About the Software - DecisionPro Inc.

What you put in...	What you get out...
Customer's choice data for alternative offerings Customer ratings of alternative offerings on their key attributes	Purchase probabilities, predicted and observed choices of customers Factors influencing customer choice, including brand as well as performance attributes

The Customer Choice (Logit) model is an individual-level response model that helps to analyze and explain the choices individual customers make in the market. The Customer Choice model helps firms to understand the extent to which such factors as price of a brand or its ease of installation influence a customer's choice of a brand. A brand's purchase probability at the individual level is equivalent to the brand's market share at the market level.

Firms can use Customer Choice analysis to develop marketing programs that are tailored to specific market segments, or even tailored to individual customers.

This model uses the following input:

Single Alternative/Boolean
This method analyzes only one option instead of choosing one among several alternatives. For this analysis, only one brand's data is required.
Multiple Alternatives
This method considers customer response across a subset of related competitors. For this analysis, the following data is required for all competing brands involved in the study.

For each customer, the data that goes into this model is a set of ratings on various attributes of each alternative (either single alternative "yes/no" response, or multiple alternatives "chose one of N" response) involved in the study, and the alternative that the customer chose in each period. For the "Single Alternative/Boolean" option, this would be a 1 or 0, depending on whether or not the customer chose this alternative. For the "Multiple Alternatives" option, one alternative would be a 1 to indicate the alternative chosen during this period, while the others remain 0 to indicate that this particular customer did not choose the other alternatives.

What you put in...	What you get out...
Customer ratings of a set of real or potential product offerings, defined by their key attributes Market share of existing products New product profiles	Customers' preferences and responses to new products Relative worth of product attributes Optimal product design Market share estimates for alternative products Drivers for purchase choices Customers' willingness to pay for product attributes Potential incremental revenue from new offerings/features

The Conjoint Analysis model is widely employed for designing new products. It is a procedure for measuring, analyzing, and predicting customers' responses to new products and to new features of existing products. It enables companies to decompose customers' preferences for products and services (provided as descriptions or visual images) into "part-worth" utilities associated with each option of each attribute or feature of the product. Firms can then recombine the part-worths to predict customers' preferences for any possible combination of attribute options. Firms can use conjoint analysis to:

Design new products that maximize customer utility.
Forecast sales/market share of alternative product bundles.
Identify market segments for which a given product concept has high value.
Identify the "best" product concept for a target segment

What you put in...	What you get out...
Market potential for new products Historical sales data OR selection of analogous products Advertising and pricing plan	Sales/adoption rate forecast for new product Sensitivity of sales forecast to marketing activities Ability to link to revenue and profit projections

The Bass diffusion model is used to forecast the sales of a new product or service that has no close competitors. It can be used to forecast the long-term sales pattern of a product when one of the following is true:

The product has recently been introduced and sales have been observed for a few time periods.
The product has not yet been introduced, but it resembles another product in the market whose sales history is known.

The model helps to predict:

the number of customers in the target segment that will eventually adopt the new product or service.
when they will adopt it.

bass The Marketing Engineering for Excel software includes both the Bass model and the Generalized Bass model. The Bass model assumes that the sales rate for the product is not affected by marketing mix variables while the Generalized Bass model assumes that the product sales rate in the target segment is affected by the level of advertising for the new product, and by the price of the new product.

Both models include three parameters to describe the two factors which affect product diffusion in the market and the market growth

p - the coefficient of innovation (or coefficient of external influence)
q - the coefficient of imitation (or coefficient of internal influence)
r - market growth (as a function of existing market size)

The Generalized Bass model includes three more parameters:

s - the market price elasticity, or how much a change in price affects the total market potential
v - the advertising coefficient, which affects the strength of the effect of changes in advertising levels in product diffusion
w - the price coefficient, which affects the strength of the effect of changes in price in product diffusion