Now that we've covered capacity-related issues, let's look at the other side of the equation. To control variations in demand for a particular service, managers need to determine the answers to a series of important questions about the patterns of demand and their underlying causes. As you think about some of the seemingly "random" causes of fluctuations in demand, consider how rain and cold affect the use of indoor and outdoor recreational or entertainment services.
Then reflect on how heart attacks and births affect the demand for hospital services. Imagine what it's like to be a police officer, firefighter, or ambulance driver you never know exactly where your next call will come from or what the nature of the emergency will be. Finally, think about the impact of natural disasters like earthquakes, tornadoes, and hurricanes, not only on emergency services but also for disaster recovery specialists and insurance firms.
Random fluctuations are usually caused by factors beyond management's control. Where relevant, it's useful to record weather conditions and other special factors (a strike, an accident, a big convention in town, a price change, launch of a competing service, etc.) that might have influenced demand. Detailed market analysis may sometimes reveal that a predictable demand cycle is concealed within a broader, seemingly random pattern.
For example, while a convenience store might experience wide swings in daily patronage, research may indicate that a core group of customers visits every weekday to buy staple items such as newspapers, candy, and lottery tickets.
A repair and maintenance shop servicing industrial electrical equipment may already know that a certain proportion of its work consists of regularly scheduled contracts to perform preventive maintenance. The balance may come from "walk-in" business and emergency repairs. While it might seem hard to predict or control the timing and volume of such work, further analysis could show that walk-in business was more prevalent on some days of the week than others and that emergency repairs were frequently requested following damage sustained during thunderstorms (which tend to be seasonal in nature and can often be forecast a day or two in advance).
Analyzing Demand by Market Segment
No strategy for smoothing demand is likely to succeed unless it's based on an understanding of why customers from a specific market segment choose to use the service when they do. For example, it's difficult for hotels to convince business travelers to remain on Saturday nights since few executives do business away from home over the weekend. Instead, many hotels promote weekend use of their facilities for conferences or pleasure travel. Attempts to get commuters to shift their travel to off-peak periods will probably fail too, since such travel is determined by people's employment hours.
Instead, efforts should be directed at employers to persuade them to adopt flextime or staggered working hours. Resort areas like Cape Cod may have good opportunities to build business during the "shoulder seasons" of spring and fall (which some consider the most attractive times to visit the Cape) by adapting the mix and focus of services to appeal to new target segments. For example, different attractions could be promoted like hiking, bird watching, visiting museums, and looking for bargains in antique stores.
Keeping good records of customer transactions helps when it comes to analyzing demand patterns by market segment. Computer-based services can track customer consumption patterns by date and time of day automatically and enter them into a company's database. For example, Harrah's recently installed magnetic card readers at its slot machines and gaming tables to track casino customers' gambling behavior. Customers are issued Total Gold cards that they swipe through the magnetic readers before gambling to earn reward points that are redeemable at any Harrah's location.
This allows the company to collect accurate information about when customers visit its casinos and how much they spend by day, by week, and by trip. If each customer transaction is recorded separately and backed up by detailed notes (as in a medical or dental visit, or an accountant's audit), then the task of analyzing demand by market segment is greatly simplified. In subscription and charge account services, when each customer's identity is known and itemized monthly bills are sent, managers can gain immediate insights into usage patterns.
Some services, such as telephone and electrical, even have the ability to track subscriber consumption patterns by time of day. Although this data may not always yield specific information on the purpose for which the service is being used, it is often possible to make informed judgments about the volume of sales generated by different user groups.
Sometimes it is in a firm's best interest to discourage demand from certain types of Customers or at least to encourage these customers to use the services at no peak times. Some requests for service are inappropriate and make it difficult for the organization to respond to the legitimate needs of its target segments. Discouraging undesirable demand through marketing campaigns or screening procedures may help keep peak demand levels within the service capacity of the organization.
Multiple Influences on Demand
Periodic cycles influencing demand for a particular service can vary in length from one day to one year. In many instances, multiple cycles may operate simultaneously. For example, demand levels for public transportation may differ by time of day (highest during commute hours), day of week (less travel to work on weekends but more leisure travel), and season of year (more travel by tourists in summer).
The demand for service during the peak period on a Monday in summer may be different from the level during the peak period on a Saturday in winter, reflecting day-of-week and seasonal variations jointly. Figure shows how the combination of four time-of-day periods (morning peak, midday, afternoon peak, evening/night), two day-of-week periods (weekday, weekend), and three seasonal periods (peak, shoulder, off-peak) can be combined to create 24 different demand periods.
In theory, each of these might have its own distinct demand level (at a given price) and customer profiles (with resulting differences in needs and expectations). But in practice, analysis might show close similarities between many of the periods. This would make it possible to collapse the framework into a total of three to six cells, each requiring a distinct marketing treatment to optimize the use of available capacity and obtain the most desirable customer mix.
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