Sales Forcasting Flashcards
What is sales forecasting
predicting future demand by anticipating what consumers are likely to do in a given set of circumstances.
Quantitative sales forecasting techniques
time series analysis
use of market research data
Extrapolation
Using past experience or past business data to forecast future sales is called extrapolation. Extrapolation involves making statistical forecasts by using historical trends that are projected for a specified period of time into the future. It is only used for time-series forecasts.
Qualitative sales forecasting methods
the Delphi technique
brainstorming
intuition
expert opinion
These factors should be considered when carrying out sales forecasting. They include:
Economic factors
Consumer factors
Competition factors
Economic factors
such as unemployment levels, inflation, interest rates, exchange rates, economic growth. For example, if there is an unexpected rise in inflation then this will affect consumer spending and will have an impact on sales forecasts. In this case it will lower the sales forecast. A change in any of these economic variables could reduce the accuracy of the sales forecast.
Consumer factors
consumers’ tastes and fashions are constantly changing, and businesses try to anticipate these changes through market research. However, consumers are notoriously unpredictable, and their preferences can change quickly. Changes in consumer behaviour can be short term or long term. A long-term trend is easier to identify and to take into account when sales forecasting.
Competition factors
a business cannot control the actions of their competitors. However, their actions will affect not only the present business performance but the future business performance too. Competitors will have their own strategies and plans for the future and any significant action by competitors could reduce the accuracy of sales forecasting.
When to use quantitative forecasting methods
Quantitative forecasting methods are used when there is historical data available. A number of different models can be used to forecast future events. Quantitative methods rely heavily on data and are objective.
Time series analysis
Time-series analysis uses evidence from past sales records to predict future sales patterns. There are several methods of time-series analysis that can be used:
Seasonal analysis
Trend analysis
Cycle analysis
Random factor analysis
Seasonal analysis
sales are measured on a monthly or weekly basis to examine the seasonality of demand. For example, the sales of ice cream will be higher in the warmer seasons and lower in the colder seasons or according to daily weather changes.
Trend analysis
this focuses on long-term data, which has been collected over a number of years. The objective is to determine the general tendency of sales - rising, falling or stagnant.
Cycle analysis
as with trend analysis, long-term figures are used but now the objective is to examine the relationship between demand levels and economic activity. For example, by asking the question ‘what is the relationship between demand for the product or products and the stage in the economic or business cycle?’.
Random factor analysis
this method of analysis attempts to explain how unusual or extreme sales figures occur. For example, if sales of ice cream double for a two-week period, then could this be explained by weather conditions, rather than an effective advertising campaign? Random factor analysis therefore attempts to provide explanations for unusual or abnormal sales activity.
Market analysis
A detailed examination of features of a market such as market size and sales, to predict future trends