About the Software

What you put in...	What you get out...
Customers' importance ratings for each measure of value for offerings in a product class Customer descriptors (demographic or firmographic variables)	Number, size, and profile of needs-based market segments Identification of factors that differentiate segments, both in terms of needs and descriptors Classification tool to allocate any potential customer to a segment based on customer descriptors

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.

What you put in...	What you get out...
Number of market segments, products, geographies or other basis for resource allocation Current level of spending and associated sales Profit margins Response functions - how sales would change if spending were higher or lower than current spending Constraints (minimum / maximum) for each basis unit	Optimal level of total spending Optimal allocation of spending across units Profit associated with optimal plan versus current plan Incremental gain or loss associated with changes from current or optimal plan

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).

What you put in...	What you get out...
Customers' rating of focal brand and key competitors on dimensions of merit Individual customer preference ratings of all competitors	Perceptual map, showing which brands are closest to one another Attributes that differentiate brands Locations of individual customer preferences Projected market share associated with current and new positions on the map

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?

What you put in...	What you get out...
SBU, product, or segment ratings on key attributes Importance of these attributes to the firm	Visual representation of relative attractiveness of SBUs, products, or market segments on a 2-dimensional map Sensitivity of map to attribute importance scores

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?

What you put in...	What you get out...
Observed churn rates Customer acquisition cost Number of customers/segments Gross margins by segment Customer transition probabilities across segments	Value of current customer base Time required to recoup customer investments ROI on customer/segment investments Size and profitability of customer segments over time; sensitivity to marketing investment plan

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.

clv 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.

About the Software

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