XLSTAT-MX

XLSTAT-MX is a must have complement for XLSTAT-Pro users who do sensory data analysis, and who use Preference Mapping or other related techniques to understand the customers' perceptions. MX stands for Marketing analytics. The tools of the XLSTAT-MX module can be accessed from the XLSTAT menu of from the XLSTAT toolbar.

The following are the main features of the current XLSTAT-MX version:

Preference Mapping (Prefmap) allows you to build maps which are useful in a variety of domains. A preference map is a decision support tool in analyses where a configuration of objects has been obtained from a first analysis (PCA, MCA, MDS), and where a table with complementary data describing the objects is available (attributes or preference data). In the market research and consumer analytics domains (sensory data analysis), Prefmap is used to analyze products (the objects) and to answer questions such as:

• How is our product positioned compared with the competitors' products?
• Which product is the closest to ours?
• Which type of consumer prefers my product?
• Why are the competitors' products positioned as such?
• How can I reposition my product so that it fits better my target group?
• What success can I expect from my product?
• Which new products should I encourage the R&D department to create?

Preference mapping provides a powerful approach to optimising product acceptability. XLSTAT-MX offers several regression models to project complementary data on the objects maps:

• Vector model
• Circular ideal point model,
• Elliptical ideal point model,
• Quadratic ideal point model.

XLSTAT-MX displays detailed results in addition to the preference map to facilitate the interpreting of results.

Semantic Differential - Use this method developed by the psychologist Charles E. Osgood to visually compare how "judges" rated a "product" using a series of criteria.

Generalized Procrustean Analysis (GPA) - GPA is used in sensory data analysis before a Preference Mapping to reduce the scale effects and to obtain a consensual configuration. It also allows to compare the proximity between the terms that are used by different experts to describe products.

Multiple Factor Analysis (MFA) - Use the Multiple Factor Analysis (MFA) to simultaneously analyze several tables of variables, and to obtain results, particularly charts, that allow to study the relationship between the observations, the variables and the tables. Within a table, the variables must be of the same type (quantitative or qualitative), but the tables can be of different types.

Penalty analysis - Penalty analysis is a method used in sensory data analysis to identify potential directions for the improvement of products, on the basis of surveys performed on consumers or experts.

• Preference data (or liking scores) that correspond to a global satisfaction index for a product (for example, liking scores on a 9 point scale for a chocolate bar), or for a characteristic of a product (for example, the comfort of a car rated from 1 to 10).
• Data collected on a JAR (Just About Right) 5 point scale. These correspond to ratings ranging from 1 to 6 for one ore more characteristics of the product of interest. 1 corresponds not « Not enough at all », 2 to « Not enough », 3 to « JAR » (Just About Right), an ideal for the consumer, 4 to « Too much » and 5 to « Far too much ». For example, for a chocolate bar, one can rate the bitterness, and for the comfort of the car, the sound volume of the engine.

Some tutorials are available for in depth descriptions on how to use the product.

Demo version

A trial version of XLSTAT-MX is included in the main XLStat-Pro download.

OrderingPrices and ordering

For prices, on-line ordering and other purchasing information please go to our ordering page.