First and foremost, the hypothesis matrix is not aimed at finding ideas for corresponding problems, but rather at helping to understand them better through the analytical processing of facts and to show the connections that lie within them.
The resulting findings are to be used again in a target-oriented manner to find further solutions.
So the typical use case is the analysis of interdependencies and the detection of relationships and interactions between two complex facts.
Examples of such issues:
– How do the design elements of a package (A) on the purchasing behavior of a specific target group (B)?
– Which of given production conditions (A) cause the appearance of a certain production defect
(B)?
It is a good idea to have the statements on the individual elements of A and B checked and enriched by as many experts as possible. This increases the probability of adding diverse information to the hypothesis matrix from the beginning. Much more likely than if it were only created by a problem solver.
In testing, not only simple markers come into question, but the statement of a hypothesis matrix can be additionally raised by a detailed description of the relationships.
As a result, the complexity of the matrix increases over time, as does the number of potentially possible hypotheses for a given question.
