Part 2. MM4XL Tools > 1. Strategic Tools > Profile Manager > 1. Anatomy of a Profile Manager Report > Input Data

Profile Manager

Input Data

We begin by taking a look at the input data required for running the analysis.

The table below shows the input data to Profile Manager. It is made of three parts:

  • variable labels (cells A3:A10)
  • input data (cells B2:E10)
  • the marketing response vector (F2:F10)

The first two parts, labels and data, make up the profile(s) and they can be gathered in a variety of ways, ranging from sample surveys to desk research and competitive intelligence. The third part, the marketing response vector, is again the result of a survey or of educated guessing. Educated guessing is often done at the early stage of concept development, for instance for a new product launch or just a new product feature. One can simply list his or her best guesses about the relative importance of each of the items in the profile, and run several scenarios to compare. However we suggest caution in running this.

Profile Manager Software to Model Market Development

The profiles above are expressed on eight variables (rows), most of which have a different scale of measurement from the others. If you check the option bRescale datab in the tool window, Profile Manager rescales data automatically for you. Otherwise, in order to avoid rescaling, you can enter row vectors with sum equal to 1. The higher the frequency, the higher the relative attractiveness of that attribute.

Professor Kotler calls the matrix above competitive marketing mix matrixand he explains it is used for summarizing the average market perception of the analyzed brands measured along several dimensions of competition. On the other side, continues Kotler, the Marketing Response Vector, column F above, stands for the average relative importance consumers (or your gut feeling) attribute to each of the dimensions. In the data above, for instance, the sample of surveyed consumers attributed the lowest importance to getting a good price deal when buying the product category (5%), and they attributed the highest rating (20%) to Quality and Adv. The sum of the response vector is 1 and the list of variables used for the analysis varies from business to business.

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