Validation of models for the operationalization of human interaction by means of external stimuli and behavioral responses captured by the experimental instruments, identify empirically core metrics that play central role in human interaction
Focus on the development of the experimental setting and the use experimental tools, measure expressions, cognition and workload, motivation, attention, arousal and valence. Objective is to improve workplace interaction performance.
Visualization of human interaction data
Using public and private business strategy sources, organize and collect the key available strategic business metrics and means for data-driven companies focusing on profitability, efficiency, revenues and risk
Identify dynamic, real time metrics and means to obtain them.
Ensuring the quality of the metric vs. right way of collecting it (if a business is measuring the wrong piece of data — or measuring the right piece of data in the wrong way –any machine learning models built on those metrics can lead to bad decisions).
Use ML to make sense out of the data, customization for e.g. customer retention rate, conversion rate, market share, determining how well a business is doing, adaption
Gain empirical evidence of Ethically Aligned Design practices and methods.
In the Beyond PoC phase, we focus on the actual data and further develop the experimental setup