Sales forecasting is a difficult area of management. Most managers believe they are good at forecasting. However, forecasts made usually turn out to be wrong!
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Reasons for undertaking sales forecasts
Businesses are forced to look well ahead in order to plan their investments, launch new products, decide when to close or withdraw products and so on. The sales forecasting process is a critical one for most businesses. Key decisions that are derived from a sales forecast include:
There are two major types of forecasting, which can be broadly described as macro and micro:
Macro forecasting is concerned with forecasting markets in total. This is about determining the existing level of Market Demand and considering what will happen to market demand in the future.
Micro forecasting is concerned with detailed unit sales forecasts. This is about determining a product’s market share in a particular industry and considering what will happen to that market share in the future.
The selection of which type of forecasting to use depends on several factors:
(1) The degree of accuracy required – if the decisions that are to be made on the basis of the sales forecast have high risks attached to them, then it stands to reason that the forecast should be prepared as accurately as possible. However, this involves more cost
(2) The availability of data and information – in some markets there is a wealth of available sales information (e.g. clothing retail, food retailing, and holidays); in others it is hard to find reliable, up-to-date information
(3) The time horizon that the sales forecast is intended to cover. For example, are we forecasting next weeks’ sales, or are we trying to forecast what will happen to the overall size of the market in the next five years?
(4) The position of the products in its life cycle. For example, for products at the “introductory” stage of the product life cycle, less sales data and information may be available than for products at the “maturity’ stage when time series can be a useful forecasting method.
Creating the Sales Forecast for a Product
1) The first stage in creating the sales forecast is to estimate Market Demand.
Definition – Market Demand for a product is the total volume that would be bought by a defined customer group, in a defined geographical area, in a defined time period, in a given marketing environment. This is sometimes referred to as the Market Demand Curve.
Thus market demand can be defined as:
Defined Customer Group: Customers Who Buy an Air-Inclusive Package Holiday
Defined Geographical Area: Customers in India
Defined Time Period: A financial year
Defined Marketing Environment: Strong consumer spending in India but overseas holidays affected by concerns over international terrorism
For example, you might calculate market demand of a holiday package as follows:
– Number of Customers in India: 1000 per financial year
– Average Selling Price per Holiday: Rs 25000
– Estimate of market demand: Rs 2.5 crores (customers x average price)
2) Stage two in the forecast is to estimate Company Demand
Company demand is the company’s share of market demand.
This can be expressed as a formula:
Company Demand = Market Demand * Company’s Market Share
For example, taking our package holiday market example; the company demand for First Choice Holidays in this market can be calculated as follows:
First Choice Holidays Demand = Rs 5 crore x 10% Market Share = Rs 50 lacs
A company’s share of market demand depends on how its products, services, prices, brands and so on are perceived relative to the competitors. All other things being equal, the company’s market share will depend on the size and effectiveness of its marketing spending relative to competitors.
3) Step Three is then to develop the Sales Forecast
The Sales Forecast is the expected level of company sales based on a chosen marketing plan and an assumed marketing environment.
A sales target (or goal) is set for the sales force as a way of defining and encouraging sales effort. Sales targets are often set some way higher than estimated sales to “stretch” the efforts of the sales force.
A sales budget is a more conservative estimate of the expected volume of sales. It is primarily used for making current purchasing, production and cash-flow decisions. Sales budgets need to take into account the risks involved in sales forecasting. They are, therefore, generally set lower than the sales forecast.
Obtaining information on existing market demand
As a starting point for estimating market demand, a company needs to know the actual industry sales taking place in the market. This involves identifying its competitors and estimating their sales.
An industry trade association will often collect and publish (sometime only to members) total industry sales, although rarely listing individual company sales separately. By using this information, each company can evaluate its performance against the whole market.
This is an important piece of analysis. Say, for example, that Company A has sales that are rising at 10% per year. However, it finds out that overall industry sales are rising by 15% per year. This must mean that Company A is losing market share – its relative standing in the industry.
Another way you can estimate sales is to buy reports from a marketing research firm such as IDC, or ORG-MARG. These are usually good sources of information for consumer markets – where retail sales can be tracked in great detail at the point of sale. Such sources are less useful in industrial markets that usually rely on distributors.
Here is a video by Marketing91 on Sales Forecasting.
Estimating Future Demand
So far we have identified how a company can determine the current position: Current Company Demand = Current Market Demand x Current Market Share How can future market demand and company demand be forecast?
Very few products or services lend themselves to easy forecasting. These tend to involve a product whose absolute level or trend of sales is fairly constant and where competition is either non-existent (e.g. monopolies such as public utilities) or stable (pure oligopolies). In most markets, total demand and company demand are not stable – which makes good sales forecasting a critical success factor.
A common method of preparing a sales forecast has three stages:
Prepare a macroeconomic forecast – what will happen to overall economic activity in the relevant economies in which a product is to be sold.
Prepare an industry sales forecast – what will happen to overall sales in an industry based on the issues that influence the macroeconomic forecast.
Prepare a company sales forecast – based on what management expect to happen to the company’s market share.
Sales forecasts can be based on three types of information:
- What customers say about their intentions to continue buying products in the industry
- What customers are actually doing in the market
- What customers have done in the past in the market
There are many market research businesses that undertake surveys of customer intentions – and sell this information to businesses that need the data for sales forecasting purposes. The value of a customer intention survey increases when there are a relatively small number of customers, the cost of reaching them is small, and they have clear intentions. An alternative way of measuring customer intentions is to sample the opinions of the sales force or to consult industry experts.
Time Series Analysis
- Many businesses prepare their sales forecast on the basis of past sales. Time series analysis involves breaking past sales down into four components:
- The trend: are sales growing, “flat-lining” or in decline?
- Seasonal or cyclical factors. Sales are affected by swings in general economic activity (e.g. increases in the disposable income of consumers may lead to increase in sales for products in a particular industry). Seasonal and cyclical factors occur in a regular pattern;
- Erratic events; these include strikes, fashion fads, war scares and other disturbances to the market which need to be isolated from past sales data in order to be able to identify the more normal pattern of sales
- Responses: the results of particular measures that have been taken to increase sales (e.g. a major new advertising campaign)
Using time series analysis to prepare an effective sales forecast requires management to:
- Smooth out the erratic factors (e.g. by using a moving average)
- Adjust for seasonal variation
- Identify and estimate the effect of specific marketing response
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