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Developing the sales campaign plan
The first step in the plan is to enter all the headings so that there is a structure for the model. Some sample data should be entered in order that the logic for the formulae can be checked as it is being developed.
The number of enquiries received each week after the initial promo- tion is an estimate. In this example it is assumed that the effects of the promotion increase over a five-week period and then begin to drop off until there are only a few enquiries at the end of the ten weeks.
The estimated numbers of sales presentations are assumed to be linked with the numbers of enquiries. In this example it is esti- mated to be between 30 and 40% of the previous week’s enquiries.
The values in the model are derived by a random number generator used in conjunction with a one-period lag on the number of enquiries. The formula entered into cell D13, which is copied for the remaining weeks is:
⫽INT(RAND()*0.1)⫹0.3)*B12
TheRAND function in the above formula generates a random num- ber between 0 and 1. However, by manipulating the result as shown in the above formula the result will always be between 30 and 40%
of the previous week’s number of enquiries.
The problem with the RANDfunction is that it is re-evaluated every time the spreadsheet is recalculated and therefore different values will be returned. Therefore having produced a series of estimated Figure 11.12 Sales campaign appraisal model
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values these cells should be converted to values only. This is achieved by copying the range with the Edit Copy command and then selecting Paste Special Values. This removes the underlying formula leaving only the results in the cells.
The units sold are calculated by multiplying the number of sales pre- sentations by the conversion to sale percentage. The following formula is required in cell F13, which can be copied for the remaining weeks:
⫽INT(D13*$E$7)
TheINT function has been incorporated into the formula to ensure that the result is rounded down to a whole number.
The cost per presentation is calculated by multiplying the cost per sales presentation by the number of presentations, adding that to the total cost of promotion and dividing by the cumulative sales presentation. The formula in cell H13, which is copied for the remaining weeks is:
⫽($E$4⫹(D13*$E$6))/E13
The marketing cost per sale is the promotion cost divided by the cumulative sales, which means the following formula is required in cellI13:
⫽$E$4/G13
The total costs are calculated as the promotion costs plus the cost of the sales presentations that week plus the production costs for units sold. The formula required in cell J13 is therefore:
⫽$E$4⫹($E$6*D13)⫹(G13*$E$9)
The total revenue is the number of units sold multiplied by the rev- enue per unit and the profit on the campaign is therefore calculated as the total costs less the total revenue.
Summary
In this chapter a number of different business plans have been devel- oped in order to demonstrate some of the applications for which a spreadsheet can be used. It is hoped that readers will find some of the models directly applicable but some of the techniques and functions shown can also be applied in a number of complimentary areas.
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What-if Analysis
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Good judgment is usually the result of experience. And experi- ence is frequently the result of bad judgment.
– R.E. Neustadt and E.R. May,Thinking in Time, 1986.
Introduction
What-if analysis may be defined as the technique of asking specific questions about the result of a change or of a series of changes to assumptions in a model or a business plan.
What-if analysis has been performed manually for decades before the arrival of computers. In the spreadsheet environment, however, it is a direct product of the fact that once a model or plan has been entered into the computer, it may be recalculated again and again.
It allows the user to change assumptions concerning input data or input relationships, and to recalculate a model to see the impact of these changes on critical output values.
Typical what-if questions might be to ask what effect will a 2%
increase in direct labour costs have on profit and return on invest- ment? What effect will a further 30 day delay in receiving cash from the debtors have on the overdraft and/or return on investment?
What-if questions may be considered one at a time, or several at a time. If it is necessary to investigate the effect of two simultaneous changes in the assumptions, then it is usually advantageous to also consider these changes in isolation, i.e. one at a time, so that their individual effects, as well as their joint effects, will be known.
Irrespective of whether single or multiple changes to input are made, several factors or objectives will usually be monitored.
In addition to what-if analysis, there are two other related concepts which should also be considered. These are goal seekingorbackward iterationandsensitivity analysis.
Goal seeking is a technique whereby a model calculates the value of an input variable that is required in order to achieve a stated output objective. For example, using goal seeking the system could calcu- late the level of sales required for a return on investment of 25%.
Thus the goal-seeking procedure requires an input variable, which is usually considered the models’ output, and the result is the value for a variable that is normally input to the model. Excel has a built- in command for this, which will be discussed later in this chapter.
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Sensitivity analysis is a technique that ascertains the relative impor- tance of specified input variables in a business scenario or plan. This is achieved by calculating the result of a number of relatively small changes in the specified input factors on the objective function, or output of the business model. These input and output changes are then compared to ascertain which variables have the greatest impact.
The user of sensitivity analysis is concerned to establish whether a 5% increase in raw material costs, or a 2% increase in labour costs will have the worse impact on the profit of the business.
Sensitivity analysis and what-if analysis are quite different tech- niques, although the terms are often used interchangeably.