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.

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

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:

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

Conjoint Analysis Resources

Conjoint Analysis Webinar: View the Product Design and Customer Segmentation with Conjoint Analysis webinar

Conjoint Analysis Technical Note: Learn the analytics behind conjoint analysis.

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.

Customer Lifetime Value (CLV) is a metric of a customer's value to the organization over the entire history of the relationship. Short-term sales are a factor, but so are overall customer satisfaction, the churn rate in the segment, and the costs to acquire a new customer and retain an existing customer.

The model uses the following input:

Segment Description

• Number of Customers per segment. As of today, how many customers does the company have in each segment?
• Gross Margins are the average margins that can be expected from a customer over each period (e.g., a year, a quarter), based on which segment this customer belongs to at the beginning of this period.
• Marketing Costs quantifies how much money the company spends per customer and per period, depending on which segment this customer belongs. Typically, active customers are followed more closely, receive more attention (e.g., direct marketing solicitations or sales representatives visits), and cost more to the firm.

Transition Matrix

• The Transition Matrix summarizes the likelihood a customer will switch segments at each period. This matrix should be read horizontally, and each line sums up to 100% (since all customers need to go somewhere). In the above example, an "active customer" has a 75% likelihood of still being in the same segment next period, and 25% chance of switching to the "warm customer" segment.

The GE Portfolio approach evaluates a business on the basis of two composite dimensions: industry attractiveness and business strength. These dimensions, in turn, consist of a series of weighted factors. Both the factor weights and the factors themselves may vary from one application to another; for example, industry attractiveness includes measures of market size, growth rate, competitive intensity, and the like, whereas business strength normally includes such measures as market share, share growth, and product quality. Analysts assign each business a rating for each factor and a weight to each factor. Multiplying the factor ratings by the weights produces a position for each business on the strength/attractiveness matrix.

While designed to assist in the GE/McKinsey approach to portfolio management, this model can be used for any situation where a certain number of items are ranked on two sets of weighted factors. Optionally, multiple sets of weights can be used.

The GE Portfolio approach helps firms answer such questions as:

• On which products, offerings, or divisions should we focus our efforts?
• What method can we use to assess and understand the weights that various members of the management team assign to different dimensions?
• How can we reconcile different points of view?

Positioning Analysis software incorporates several mapping techniques that enable firms to develop differentiation and positioning strategies for their products. By using this tool, managers can visualize the competitive structure of their markets as perceived by their customers. Typically, data for mapping are customer perceptions of existing products (and new concepts) along various attributes, customer preferences for products, or measures of behavioral response of customers toward the products (e.g., current market shares of the products).

Positioning Analysis uses perceptual mapping and preference mapping techniques. Perceptual-mapping helps firms to understand how customers view their product(s) relative to competitive products. The preference map introduces preference vectors or ideal points for each respondent on to a perceptual map. The ideal point represents the location of the (hypothetical) product that most appeals to a specific respondent. The preference vector indicates the direction in which a respondent’s preference increases. In other words, a respondent’s “ideal” product lies as far up the preference vector as possible. The preference map starts out with a perceptual map giving the locations of the product alternatives. In the second step, it introduces for each respondent either an ideal brand or a preference vector.

Positioning Analysis also helps firms to answer such questions as:

• Based on customer perceptions, which target segments are the most attractive?
• How should we position our new products with respect to our existing products?
• How do our customers view our brand?
• What product name is closely associated with the attributes that our target segment perceives as desirable?
• Which brands do our target segments see as our closest competitors?
• What product attributes are responsible for the perceived differences between products?
• How would changes in a product's perceived attributes alter the product's market share?

Resource Allocation helps optimize resource sizing and resource allocations across segments, products, channels, etc. It answers such questions as

• How much should we spend in total during a given planning horizon?
• How should that spending get allocated to each product or market segment? To each marketing mix element? How much of our budget should be spent on advertising and other forms of impersonal marketing communications? On sales promotions? On the sales force?
• How should budgets given to an individual (e.g., salesperson, manager of department) be allocated? To customers? To geographies? To sub-elements of the marketing communications mix? Over time?

There are four primary steps:

• Enter data for current efforts and outcomes-- typically these are hard data about current market situations.
• Enter calibration data--typically these are judgmental data, unless there are data available from experimentation.
• Calibrate the response curves. Check to ensure that the curves fit the points.
• Run the analysis (and optionally enter constraints).

Segmentation/Targeting is an analytic technique that helps firms to segment customers in a market.

Segmentation is the process of classifying customers into homogenous groups (segments) such that each group of customers shares enough characteristics in common to make it viable for the firm to design specific offerings or products for selected segments. The application finds customer segments using needs-based variables called basis variables. Cluster analysis helps firms to:

• Better understand their customers.
• Identify different segments in a market.
• Choose attractive customer segments for targeting its marketing programs.