Portfolio Analysis and Sales Forecasting
  • Portfolio Analysis and Sales Forecasting
  • Portfolio Analysis and Sales Forecasting
  • Portfolio Analysis and Sales Forecasting
  • Portfolio Analysis and Sales Forecasting
  • Portfolio Analysis and Sales Forecasting
  • Portfolio Analysis and Sales Forecasting
  • Portfolio Analysis and Sales Forecasting
  • Portfolio Analysis and Sales Forecasting
  • Portfolio Analysis and Sales Forecasting
Originally published: 20/10/2019 12:49
Last version published: 12/12/2019 07:55
Publication number: ELQ-16605-2
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Portfolio Analysis and Sales Forecasting

A set of templates to describe the current sales performance and to make predictions and sensitivities for the future

Description
This file is a very practical set of tools providing a mathematical basis for:

1) Building a full picture of past sales for the whole portfolio and by feature:

– calculating total, average, median sales and other statistics giving a full picture of the historic sales performance
– analysing variances between the periods and explaining deviations between budget and actual (price, mix and volume effect)
– drawing seasonality patterns on a cycle plot

2) Making educated predictions of future sales and portfolio performance:

– How to build trends and why commonly used CAGR rarely gives reliable future estimates. CAGR takes only two points (beginning and end of the analysed period) and does not take into account possible fluctuations between those points.
– How to analyse and forecast seasonalities based on monthly, quarterly or weekly data. Explains how to "clear" the data from seasonal patterns and, in the opposite case, to apply seasonalities to forecasted trend data
– How to measure historic volatilities and translate them into model scenarios with a desired level of confidence. In this part we will calculate the standard deviation for quarterly data and see how it is extrapolated to the annual basis.
– How to tie volatilities to a timeline, measure standard error and set up scenarios based on that data. If we want to estimate volatility for e.g. exchange rates over the year, this is not so straightforward as we need to take into account this is a time series, not a randomly scattered data.

This Best Practice includes
1 Excel file, 1 PDF file

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Further information

Make accurate predictions and possible volatilities in financial models based on historic data

Making estimates of future numbers based on historic data

If there are indicators of future performance other than purely historic data, statistics might not be the best tool for making forecasts

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