# Forecasting From a Single Observed Performance with a Positive Relationship between Input and Output Ted Mitchell.

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Forecasting From a Single Observed Performance with a Positive Relationship between Input and Output Ted Mitchell

Four Virtues of a Two-Factor machine of Presenting a Marketing Performance as the Performance of a Two-Factor Machine 1) Makes it easy to explain the job of the marketing manager to non-marketers 2) Presents an abstract process in concrete terms of a conversion process 3) Managers can visualize the job of doing the diagnostics on a machine to explain performance and recommending changes for improving performance 4) Managers can forecast the likely changes in a machine’s output given changes in the machine’s inputs and construction

The single recorded performance Of a Two-Factor marketing machine in which there is a positive and direct relationship between the Input and the output Has the virtues of 1) being easy to visualize 2) provide the basis for a simple forecast

A Single Performance Provides Simple Model for Forecasting A single observation provides a Fully Calibrated Performance A model for forecasting requires only the calibrated conversion rate, r Proposed amount of Input for forecasted period Input, I = O/r20 servers Conversion rate r = O/I r = 2,000/20 r = 100 cups per server Output, O = r x I2,000 cups sold

A Single performance Provides Simple Model for Forecasting A single observation provides a Fully Calibrated Performance A model for forecasting requires only the calibrated conversion rate, r Proposed amount of Input for forecasted period Input, I = O/r20 servers Conversion rate r = O/I r = 2,000/20 r = 100 cups per server Output, O = r x I2,000 cups sold

A Single performance Provides Simple Model for Forecasting A single observation provides a Fully Calibrated Performance A model for forecasting requires only the calibrated conversion rate, r Proposed amount of Input for forecasted period Input, I = O/r20 servers Proposed Input, I* = 18 servers Conversion rate r = O/I r = 2,000/20 r = 100 cups per server Output, O = r x I2,000 cups sold Forecasted Output, O* = r x I* O* = 100 x 18 O* = 1,800 cups

A Forecast is easily seen in the slope- origin presentation The Forecasted Output must lie on the line that connects the origin, (0, 0) to the observed point of performance,, (20, 2,000)

Forecast Presented as a Slope-Origin X-axis 0, 0 X Y-axis o Observed point P = (X, y) Y Slope of Line Y/X Slope of Line Y/X Origin 0, 0 Forecast must lie along the line

Presentation of a Forecast Input on the X- axis 0, 0 Input, 20 servers Y-axis o Observed Point, P = (20, 2,000) Output, 2,000 cups Slope of Line, r = 100 cups per server is the conversion rate

Presentation of a Forecast Input on the X- axis 0, 0 Proposed Input, 18 Servers Y-axis o Observed Point, P = (20, 2,000) Forecasted Output, 1,800 cups Slope of Line, r = 100 cups per server is the conversion rate 20 20 cups

Most Forecasts Using a Slope-Origin Equation Output, O* = (calibrated r) x Input, I* From a single observation are not very accurate More Observations of a machine’s performancecan improve the forecast’s accuracy

Any Questions about Using a single recorded performance to build a forecasted outcome from a proposed input that has a direct and positive relationship with the quantity customers are demanding

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