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Unit 7: Market and Demand Analysis

5. A ………………… is one method of market research that is based on questioning an Notes
audience or segment of the market.
6. Conducting market research through a focus group is a valuable way to obtain
………………… about a product or a service.

7.4 Demand and Forecasting

Demand forecasting and estimation gives businesses valuable information about the markets in
which they operate and the markets they plan to pursue. Forecasting and estimation are
interchangeable terms that basically mean predicting what will happen in the future. If businesses
do not use demand forecasting and estimation, they risk entering markets that have no need for
the business’s product.

Demand forecasting is the area of predictive analytics dedicated to understanding consumer
demand for goods or services. That understanding is harnessed and used to forecast consumer
demand. Knowledge of how demand will fluctuate enables the supplier to keep the right amount
of stock on hand. If demand is underestimated, sales can be lost due to the lack of supply of
goods. If demand is overestimated, the supplier is left with a surplus that can also be a financial
drain. Understanding demand makes a company more competitive in the marketplace.
Understanding demand and the ability to accurately predict it is imperative for efficient
manufacturers, suppliers, and retailers. To be able to meet consumers’ needs, appropriate
forecasting models are vital. Although no forecasting model is flawless, unnecessary costs
stemming from too much or too little supply can often be avoided using data mining methods.
Using these techniques, a business is better prepared to meet the actual demands of its customers.

7.4.1 Understanding Consumer Demand

Demand Anomalies

In demand forecasting, as with most analysis endeavors, data preparation efforts are critical.
Data is the main resource in data mining; therefore it should be properly prepared before
applying data mining and forecasting tools. Without proper data preparation, the old adage of
“garbage in, garbage out” may apply: useless data results in meaningless forecast models.
Major strategic decisions are made based on the demand forecast results. Errors and anomalies
in the data used to create forecast models may impact the model’s ability to forecast. These
errors give rise to the potential for bad forecasts, resulting in losses. With properly prepared
data, the best possible decisions can be made.
There are several sources for problems with data. Data entry errors are one possible source of
error that can adversely affect the demand forecasting efforts. Basic statistical
summaries and graphing procedures can often make these types of error apparent. Artificial
demand shifts are another error source. For example, consumer response to a promotional offer
may temporarily boost sales of an item. Without a similar promotion, the same increase cannot
be expected in the future. Some uncontrollable factors have the ability to influence consumer
demand as well. A factor such as economic conditions may tend to impact demand. An unusually
mild winter will likely cause lower energy demand. Accounting for these influences of demand
can help fine tune forecast modelling.

Seasonal Fluctuations

Every business sees seasonal fluctuations. Holidays and weather changes influence products
and services that consumers want. While it is extremely important to account for how seasonal

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