Sales Forecasting (Cambridge (CIE) A Level Business): Revision Note

The need to forecast sales

• Sales forecasts predict future revenues based on past sales figures

  • They are an important business planning tool

  • They can be used to identify trends in sales which can then be compared with the market as a whole

• They commonly focus on what will happen in the future to:

  • The volume and value of sales

  • The size of the market

  • Sales as a result of promotional activity

  • Sales as a result of cyclical factors

• Sales forecasts are an important tool to support planning and improve the validity of cash flow forecasts

• Businesses use sales forecasts to determine resource requirements, such as

  • How many staff are needed

  • How much stock is required

  • Whether capacity needs to be expanded or reduced

  • Whether equipment needs to be upgraded or replaced

  • How much and which type of finance is required

  • Whether and when promotional activity (e.g. advertising) is required

Time series analysis

• Raw sales data on its own may not be very useful for sales forecasting

  • It can be hard to spot patterns, trends or seasonal changes by looking at rows of data or, even, data in a graph, especially if sales are volatile

    • In the example below, it would be difficult to draw a line of best fit in order to extrapolate and make sales forecasts

Example of raw data plotted in a chart

• Businesses may uses time series analysis to smooth data, making it easier to predict likely sales in the future based on past performance

How to conduct a time series analysis

Step 1: Calculate moving totals

• In this case, a three-month moving average will be calculated

• Add together sales for January, February and March

• Place the total alongside March

• Now do the same for February, March and April

• Place the total alongside March

• Now do the same for March, April and May

• Place the total alongside May

• Repeat the process for each subsequent group of three months

Completed table: 3-month moving totals

Step 2: Calculate moving averages

• Divide March's moving total by 3

• Place the average alongside February

• Divide April's moving total by 3

• Place the average alongside March

• Divide May's moving total by 3

• Place the average alongside April

• Repeat the process for each subsequent group of three months

Completed table: 3-month moving averages

Step 3: Plot the moving averages and identify the line of best fit

• Include as many data points above the line as below the line.

  • Watch out for outlying data - if there is more than one outlier above the line, adjust your line of best fit upwards

  • Similarly, if there is more than one outlier below the line, adjust your line of best fit downwards

The line of best fit

Step 4: Extrapolate to forecast future sales

• Extend the line of best fit to predict future sales values

Extension of the line of best fit

• In this cases, the line of best fit is extended to June, allowing a prediction of 17,750 sales in that month

Qualitative sales forecasting

• Qualitative sales forecasting involves predicting future sales based on non-numerical data

  • This may include expert opinions, market experience or customer feedback, rather than historical figures or statistical analysis

• Qualitative forecasting relies on judgement, knowledge and experience rather than data modelling

• Methods include

  • Expert opinion

    • Insights from experienced managers, industry specialists or consultants

  • Sales force estimates

    • Forecasts made by sales staff based on their direct interaction with customers

  • Customer surveys

    • Using feedback from potential buyers to estimate future demand

• It is especially useful when a business

  • is launching a new product with no past sales data

  • is entering a new market or responding to major market changes

  • wants to understand consumer attitudes and future buying behaviour

Advantages of qualitative sales forecasting

• Useful when historical data is unavailable

  • This makes it ideal for use with new products or markets

• Incorporates expert judgement

  • Makes use of real-world knowledge and insights that numbers can’t always capture

• Flexible and adaptable

  • Can be quickly adjusted based on changing market conditions

• Encourages team involvement

  • Sales teams and managers contribute ideas, improving internal communication and morale

Disadvantages of qualitative sales forecasting:

• Subjective and prone to bias

  • Forecasts may reflect opinions rather than facts

• Less accurate than quantitative methods

  • Without numerical data, predictions may be vague or inconsistent

• Hard to measure reliability

  • It is difficult to test or prove whether a forecast is correct until actual sales occur

• Time-consuming

  • Gathering expert opinions or conducting surveys takes time and requires coordination

The impact of sales forecasting on business decisions

• Accurate sales forecasting can help a business plan ahead and make informed decisions across functional areas

• However, sales factors should be considered alongside other research on likely future business performance

Sales forecasting and functional decision making