Predicting Success in Roles.

The client wanted to understand the characteristics of most successful employees in the organisation and therefore gain insights on predictors of success to target recruitment and focus development internally for those who possess these characteristics.

We brought together multiple existing internal data sets to identify trends and validate hypotheses on Success & Talent Management. Quantified the attributes with greatest impact on success of the advisors and fed the results into recruitment, development and performance management practices gaining positive business results. With the use of Machine Learning Algorithms, we had identified the most crucial factors affecting the output and created rules to predict the performance

Existing HR, Sales, Finance, Recruitment and Relationship data was brought together and advanced analytics techniques were used to determine the correlations between the data and an individual’s success. We had created business rules based on proven hypothesis and used in live scenarios. Advanced quantitative approaches like classification trees, principal component analysis, logistic regression were used to solve the situation.

By understanding the characteristics of success for their employees, the company had identified individuals who would be most successful at the senior grades. The company could then identify which characteristics should be sought after when recruiting into these grades and also identify early top talent (i.e. to focus on developing individuals who possess these characteristics).

Significant changes were done to their training strategy and had improved their organizational policies related to promotion, recruitment, assessment etc. Our model resulted in the reduction of low performers by an average of 15 to 25% over a year.