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Models
Marketing Programs Analysis measures effectiveness of programs. Allocates revenue to programs and depreciates program expenses to compute contribution margins for programs. more...
This model estimates the effectiveness of marketing programs at selling particular products. This capability was only a dream for marketers, until adequate data, lower computing costs, and improved algorithms combined to make this kind of model an extremely useful tool in marketing management. It also includes measures of the profit performance of individual customers or customer groups and products or product groups.
The model can help you to identify top-performing programs that should be expanded, and under-performing programs and need a 'get-well plan'. You should combine insights from the model with insights of field experts to make better decisions about programs. The model has done the deep thinking for you. As a result, the input data and interpretation of results is relatively straightforward.
The key output is an estimate of the 'contribution margin' of each marketing program. (A contribution margin is a form of profit margin where you don't have to account for all the costs in the organization, so it is not an operating margin or net income.) The main steps in the process are:
- Allocate gross margin (revenue - cost of goods sold) to marketing programs. That is, reward programs for generating gross margin dollars, not revenue, so programs are rewarded more for selling high-margin products. Allocate gross margin from each sales order to specific Programs, Program Events, Lead Events that touch customers who place orders. This is the key step. Definitions:
- A 'Program' is a spending program, such as webinars that promote a particular product.
- A 'Program Event' is part of a Program that occurs on a particular date in a particular location for a particular product line.
- A 'Lead Event' consists of one prospective customer person who attends a Program Event.
- Allocate expenses to Programs, Program Events, Lead Events using fixed expense and variable expense per Lead Event.
- Allocating program expense to individual programs is usually relatively easy, and data of reasonable quality and completeness is available in most organizations.
- The model allows you to distinguish fixed expense and variable expense.
- Fixed expense does not change with the number of Lead Events (customer persons who are touched by the program). Examples: development cost of a seminar or webinar.
- Variable expense changes with the number of Lead Events. Examples: rented space for an event, expense for staff that attend.
- Compute contribution margin (of a Program, Program Event or Lead Event) = allocated gross margin - allocated costs.
The model makes some key assumptions that improve the results.
- How a customer is defined important to the analysis.
- A customer is a decision network that collectively decides to buy a product or service.
- A customer is not generally a person who attends a program event, or who places an order, or who uses the product. (Such a definition would prevent the model from connecting a user of the product who attends a marketing event and a purchasing agent who places the order.)
- In practical terms, we suggest defining a customer to be a unique combination of company name, division, and address. This rule isn't perfect, but it is simple and usually yields good results.
- The time lag between a Lead Event and a resulting sales order at the same customer is in important factor in estimating the impact of the Lead Event on the sale.
- Each type of Program has an effective lifetime. A program gets less credit for an order as the time lag between the Lead Event and the order increases. You can specify the lifetime and how fast the program effectiveness drops off with time.
- The model allocates a fraction of total expense for a Program Event to each time period in wihwch there are some Program Events. The computation resembles depreciation of manufacturing equipment, in which depreciation expense is allocated to each time period during the life of the equipment.
- If a marketing program is not fully 'depreciated' at the end of model time, then some 'book value' remains in the program, and that value is not included in the analysis of what happened during model time.
The key results are collected on worksheet 'Contrib Margin'. Advanced versions include a pivot table of customer information.
We got great results using this method in a company with tens of thousands of marketing events annually, and dozens of products. In companies with marketing programs of this complexity, no experts or small team can carry in their heads all the knowledge that the model can capture. On the other hand, the analysis does not know all the facts that field personnel know, so you should take into consideration both the results of the analysis and expert opinions.
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This model uses market tests to estimate the impact of price changes on sales and (optionally) profit for one product. more...
This model uses results of pricing tests to estimate the impact of price changes on unit sales, revenue and profit for one product.
The model helps address two key pricing questions.
- What price for a product maximizes its revenue?
- What price for a product maximizes its profits? (Advanced version only)
The key to estimating sales from pricing tests is to estimate a constant 'price elasticity' for the product. Price elasticity is defined by the following relationship: If I raise (lower) the price by x% (where x is small), then the unit sales rise (fall) by elasticity * x%. This model assumes that price elasticity is constant over the range of prices being considered.
The key step in computing profit margins is to estimate costs as a function of unit sales. Costs can be cost of goods or total costs or whatever you choose.
The model offers two types of price elasticity analysis.
- Simple (or constant) price elasticity. (This is the only price elasticity method in the Standard version.) This is the standard 'textbook' model of price elasticity, characterized by the relation sales units = constant x price^elasticity, where elasticity is a constant derived from pricing test data.
- Generalize (or non-constant) price elasticity. (Advanced versions offer both elasticity methods.) This model allows the elasticity to change as a function of price. For a given set of pricing test data, this method is better at identifying prices that maximize revenue or profits.
Pricing tests proceed through the following steps.
- Select a large market where you sell the product at a standard price, and you know how many units you sell at that price (per time period).
- Select some smaller test markets within the large market that have similar response to price changes. The test markets should be chosen to minimize "cross-talk" between test markets about prices, and commuting between test markets to buy at a lower price. For example, test markets consisting of large cities separated by a few hundred miles generally meet these criteria.
- Offer the product at different prices in the different test markets. You probably want to include some prices above the current standard price, and some below. Run the test for sufficient time that customers react to the test prices as if they were permanent prices. Measure the unit sales in each test market.
- Enter the following data into the price testing template.
- The price and sales units for the entire market that prevailed before the pricing test
- The number of units sold in each test market before the pricing test
- The price and number of units sold in each test market during the pricing test
From this data the model estimates the price sensitivity (price elasticity) of the total market. It then estimates the revenue and sales units (and profit margin if that is included in your model) for the entire market at different prices that you specify.
The model has several other key features.
- The model provides some standard statistical measures of goodness of fit of the model and the market test data.
- r^2 ('r squared') measures the fraction of the variance in sales units with price that is accounted for by the changes in price, and by the model. r^2 takes on values between zero and one. A value over 0.8 means that the constant price elasticity method accounts for nearly all of the variation in unit sales with price. A value below 0.2 means that other factors besides a constant price elasticity are needed to explain well the variations in sales units across test markets.
- The 'standard error of elasticity' measures the likely range of price elasticity values that are reasonably consistent with the data.
- The template contains comments on factors that help to ensure that market tests yield valid conclusions about pricing behavior.
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Pricing Analysis uses pricing tests to estimate the impact of price changes on sales and (optionally) profit for several products that affect each other's sales. The model provides information that can help set prices to optimize revenue or profits. more...
This model uses results of pricing tests to estimate the impact of price changes on unit sales, revenue and profit margins for several related products. The results can help you optimize revenue or profit margin with limited pricing experiments.
The model helps address two key pricing questions.
- What prices for a product line with several products will maximize its revenue?
- What prices for a product line of several products will maximize its profits? (Advanced version only)
The products can include your own products, competing products, and substitutes that are not direct competitors.
The key to estimating sales from pricing tests is to estimate a constant price elasticity for each product, and constant price cross-elasticities for each pair of products from test results.
- Price elasticity is defined by the following relationship: If I raise (lower) the price by x% (where x is small), then unit sales rise (fall) by elasticity * x%.
- Price cross-elasticity between Product A and Product B is defined by the following relationship: If I raise (lower) the price of Product B by x% (where x is small), then the unit sales of Product A rise (fall) by corss-elasticity * x%.
This model assumes that price elasticity and cross-elasticity is constant over the range of prices being considered.
The key step in computing profit margins is to estimate costs as a function of unit sales levels for the products. Costs can be cost of goods or total costs or whatever you choose.
The model offers two types of price elasticity analysis.
- Simple (or constant) price elasticity. (This is the only price elasticity method in the Standard version.) This is the standard 'textbook' model of price elasticity, characterized by the relation sales units = constant x price^elasticity, where elasticity is a constant derived from pricing test data.
- Generalize (or non-constant) price elasticity. (Advanced versions offer both elasticity methods.) This model allows the elasticity to change as a function of price. For a given set of pricing test data, this method is better at identifying prices that maximize revenue or profits.
Pricing tests proceed through the following steps.
- Select a large market where you sell the product at a standard price, and you know how many units you sell at that price (per time period).
- Select some smaller test markets within the large market that have similar response to price changes. The test markets should be chosen to minimize "cross-talk" between test markets about prices, and commuting between test markets to buy at a lower price. For example, test markets consisting of large cities separated by a few hundred miles generally meet these criteria.
- Offer the product at different prices in the different test markets. You probably want to include some prices above the current standard price, and some below. Run the test for sufficient time that customers react to the test prices as if they were permanent prices. Measure the unit sales in each test market.
- Enter the following data into the price testing template.
- The price and sales units for the entire market that prevailed before the pricing test
- The number of units sold in each test market before the pricing test
- The price and number of units sold in each test market during the pricing test
From this data the model estimates the price sensitivity (price elasticity) of the total market. It then estimates the revenue and sales units (and profit margin if that is included in your model) for the entire market at different prices that you specify.
The model has several other key features.
- The model provides some standard statistical measures of goodness of fit of the model and the market test data.
- The model reports r^2 ('r squared') for each product and pair of products. These measure the fraction of the variance in sales units for a product that is accounted for the change in price of that product or another product, and by the model. r^2 takes on values between zero and one. A value over 0.8 means that the constant price elasticity method accounts for nearly all of the variation in unit sales with price. A value below 0.2 means that other factors besides a constant price elasticity are needed to explain well the variations in sales units across test markets.
- The model reports a 'standard error of elasticity' for each product and each pair of products. These measure the likely range of price elasticity (and cross-elasticity of two products) that are reasonably consistent with the data.
- The template contains comments on factors that help to ensure that market tests yield valid conclusions about pricing behavior.
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Product Profitability Analysis estimates profit generated by each product after accounting for cost of goods, royalties, and operating expenses for development, marketing, and customer support. more...
This template estimates time-dependent 'contribution margin' for each product by subtracting from revenue the cost of goods and operating expenses that are directly attributable to each product. This analysis is often extremely useful for decision-making and profit optimization in many industries, including manufacturing, distribution, services, and software. It is often a 'sleeper'; that is, it can produce surprisingly novel and credible results that were not fully recognized and utilized before.
The model computes gross margin and gross margin % for each product by subtracting cost of sales, which consists of cost of goods and royalties.
- Cost of goods are optionally specified as cost per unit, cost as a % of revenue, or fixed costs for each product, or by all these methods.
- Royalties paid are optionally specified as cost per unit, cost as a % of revenue, or fixed costs for each product, or by all these methods.
The model computes contribution margin for each product by subtracting allocated operating expenses from gross margin. It tracks the following expenses.
- Marketing expenses
- Tracks marketing staff equivalent headcount allocated to each product, staff expense per head for several job levels, and total expense of marketing staff for each product.
- Tracks marketing programs expense by program and by product.
- Product development expenses
- Tracks equivalent number of development staff applied to each product, segmented by development groups; and staff expense per head by job levels.
- Tracks development program expenses, segmented by program and by product
- Tracks development overhead expenses
- Customer support expenses. Differences in support expenses across products often are not fully recognized, yet they can be major factors in determining profitability of some products.
- Tracks number of support calls/cases, segmented according to the amount of staff time applied to each call. Tracks staff expense per head by job level. Tracks utilization of support staff.
- Estimates support activity and expense segmented by support issues (such as installation, maintenance, application assistance, explaining poorly documented features). Tracking expense by support issues is a powerful method for identifying opportunities to improve customer satisfaction and product profitability.
Product profitability analysis should be just one factor in decisions about allocating resources among products.
- The most important limitation of product profitability analysis is that is must be balanced with strategic factors, such as revenue growth rate, and long-term potential of products that is not reflected in current profitability.
- Where results of the analysis differ from expert opinions, we recommend that you carefully explore the reasons for the discrepancy. The experts may not have an accurate perception of expenses, or the profitability analysis may have missed factors that the experts recognize.
Not all features are included in the Standard or Light versions.
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Sales Plan combines managers' sales targets, sales history and pricing plan. Segmented by products, locations, sales channels, customer industries, and time. more...
This application generates sales plans from historical trends and managers' targets. Managers' judgment controls the plan, and historical data fills in detail where managers don't provide targets.
The current model includes these features. (For a simpler sales plan, see "Sales Plan for a Small Startup".)
- Uses linear regression on sales unit history to extract trends.
- Limits positive and negative growth rates, so that small and unstable segments do not blow up the overall plan.
- New products start their trend lines at product introduction dates.
- Forecasts revenue for product x location x channel x industry segments, using unit sales plan, list prices, and price discount percentages.
- Managers' revenue targets for various segments override historical trends so human judgment controls the plan.
- The model computes suggested adjustment factors to raw regression forecast to make the final forecast closely match managers' targets.
- If managers' targets are inconsistent (e.g. product managers targets canot be reconciled with channel managers' targets), the planner can choose which targets to emphasize.
- Includes adjustment factors for product x location x sales channel x industry segments.
- Forecasts actual average selling prices by segment from list prices and actual price discount history.
- When time grain is Quarters, the model extracts seasonality from historical data and applies it to the plan.
- Estimates growth trends free of seasonality.
- Estimates seasonal effects using regression methods.
- Adds historic seasonality back into forecast.
- Provides trailing-4-quarter history and plan, to eliminate seasonality and reduce effects of random large orders.
- Optionally uses local currency along with global currency - for now, Euros in the Euro Zone.
- Optionally accepts historical input data for revenue, sales units, prices (and variable costs if you select the margin forecasting feature) from databases.
Not all features are included in the Standard version.
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Sales Plan for a small start-up business tracks planned revenues, sales units, and prices over a specified time range with specified time grain. Compares actual and planned values. more...
This template organizes sales history and sales plans by product. The advanced version plans sales units and prices, segments sales by sales channels, and optionally provides a worksheet to store assumptions behind the plan. It helps to plan revenue for a product line, enter actual sales as they occur, and track revenue against historical data. It is modeled after the SCORE sales planning template.
ModelSheet offers several planning templates for small startups. We recommend starting with this template 'Sales Plan for a Small Startup', next 'Cash Requirements for a Small Startup', then 'Cash Flow for a Small Startup'. The information from each of these templates is useful in setting up the following templates.
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