Topic 4 Marketing Marketing Planning HL ONLY

Learning Objectives Analyse sales-forecasting methods and evaluate their significance for marketing and resource planning

Sales forecasting What do you need to know to forecast accurately? Why is it important to get it right? What factors can impact your sales forecast? How can you make your forecast as accurate as possible? How might forecasting impact other areas of the business?

Sales Forecasting Predicting future sales levels and sales trends

Quantitative Sales Forecasting Methods – Time Series Analysis Moving averages Extrapolation These methods of sales forecasting is based on past sales data. These are referred to as a “time series”

Moving Averages Allows the identification of underlying factors that are expected to influence future sales These are the trend, seasonal variations, cyclical variations and random variations Once they have been identified, then short term sales forecasts can be made Trend – Underlying movement of the data in a time series Seasonal Variations – Regular and repeated variations that occur in sales data within a period of 12 months or less Cyclical Variations – Variations in sales occurring over periods of time of much more than a year, they are related to the business cycle Random Variations – Occur at any time and cause unusual and unpredictable sales figures

Moving averages Plot the graph of data – Reflection – what can we interpret from this data Smooth the data – moving averages What can we interpret? Plot the moving average data Review moving averages as a model – pros and cons

Sales Data Sales £000 3 month moving average January9 February12 March15 April15 May18 June21 July9 August18 September21 October24 November12 December24

Sales Data Sales £000 3 month moving average January9 February12 March15 April15 May18 June21 July9 August18 September21 October24 November12 December24 Task 1 Plot the data on graph paper sales months What can we interpret from this data? Can you see a trend?

Calculate the ‘moving average’ This looks at several periods at a time and averages out the data

Sales Data Sales £000 3 month moving average January9 February12Jan + Feb + Mar / 3 March15Feb + Mar + Apr / 3 April15Mar + Apr + May / 3 May18 June21 July9 August18 September21 October24 November12 December24 Write this in the centre of your table and the value in the far right column

Sales Data Sales £000 3 month moving average January9 February12( )/312 March15( )/314 April15( )/316 May18( )318 June21( )/316 July9( )/316 August18( )/316 September21( )/321 October24( )/319 November12( )/320 December24

Calculate the ‘moving average’ This looks at several periods at a time and averages out the data Hi/Lo figures are smoothed by the averaging process The underlying trend is clearer

Moving Averages Advantages Useful for identifying and applying the seasonal variation to predictions Reasonably accurate for short term forecasts in reasonably stable economic conditions Identifies the average seasonal variations for each time period and this can assist in planning Disadvantages Complex calculation Forecasts further into the future become less accurate

Examiner’s note Unlikely you would be asked to do this in an exam The data might be provided that show a trend and/or seasonal variations that have already been calculated Or you may be required to interpret a graph

Extrapolation Identify the trend and project forwards Only likely to be effective if market conditions continue to develop in the future as they have done in the past Risky in markets where technology is developing rapidly sales months x x xx x x x x x x x PresentFuture trend Extrapolated trend Kodak and digital cameras CD manufacturers and MP3 players Beef and BSE