beqom, a provider of compensation and continuous performance management cloud solutions, has released a Pay Prediction solution that uses advanced Machine Learning (ML) capabilities to help HR and managers make data-driven decisions regarding compensation. This new solution is one of the several offerings available on beqom’s AI-enabled platform.
Applying this technology can help companies make pay recommendations and produce smart pay ranges that help to retain employees and attract talent in a competitive job market, as well as help reduce employee churn by predicting which employees are most likely at risk of leaving due to a pay gap.
Used in conjunction with beqom’s Total Compensation Management solution, the Pay Predictor, based on multivariate regression models, can assist in making more advanced, data-driven decisions about pay, such as determining the right salary for a new job offer or for a merit increase or promotion for an existing employee, using factors such as job, employee skills, location, demographics, existing company pay scales, and industry benchmarks.
To ensure cost-effective decision-making, beqom enables managers to run simulations to see how proposed pay changes compare to the market, and what they will cost vs. the budget. The simulations can also reveal the impact on pay equity across the organization.
“With beqom’s Pay Predictor, companies can be confident they are making informed and fair decisions, paying enough to attract and keep the talent they need, but not overpaying in the market either,” says Tanya Jansen, beqom CMO. “Having this advanced capability embedded within beqom’s compensation management system means that pay decisions made during regular compensation cycles can be data-driven and optimized to achieve talent goals while keeping costs down.”
“The use cases for applying machine learning to employee rewards are very compelling,” according to Sébastien Baehni, beqom CTO. “ML provides a whole new level of insight for ensuring equal pay, reducing employee turnover, and keeping your pay scales consistently aligned with the market.”
Because the pay prediction happens within the compensation system, rather than by exporting and importing data from a separate analysis tool, the risk of data discrepancies between systems is eliminated.