Data Maturity Model
In our research we use the Data Maturity Model developed by Presearch.1. On the basis of this model, we look further than just capturing, for example, the Satisfactionengagement and/or involvement. This means that we make organisations understand that certain variables score less well. If mutual relationships are not taken into account, it can mean that a low scoring variable hardly contributes to the NPS score of an organization, for example. At the same time, we see that organizations often focus on "the lowest scoring" variables.
Application of models
In order to achieve greater impact, it is important that connections between variables are visible and predictable. This is why we have developed extensive (regression) models that enable organisations to make even more fact-based adjustments. These extended regression models calculate and predict relationships between variables. The results from these models have been translated into accessible dashboards where our customers can easily access the performance of their most relevant variables.
Presearch is a professional and entrepreneurial research firm that is quickly switched on and considered. With their appropriate advice and solutions you come to a desired result.
Together with Presearch we have set up a method in a short time. That this method was appropriate, showed among others the positive reactions of employees and the high response rate.
1 The Data Maturity Model is partly based on Fitz-enz, J. (2010). The New HR Analytics: Predicting the Economic Value of Your Company's Human Capital Investments. American Management Association.