10-01-96
We've seen this one before. It's the central model of the MAP(c) system, and it's also the basic model underlying MARKET MAPPING, STRUCTURE MAPPING and SEMANTIC MAPPING.
What it also implies is that marketing systems are 'ecological', and that the OUTCOMES for a particular product or brand are a function of both its own properties and decisions, and the properties and decisions made by its COMPETITION.
11-23-96
One of the neat things about this model is that it allows for the systematic discovery of UNMET NEEDS or OPPORTUNITIES in a particular market (see STRUCTURE MAPPING for more details).
This type of discovery process has often led to improvements in existing products, and occasionally has even led to the successful introduction of NEW PRODUCTS. It's therefore not unreasonable to suggest that NEW PRODUCTS have a much better chance in the marketplace if they address one or more important UNMET CONSUMER NEEDS.
However, it's also useful to keep in mind that if constraints such as COST are a factor, addressing an UNMET NEED may result in neglecting other important needs (see NO FREE LUNCH ).
The CAVEAT in addressing UNMET NEEDS is the implicit assumption of Ceteris Paribus (i.e. that all other things remain equal). Less expensive is dandy, but only if nothing else is sacrificed. Fewer calories is great, as long as taste is not affected. You get what you pay for.
Let me propose a thought exercise:
Can you think of one or more UNMET consumer or business NEEDS that are addressed by the NC (Network Computer). What implications might this have for its market potential?
05-04-07
It's now later and here's more.
The MAP(c) and/or Multinomial Choice model has two key components: IMAGE SHARE and OPPORTUNITY SCORE. Both of these components can be computed using APL 'one liners'.
IMAGE SHARE
The inputs for this procedure are stored in two data arrays:
It is assumed that the criteria importance weights range from 0 to 1 and their sum across all columns equals 1.0. (SEE: NO FREE LUNCH ).
It's also assumed that the performance/perception ratings are on an 11 point scale (i.e. they range from 0 to 10), with 5 being 'average'. What's more, these ratings should be interpreted as being on a 'more is better scale' (as opposed to a 'hedonic scale' where the MID-POINT may be the BEST), with 10 being the BEST and 0 being the WORST.
Both the weights (WT) and ratings (MAT) can come from either a single individual, or from many individuals, in which case they are aggregates averaged across all observations of individuals doing the rating. The computational details in APL look as follows:
The output numbers represent the IMAGE SHARE for each of the rows or ITEMS (which could also be BRANDS or possibly CANDIDATES).
OPPORTUNITY SCORE
Using the same inputs as for IMAGE SHARE with the same restrictions, we compute an OPPORTUNITY SCORE for each criterion in APL as follows:
The magnitude of the score for each criterion can serve as a guide for the order of priority in which these criteria should be examined in order to focus on 'unmet needs'. Criteria with the highest scores should be addressed first. Improving perceptions on these criteria will have the greatest POSITIVE IMPACT on overall IMAGE SHARE for a particular object or item.
Additional information on how these two MAP(c) components work together can be found here:
There are several ways to implement such computations on your own computer. Link to our Array Processing Resources page to find out more.
SEE: ARRAY PROCESSING RESOURCES
o Return to: 'SPRING SYSTEMS HOME PAGE'