero brought its own network as an independent IT infrastructure, reducing setup time and simplifying the rollout.
3. It had to be flexible in terms of cost
Cost was a significant barrier. The Crown team didn't want to invest a considerable amount upfront to install hardware in their buildings.
Mero doesn’t charge for hardware or training with zero CAPEX involved.
With a simple monthly price and no multi-year contracts, Crown was confident they would see results before scaling.
Near-zero occupancy hits the world
To compound the situation, cleaning standards have become more demanding than ever. High touch points and sanitization on doorways and washrooms became a must - amplifying the existing issues the Crown team was facing:
- There was no visibility across building occupancy and usage. That means no way to know how much and where to allocate labor.
- Cleaning costs were based on full occupancy. Crown would be paying for unused labor if things didn't change.
- Tenants needed reassurance that buildings were safe before coming back to work. Crown had no way to communicate how they were investing in cleanliness and protecting the health of occupants.
Going from reactive to proactive
They are living in reality with real-time visibility over occupancy, supply levels, and the history and length of each cleaning session.
Equipped with this data, they make confident decisions on how much and where to deploy labor.
Instead of following static routines, cleaning staff gets alerts routing them to the right space at the right time whenever a dispenser is running empty, or a restroom/lobby reaches a high traffic threshold.
Cleaning staff now prioritize cleaning where people are active, preventing tenant complaints before they happen and eliminating redundant work.
Before Mero, Crown buildings were wasting nearly half of supplies due to pre-emptive refills. After Mero, Crown improved its refill levels significantly, translating to a 24% reduction in consumables waste and over 2000 paper rolls diverted from landfills.
This use case has since evolved from a tenant-facing app to an issue-reporting tool where tenants can let landlords know if anything needs fixing or servicing.
How Crown used occupancy data to unlock 1M$ in savings
This allows them to answer three questions at all times:
1. How much cleaning is needed in each building
2. How much labor should be allocated to each building
3. How much should be billed by cleaning partners
The data was then fed into their proprietary cost models.
Property managers knew how much cleaning was needed each week and implemented changes directly with cleaning companies.
Cleaning partners could be confident that they weren't overcleaning spaces that weren't used and could spend more time on disinfection and deep cleaning.
And Crown was confident they were getting the best service and only paying for used labor.
Using this methodology, Crown saved over 1M$ in cleaning costs across its portfolio by adjusting upfront hours and moving them into the end of contracts.
To summarize, Crown followed these steps:
1. Get occupancy traffic weekly.
2. Plug occupancy data into their proprietary cost model to calculate the amount and cost of cleaning labor required based on occupancy.
3. Create cleaning schedules for each building weekly.
4. With accurate data on hand, work directly with cleaning partners to adjust upfront hours and move them at the end of contracts.
Make informed decisions when it matters most
Because Crown had visibility over occupancy, supply levels, and proof of service, it could make informed decisions fast when it mattered most.
Without this data, they would be waiting for post-mortem or retrospective reports. They would be getting the information to make corrective decisions months or even years after the fact - when it would be too late.
The opportunity cost of waiting is, well…costly.
If they hadn't assessed the situation using real-time data and proactively worked with cleaning partners, Crown would have faced an additional 1M$ in costs when it most needed cash flow.
In short, they could adapt faster than the competition because they had the insights to confidently make decisions, something impossible without occupancy or supply data.
"You should still do quarterly reports and end-of-contract reports. But now, Crown has another set of data that allows them to respond in real-time instead of waiting for the end of those quarters and contracts", adds Nathan.
Takeaways for the Era of variable occupancy
Commercial cleaners are struggling with a shrinking labor pool. With margins at risk, they need to clean faster with fewer people while meeting more demanding standards.
Property managers need to manage maintenance costs, ensure they get the best service, and reach their P&L targets.
And both are struggling with occupancy that can vary wildly from day to day.
All stakeholders have incentives to make sure cleaning is more transparent and allocated efficiently based on usage.
And here's the crux: no one knows what will happen next.
Tenants could come back faster than anticipated. Fluctuating daily occupancy could become the new default.
Having real-time data lets you adjust faster to changes, whatever they might be.
This ability is invaluable when unprecedented factors are rewriting the rules.
To summarize, here are the takeaways:
1. Real-time data gives you the confidence to make decisions fast during critical times.
Using Mero's data and insights, Crown can adjust exponentially quicker than static scheduling and having no data.
They can adapt in real time. They don't have to wait for retrospective data months after the fact.
They can work with cleaning partners during the length of their contracts rather than wait until the end to renegotiate.
2. Data acts as an impartial mediator for more robust and rational collaboration
Without data, there's no alignment.
Property managers will say there's this amount of people in the buildings. Cleaners will say there are more people than anticipated.
Data bridge the gap. It pushes everyone to act on reality, not guesswork.
All stakeholders have a reference point to discuss how the work should be done and how much should be billed.
3. The opportunity cost of waiting for the entire length of your cleaning contracts before renegotiating is massive.
Being proactive and working with cleaning partners immediately (instead of months or years down the contract's life) can’t be overstated.
If Crown had waited to renegotiate cleaning contracts when occupancy hit near zero, it would have lost 1M$ in potential savings.
4. Forward-thinking cleaning partners are willing to work with you.
One thing we can guarantee you after working with commercial cleaning companies over the years:
There is no shortage of them that value transparency, operating on real-time data, and willing to work with you so labor costs reflect occupancy in your buildings.
They are facing a labor shortage. Yet, they still want to provide the best level of service possible.
It's in their best interest to use the same data to understand what's happening at a site level and improve their workflows, so they don't waste valuable time cleaning when no one's been there.
They want to use data to keep occupants healthy, reduce carbon emissions and generate savings.
But most stop short of putting it into action.
The Crown team showed the industry that it's not so daunting.
They got their hands on occupancy data, plugged it into a cost formula, and had an open discussion with their cleaning partners that pricing should fit what the data says.
Mero helped them bridge the gap to allocate labor dynamically based on real-time occupancy.
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