Pricing House Cleaning Assignments

HCA Statistical Analysis with Practical Applications

15.The Effect of Operating Profiles on Cleaning Times

Individuals vs Teams: The analysis does not relate to individual cleaners, only teams of cleaners. We imagine the man-minutes involved for individuals might be different than for teams, and show more variability. Since the test company generally employs team cleaning, the data for individuals was not available.
Constraints — Fixed vs Variable Team Size: In factoring the daily schedule, our experience shows that constraints can have a significant impact on a company’s long-term profitability. As degrees of freedom are reduced, the natural reaction for any operating manager is to hire additional employees to meet the needs for the busiest days. As our analysis shows, the overhang associated with carrying these extra employees on slower days can be costly in a sneaky way, because professional cleaners tend to balance their work days to return at uniform times. So unless a company manages to measure efficiency by day, an inexperienced operator might mistakenly associate regular return times with full utilization.
A particular clean day of the week can be the mostly costly constraint, and can only be avoided by managing clients and their expectations. Failure to limit the continuous flow of customers to Fridays can cause, in its extreme, Fridays to account for 50% of total weekly revenue.
The second most important constraint is “First Clean of the Day” and the combined two, “First Clean on Friday” can quickly cause a schedule to become quite costly to solve.
Our experience shows that constraints can frequently be managed on the busiest days by breaking teams into more groups of smaller individuals. Demonstrating flexibility in team sizes can significantly improve profitability, because it allows a company to meet the demands of its busiest days with a net smaller number of individuals than might otherwise be necessary, while still allowing for larger team sizes on slower days.
And larger average team sizes on slower days allows a company time for training; aside from time, it also provides opportunities to diversify personnel, for evaluations and training, among a larger number of supervisors per week for each cleaning professional. Our experience shows that, over time, such practices can significantly enhance overall quality of service and reduce client churn.
Flexibility in scheduling represents another significant advantage which independent house cleaning companies have over many franchise maid services.
Compensation Schemes: Compensation schemes can affect results in terms of both quality and efficiency. Although this analysis does not compare the man-minutes required to clean homes under alternative compensation schemes, our analysis of psychological factors included in the “Execution Factors” section does provide some hints about the effects. We mention the issue of compensation schemes here because our experience has shown that it does directly impact the time it takes to clean a home.
When evaluating alternative compensation schemes, operators typically choose among alternatives situated on a spectrum, extending from the scheme of paying a pure percentage of revenue by house to each worker, through to the scheme on the other end of the spectrum, paying a pure hourly wage based on start and end time for the day.
While there are loads of exceptions, generally, the pure percentage scheme is associated with low-cost / low-quality operators, while the pure hourly wage scheme is associated with high-cost / high-quality service providers.
Many companies mix elements of both to arrive at a hybrid pay system. There is no single right answer or standard when it comes to pay schemes. The important aspect of choosing a scheme is to consider the Company’s position vis-a-vis its specific customer target market. A high cost provider targeting value conscious customers may fail to earn a profit, paying employees hourly. A low cost service provider targeting quality conscious customers may fail to retain customers, paying employees on pure percentage basis.
Also worth mentioning with respect to compensation schemes with an hourly pay element is the question of whether drive time is included in hours worked or not. Excluding it can have the effect over time of contracting the Company’s effective service area, if employees are given a lower incentive to provide exemplary service to far-flung customers, based on the penalty in terms of lost pay associated with the longer travel times for such customers.
Work Scope: It might seem too obvious to mention, but it takes more time to clean a house thoroughly than it takes to clean it fast. The reason we bother mentioning it is because any company which uses the HCA Pricing Tools has to consider how their company compares to the test company. As a comparison, for the dataset used in this analysis, the scope favors thoroughness over speed. So if you are the fastest cleaner in the West, you might want to consider tweaking the formula downwards a bit to yield lower prices and allow for this difference.
Man-Minutes and Employee Training: Our experience shows that employees can be trained to clean more efficiently. There are marginal returns in doing so. Most of the benefits can be derived from having a Team Leader demonstrate the basics through on the job training during a new employee’s initial days in the field. As a result, the HCA Training Designer is generally used to educate professional cleaners about company policies, primarily with a view to avoiding damages and losses.
Man-Minutes and Employee Churn: Experienced employees have the ability to clean faster than less experienced employees, so employee churn can significantly impact cleaning times.
Arrival Time – The too much work syndrome: our experience shows that employees work towards returning to their homes at a predictable hour each day. Often, this can relate to their children’s school or daycare schedules and associated constraints. Arrival time at the customer’s home and revenue per hour are used to identify those days during which employees might determine they have too much work. Our experience shows that on such days, employees simply work faster, or cut corners, to contain the work day within the regular work hours, resulting in higher efficiency but more customer complaints.
Arrival Time – the Not-enough-work Syndrome: further to the point above about predictable return times, our experience shows that employees tend to stretch the allotted work to meet their own expectations for return times. This can easily be accommodated in a legitimate and subtle way just by cycling in a little extra cleaning for each house, “Let’s clean baseboards today for both of these houses, since we had a cancellation.” While it may sound faultlessly virtuous, it does beg the question about the condition of said baseboards in the event of no cancellations. Most importantly, an operator can fatally compromise profits by allowing the “not enough work syndrome” to regularly prevail.
Man-Minutes and Central Dispatch: Central dispatch can significantly contribute to the elapsed time with respect to the overall work day, particularly for drive time. The advantage of central dispatch is in schedule balancing, control, and provisioning. Most scaled maid services suffer the marginal time associated with central dispatch.
Team Size and Travel Time: a significant point about team size which has not been captured in our analysis relates to team size and travel time. While this may be obvious to an experienced operator, it is, nevertheless, sufficiently important in terms of its economic effect on a house cleaning company to require some discussion. In order to optimize scheduling and regularly make profitable decisions about team size, an operator must factor in drive time.
In determining the optimal team size, an operator must continuously consider the ratio of clean time to travel time for each team. A consideration of this ratio will result in the assignment of the fewest persons possible to barely finish the work for far flung assignments.
On days which involve some degree of slack, an operator would provide the lightest work load to those teams with the lowest travel time, thus minimizing total drive time for all teams.
As an operator, minimizing drive time is important even if you don’t pay for drive time. While the direct cost of drive time can be offloaded to employees by paying them a strict percent of revenue, generally it will be borne indirectly by the company, since the lower profits for assignments involving higher drive times will tend towards sub-par service, resulting in higher churn rates by such clients. Under such a pay scheme, a company’s geographic service area most often settles naturally into an equilibrium, as employees offer better service to clients with lower drive times and worse service to far flung clients.
We call this process of employees subconsciously culling less desirable clients, the “Cleaner Snip.” While it has a sharp sound to it, in practice it is almost never heard by an operator. It occurs silently and methodically, and the effect is most often marked up as another of life’s unsolved mysteries.
Compensating employees for drive time by whatever method may allow an operator to build a schedule around a far flung house. In contrast, under a pay scheme whereby employees are penalized for distance, the Cleaner Snip can occur before an operator even has the opportunity to build a full day’s schedule for a team around a new distant assignment. This can make it almost impossible to expand the Company’s geographic market beyond the default service area.
One of the incentives of operating an independent house cleaning company instead of a franchise maid service is to exploit a larger geographic market area. The geographic service area for a franchise maid service company is fixed contractually to a relatively small area. This contractual boundary is typically significantly smaller than the default service area which might otherwise arise from the Cleaner Snip. This is why this issue of drive time is more important for independent house cleaning companies than it is for franchise maid service companies and is the reason a strict percent revenue pay scheme might be optimal for certain franchise maid services, while being inappropriate for some independent house cleaning companies, depending on the operators penchant for scaling.
Companies with excessive customer churn rates generally fail. That notwithstanding, their demise can be hurried by pricing initial cleaning assignments at or below their direct costs. Conversely, many successful house cleaning companies price initial cleaning assignments more aggressively because they are confident of their own ability to retain the customer. Churn rate affects a company’s cost structure in two ways:
A company with a higher customer churn rate has relatively higher new customer acquisition costs.
A company with a higher customer churn rate performs a relatively higher number of initial cleaning assignments, and has fewer expected regular cleaning assignments from which to recover any profit deficiencies which might inadvertently arise from initial cleanings.

16. Variables Considered, but Excluded

Cleaning Equipment and Supplies: For a professional house cleaning company, the costs of labor is so overwhelmingly important that the per assignment costs of cleaning equipment and supplies is simply lost in rounding. We’re not saying that it is not worthwhile to carefully manage such costs, but rather that we have chosen not to attempt to allocate them by assignment.
Bedrooms, Common Rooms, and Laundry Rooms: these areas of a house are highly correlated with square feet. Including statistics for these areas of a house provides almost no meaningful predictive value to the model.
Clutter Factor: measures the extent and orderliness of clothes, toys, and paper, and the impact such items have on Clean Times. A home cluttered with clothes, toys or paper can take significantly more time to clean than an uncluttered home. While the concept seems to have merit based on our experience, we found it very difficult to apply because users invariably mixed clutter factor with Lifestyle Factor. Indeed, this was clearly demonstrated during the statistical analysis – the factors proved to be highly correlated and the clutter factor was shown to have a low predictive value, and has been removed from the equation.
Decoration Factor: measures the extent and intricacy of a Household’s decoration and the impact the household’s decoration has on Clean Times. An ornately decorated home takes longer to clean, particularly to dust, than an undecorated home. A minimalist would have a negative Decoration Factor, while an art collector might have a high Decoration Factor. While the concept seems to have merit based on our experience, the factor was shown to have an unacceptably low predictive value, so it has been removed from the equation.

17.Hourly Rates

The default rates used in the HCA pricer are not suggested rates. As with all the default values, they are set only to allow the pricer to work if a user should inadvertently fail to input a value.
Each operator sets his or her own targeted hourly rates for initial cleaning assignments and recurring assignments by taking into account a number of unique factors which may include their local pay rates, overhead costs, and target profit margins.
For an operator of a professional house cleaning company, setting an official target hourly rate at a level which is higher than the Company’s minimum acceptable rate, if flexibly applied, can allow the Company to price assignments in a way which appeals to a wider range of consumers, while still allowing it to capture the maximum total consumer surplus. To appreciate this concept, consider the figure below. The Company’s own cost structure and constraints necessitate setting prices at the blue line to avoid losing money. To remain in business and earn a producer surplus (a profit), an operator can fix its price at the Equilibrium price. But to optimize profits, an operator might retain some flexibility in pricing. This is can be done by taking into account each consumer’s price sensitivity (yet another reason to perform in-home visits, instead of quoting by phone!) when deciding whether or not to provide a quoted fixed price above the equilibrium price, or at the equilibrium price.


18.How to Use The HCA Pricing Model

HCA Desktop Pricer: the HCA Desktop Pricer is a web application which applies the formula derived from the HCA statistical analysis. Taking it one step further, the HCA Desktop Pricer allows the operator of a house cleaning company to customize the results to the Company’s own requirements, by incrementing or decrementing the coefficient values to arrive at results which may more closely match the Company’s own experience. Of course, using the HCA Pricer Tools with the default values will yield results which are associated with the results of the statistical study.

The free version of the HCA Desktop Pricer can be accessed by anyone at The free version allows a user to customize coefficient values, set hourly rates, input values for a single assignment, and generate an estimate.

A company with a higher customer churn rate has relatively higher new customer acquisition costs.

HCA Website Pricer: your customers can use the HCA Pricer by accessing it via your company website. You control the price per hour rates, the estimated conversion rate from a two-week time to the initial clean time, and the minimum price variables by accessing the HCA Desktop Pricer from inside your login. Customers open the Company’s website and complete a questionnaire which requires the Customer to evaluate their own household and estimate their own HCA Pricer Attribute values. When they submit the Attribute values, the website automatically calculates (based on price per hour rates established by the company, combined with the HCA Pricer Formulae) and transmits online in real time ball-park price estimates for cleaning their home.


HCA Remote Handheld Pricer: the Handheld Pricer is presently under development and is expected to be available for subscription by HCA Members in February 2007.

For inquiries or comments about the HCA Pricing Tools, or if you would like your company to be included in future statistical studies, please phone Hanna or Chris toll-free at 303-935-7287, or send us an email at (please write “HCA Pricing Tools” in the subject line).

About the Authors:

Supadej Ksrisuwan:Born in 1981, Bangkok, Thailand.After completing his high-school education at Assumption College, he entered Chulalongkorn University in the Department of Electrical Engineering and received his B.Eng. in 2003. Â Supadej joined Inetasia Ltd., Thailand, in the position of software support, before deciding to switch fields from engineering to economics and finance. He received his Master`s from University of Colorado at Denver in 2006. With his 2-year experience as a research assistant in the field of microeconomics, he provides statistical analysis in this paper.

Chris Lude:Received his MBA from Tuck Business School in 1994.Prior to that time, Chris worked as a CPA for Price Waterhouse.Subsequently, he held several management and finance positions, including at Bear Stearns in investment banking, and AIG in fund management.
Chris founded Denver Concierge, a regional house cleaning company in 1999. Denver Concierge grew over five years to become the largest independent house cleaning company in the United States.In 2006, Denver Concierge was sold.The transaction made the “Top Five Transactions” list for one of Denver’s leading business brokerage companies.With his experience operating a house cleaning company, Chris provides industry analysis in this paper.
In 2004, Chris founded Ripple Applications, a leading web development company which offers, on a subscription fee basis, iDispatch scheduling software, a web-based business portal application for house cleaning and other service companies.Ripple Applications has offices in Denver, Cairo, and Gothenburg.

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