OUR SOLUTIONS

Customized solutions

Every organization is unique and tackles different problems. What works for one organization might not work on the others. With a customized solution, we can help you achieve your objectives using our strengths in Psychology and Data Science.

Read more about the projects we have worked on below.

OUR SOLUTIONS

Customized solutions

Every organization is unique and tackles different problems. What works for one organization might not work on the others. With a customized solution, we can help you achieve your objectives using our strengths in Psychology and Data Science.

What we have worked on

Financial risk appetite

iAdvisor: A digital platform by Jachin Capital enabling investors access to intelligent portfolios. Mercurics integrated our AI Engine to ascertain financial risk appetite of individuals, as part of their on-boarding onto the platform. Tuned model can predict an individual's risk appetite with 80% accuracy.

Archetype profiling for targeted interventions

Archetype Development: Through the use of Natural Language Processing (NLP) and clustering, we made sense of large scale open ended text and demographic data to categorise respondents into specific archetypes. This helped our client to better understand the profiles of each archetype, and enable them to provide targeted interventions for each group.

Staff Operational Readiness

Fit-for-duty: Understanding staff operational readiness by measuring key factors such as reaction speed, attentiveness and behavioural reliability. Integrated measurement tools into an app.

Employee Understanding

To accelerate internal talent growth, a large Japanese telecommunications company wanted to enhance their understanding of employee’s psychological and behavioural traits. Mercurics customised a computational model tied with psychometric assessments to ascertain job satisfaction, aspiration, sense of professionalism and overall job fit.

Employee Flight Risk Modelling

Built a neural network model that determined the likelihood that an assessed employee would leave the company based on personality traits, work environment factors and demographic characteristics. Utilised already available internal employee data combined with customised psychological questionnaires.

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