CORESO carries out descriptive, explanatory and prospective models by applying various quantitative methods. The common objective behind all these models is to clean, analyse and visualise data to enable an effective and informed decision-making. Whether a descriptive, explanatory or prospective model is most appropriate depends on the objectives of the study and the phase of the decision making process.

Descriptive models: what?

• Descriptive models provide a precise picture of the situation at a given moment in time.
• Thanks to descriptive models, decision makers get a better understanding of the current state of their issue at hand. Such models go beyond simple summary statistics by isolating the relevant information through advanced statistical and econometrics techniques.
• Examples of descriptive models include equal pay analyses where the goal is to describe the gender pay gap in an organisation at a given point in time or our estimation of the construction density along pipelines.

Explanatory models: How?

• Explanatory models are appropriate when we wish to look at causality. They analyse relationships between factors and contribute to the understanding of how processes work.
• Understanding how processes work allows the decision-maker to act on the factors that will have the greatest impact.
• Explanatory models are also often used in combination and as a basis for prospective models. Understanding how key processes work is essential to predict future outcomes and carry our what-if analyses.

Prospective models: what-if?

• Prospective models provide an estimate of what is likely to happen in the future.
• They can enable what-if-analyses where decision-makers can simulate future outcomes based on different scenarios and policies and then choose the best.
• We have used prospective models for instance to forecast the demand for labour in a specific sector or to estimate how the cost structure of social services would change under different reform scenarios.

Our quantitative analyses strongly and directly benefit from our other services.

• Collective intelligence

  Our collective intelligence services allow us to gain new and additional information from stakeholders by the means of computer-assisted workshops. This additional information can be used to improve the specifications of quantitative models. For instance, stakeholders can help us identify factors to include or even to determine potential links across factors.

• Web tools

  Our expertise in the development of web tools offers our customers new ways to present findings and display results of quantitative models. Interactive web dashboards enable users to customise the visualisation related to the model's outcome or even carry out simulations of prospective models under different scenarios and hypotheses. Web tools can be made available to the general public or be limited to a group of decision makers.