Past Issue

Demand - Estimating It Is Complicated

By Robert Roman

11/01/15

Potential sales are total amount of sales that can be achieved in an area if all the people living in that area shopped within that area. For example, demand for gallons of gasoline in an area can be calculated if retail sales volumes (dollars), average price of gas, and population in that area are known.

Trying to determine where these dollars may get spent is complicated because roads traverse areas. Since people have choice, roads allow for sales leakage if consumers living in the area shop outside the area or surplus if consumers living outside the area shop within the area.

Roads allow consumers to make home-, work-, and shopping-based trips, which further complicate determining where dollars may get spent because trip purpose varies by time of day and distance.

Experience shows consumers do not drive long distances for conveniences. So, two important characteristics of location and demand for convenience stores and gas sites (including car wash) are demography and traffic count.

Because people tend to spread their gas purchases among several stations that are proximate, c-stores are normally located on sites within two miles of large numbers of households and daytime businesses.

Since only a portion of demand inherent in a stream of traffic will seek fulfillment at any particular store, stores are normally located on sites with high volumes of pass-by traffic so that owners can count on a viable percentage of that traffic.

Demand can be affected by income and consumer prices. Before high gas prices and recession, the wash/gas rate for c-stores was one wash bought for every 75 gallons of gas sold.

Today, the rate is one wash for every 150 gallons sold.

People’s age affects demand. According to Department of Labor statistics, heads of household, 55 to 64 years old spend 26 percent more on auto maintenance than people under 25 years and those over 65.

Vehicle age affects demand. As vehicles age from “new to five years” to “21 to 25 years,” the average expenditure for car cleaning drops from $10.85 to $3.42. Moreover, frequency of expenditure for this range of vehicle-years drops as well.

Sex affects demand. For example, researchers find single moms mostly look at prices. However, moms view routine auto maintenance (including cleaning) as an essential item (functional need) rather than non-essential (emotional need).

Weather can turn demand for car washing on and off like a faucet. When it’s well below zero and not much snow, car wash business may be non-existent. Above zero, frequent snow, and slushy roads makes for a wet experience and people don’t bother. However, on those nice dry days, even with slush and melting snow along road edges, peak hourly wash demand may be three to four times greater than average.

Special occasions affect demand. For example, when I operated a car wash, we were consistently busier on and around Mother’s and Father’s Day, Thanksgiving, Christmas, New Year’s, Valentine’s Day, and Easter.

This underscores the difficulty in trying to determine demand. There are simply a lot of things to consider.

For example, capture rate is often used to estimate expected volume for undeveloped property. This method offers an inexpensive and rapid solution but is overly simplistic and subjective.

The relationship between volume and traffic cannot explain differences that may exist between sites in terms of access (divided or undivided), visibility, curb appeal, and other factors that are thought to influence motorists.

Analogue models are preferable because of the principle of similarity. If the characteristics of a new site are consistent with analogue stores, expected sales volume for the new site would be the average of that subset. However, analogues aren’t much use for solving problems like how competitive changes may affect a car wash or how much volume to expect the week before Thanksgiving or on the second Wednesday in March.

Here, other techniques are necessary to form some expectation of demand. Some companies use historical data (year-to-year) or average sales volumes to predict demand for scheduling purposes. When I operated a car wash, I even developed a model to predict sales based on weather forecasts. In the final analysis, perhaps a Yogi Berra-ism would say it best: How well you do depends on who shows up.

 

Bob Roman is president of RJR Enterprises – Consulting Services (www.carwashplan.com). You can reach Bob via e-mail at bob@carwashplan.com.



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