On the trail of hidden data treasures

Data analysis far beyond simple cross-tabulation

Don’t waste potential

In-depth data analysis as part of our consulting services

Of course, the detailed analysis of data in the form of tables plays a central role for us and our customers. Simple and understandable parameters such as significances and other values are part of our daily business.

If we were to leave it at that, however, we would be wasting potential. That’s why we have employees with special professional skills, such as statisticians, who are able to get a little more out of your data. This is also part of our high-quality, well-established consulting services.

Because the world is usually not one-dimensional

Getting everything out of the data with multivariate statistical methods.

Finding and analysing different target groups
Cluster analysis

Find your target group
The attitudes, opinions and behaviour of the respondents are used to determine the relevant target groups of a market. The target groups are specifically defined and their size determined, thus providing practical approaches for making contact and communicating with them.

Retrieve existing clusters from previous analyses
Discriminant analysis

Cluster retrieval
If you already have a cluster analysis and want to assign the results of a new study to the existing clusters, this can easily and quickly be done by means of a cluster retrieval procedure, which is carried out with the help of a discriminant analysis.

Visualizing the position of your own brand within the market environment
Correspondence analysis

Brands in competition
With the „graphic“ analysis method, using various images in a multidimensional representation, the position of your own brand can be evaluated within the market environment, for example, in terms of its direct competitors.

Convince subjects to make decisions

Achieve better results with Trade-Off procedures

Determine the value of product features and the ideal price
Conjoint analysis

For the new development of products
With hierarchical Bayesian regression, the values of product characteristics are estimated in order to determine the product with the highest probability of success and the corresponding ideal price.

Classify product properties according to their importance

So that not everything is always considered as important
Here, respondents have to decide which (product) properties are more important to them than others. This leads to a more sophisticated differentiation with regard to the importance of the properties and provides a basis for further analyses, such as segmentation.

There’s always a good reason for everything

Measuring the influence of variables with causal analytical methods

Uncover connections
Correlation and regression

Revealing unconscious connections
Since respondents are often unaware of their decision making process, it makes more sense not to ask their motives directly, but rather to determine them mathematically. Correlation and regression analyses can be used to determine which (product) properties are particularly relevant for customer satisfaction or willingness to buy.

Uncovering complex structures
Structural Equation Models

Analyzing causal relationships
Structural equation models can also be used to examine and quantify complex causal models. If it is not known beforehand which parameters belong to the influencing variables and which belong to the target variables, or if there is more than one target variable, then relationships can be determined according to their existence and strength.