pricing and revenue monte carlo forecast model

8/17/2019 Pricing and Revenue Monte Carlo Forecast Model

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Pricing and RevenuePricing and Revenue

Forecast Model Forecast Model

ultivariate Solutionsultivariate Solutions


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Applications and Goals Applications and Goals

• This pricing and revenue forecast model is used primarily to

determine optimal pricing of a product/service, and market share penetration of a given product at specific price points.

• Using that information, a model of revenue projection can be

built using simulation methods.

• The goal is to give clients the tools to make an informeddecision about pricing a given product or service based on the

highest likelihood of maximum revenue.


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The Monte Carlo Method The Monte Carlo Method • While it is a relatively straightforward matter to develop

confidence intervals for each of the revenue parameters taken

alone, what is really at issue is the confidence interval for projected differences taken jointly. Such a problem is bestaddressed through the use of so-called Monte Carlo methods.

• In a Monte Carlo simulation, a model in spreadsheet format is

set up and the cells whose values come from the survey resultsare identified (and which are therefore subject to samplingerror). For each of these cells, a distribution of possible valuesusing the appropriate means and standard errors is specified. A

series of trials is then generated, each one of which representsa possible outcome of the process.


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The Monte Carlo MethodThe Monte Carlo Method (cont.)(cont.)

• On average, most of the trials will yield values close to themean, since the distributions are typically bell-shaped and high

values for some parameters are likely to be offset by low valuesfor others. Expected revenue represents a best-case outcomegiven the survey results and confidence intervals associatedwith the individual parameters. But if the simulation is performed, say, 1,000 times, such a best-case outcome would

represent only a small fraction of the total trials.

• The 1,000 outcomes of these trials can be arrayed in acumulative distribution, so that the probability of percentage

growth falling into any given interval can be read off as thenumber of trials with outcomes in that interval.


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The Product / ServiceThe Product / Service• The product or service must be defined before the survey is

fielded. This model does not test different features for product

construction—that is left to tradeoff methodologies.

• This example is a cleaning foam that is used in the everydaycleaning of kitchens and bathrooms.

• During the survey the product’s features and benefits aredescribed in detail to the respondent. Possible sale points are:

– $3.19

– $3.49 – $3.79

– $4.09


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Constructing the Pricing ModuleConstructing the Pricing Module• Read the product/concept description.

• The pricing module on the questionnaire is constructed asfollows:

‘Given the features of this product, would be willing to pay $3.49 for it?’

If yes, then ask, “Would you pay $3.79 for it?”

If no, then ask , “Would you pay $3.19 for it?”

Continue with Yes until the respondent says, ‘No.’ Record the highest Yes.

Continue with No until the respondent says, ‘Yes.’ Record

that Yes. If Yes at top price ($4.09), record top price.

If No at bottom price, ($3.19), record No Answer.

• Rotate the starting points so that all price points begin equally.


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Predicted Market PenetrationPredicted Market Penetration• Cumulative line chart

– The assumption is that if a respondent is willing to purchase the

product at $4.09, he/she will be willing to purchase that product at$3.79 and all lower price levels.

Probability

68%

61%

74%

57%

$3.19 $3.49 $3.79 $4.09


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Input for Revenue Forecast Input for Revenue Forecast • Input based on market penetration estimates taken from survey.

•

To project revenue, information must be supplied by the client: – Size of potential market (range OK)

• Our market is said to be 2 million foam-using households, +-10%

– Fixed costs, e.g. production, marketing, dist., etc. (range OK)

• Fixed costs are said to be $2.5 million, +-10%

Revenue Forecast

Projected Size of Target Market (Households) 2,000,000

Projected Fixed Costs $2,500,000

Price Point $3.19

Market Penetration 74%

Number of Interviews 300

Projected Revenue $2,221,200.00


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Revenue Forecast Revenue Forecast Probability That Revenue Is Greater Than or Equal to Forecast

1626

2030

2094

2179

2264

2306

2434

2221

2349

2136

2668

0%

20%

40%

60%

80%

100%

1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700

Probability

Estimated Revenue for Cleaning Foam Sales at $3.19 and $3.49 Price Points

$3.19

Forecast Revenue=$2,221,000

Probability $3.49

1688

2014

2084

2200

2293

2363

2479

2270

2409

2153

2875

0%

20%

40%

60%

80%

100%

1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700



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Revenue Forecast Revenue Forecast Probability That Revenue Is Greater Than or Equal to Forecast

1366

1846

1972

2073

2200

2250

2402

2124

2882

2023

2301

0%

20%

40%

60%

80%

100%

1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900

Probability

Estimated Revenue for Cleaning Foam Sales at $3.79 and $4.09 Price Points

$3.79


Probability $4.09

1317

1863

1972

2108

2217

2272

2490

2163

2353

2054

2953

0%

20%

40%

60%

80%

100%

1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700



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Revenue Projection ResultsRevenue Projection Results• At $3.19, mean expected revenue is $2,221,220. One can be

90% sure that revenue will exceed $2,029,800, but only 10%sure that it will exceed the higher amount of $2,433,867.

• At $3.49, the expected revenue is $2,269,667. The chances ofexceeding the revenue at $3.19 are 59%.

• At $3.79, expected revenue is $2,123,800. At this price thechances of exceeding the revenue at $3.19 are 36%, and at$3.49, 27%.

• At $4.09, expected revenue is $2,162,600. One can be 90%sure this revenue will exceed $1,862,667, but only 10% sure itwill exceed $2,489,800. The chances of exceeding the projections at $3.19 are 37%; at $3.49, 29%. Revenue at thelatter price is expected to be higher than at $3.79; the chances

of that are 57%.


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Price the Foam at $3.49 . . .Price the Foam at $3.49 . . .Mean Expected Revenue at Tested Price Points

$2,221

$2,267

$2,124

$2,163

$2,050

$2,100

$2,150

$2,200

$2,250

$2,300

$3.19 $3.49 $3.79 $4.09

x000s

pricing and revenue monte carlo forecast model

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