FMCG success story

Building competitive pay and removing bias in compensation 

A global leader in fast moving consumer goods, with employees around the world, was seeking to optimize their global compensation strategy and bring greater efficiency and fairness to their rewards processes. The best solution was to enhance their existing compensation capabilities in beqom by adding compensation recommendations based on sophisticated ML regression models.

Challenge

Making compensation fair and competitive

As a truly global organization that operates in more than 100 countries and carries out marketing activities worldwide, the company relies on strategic compensation packages to attract and retain top talent. Having competitive and fair compensation is an important part of its employer brand.

To make employee compensation more competitive and avoid any pay inequities, the company sought to build smart recommendations into their comp planning process, based on Machine Learning (ML) predictions. These recommendations would need to take into consideration all employee attributes and compensation details.
 
The smart recommendations needed to be able to be applied to any population to support data-driven and unbiased new pay ranges, organization restructuring, and new acquisitions. The ML capabilities also would be used at the individual level to forecast fair and appropriate compensation at any point along the employee journey: merit increases, promotion recommendations in conjunction with other actions like internal transfer, focal point increases, ad hoc recommendations, or pay recommendations for new hires.

Solution

Applying machine learning to global compensation processes

Adding AI to beqom compensation. The company was already using beqom to manage its annual Rewards cycle (Ratings/Merit and Bonus) in many locations around the world. Based on its strong working relationship with beqom, the HR team decided to implement beqom’s Artificial Intelligence (AI) capabilities.

Predicting with accuracy and flexibility. The solution employs mulitple comp prediction models, hosted directly on the beqom ML platform, with data model extension capabilities to manage all variables. The models are connected to the compensation data to define the prediction model associated with each employee. The platform launches one or several models in bulk or employee-by-employee to produce compensation recommendations for existing employees and future recruitment.

Embedded in the comp workflow. Recommendations are integrated into the comp review process, so managers and HR can use salary predictions as guidelines during the normal comp cycle workflow. 

Easy-to-use guidance for managers or HR. A line manager or HR manager can enter a new position, promotion, role, or location for a specific employee and get a comp recommendation range calculated by the model in beqom ML. The manager can see a comparison of current salary versus recommendation and can accept or reject the recommendation.

Constantly improving prediction accuracy. The Comp & Ben team can access specialized dashboards to access model outcomes to gain further insight into the effectiveness of each model and prediction accuracy.

Real time decision support. Because all relevant data is in one place and is provided in real-time, managers and HR can make timely decisions and apply remediation using the appropriate predictive model.

Benefits

Competing for talent effectively

beqom’s embedded ML engine helps a company run any prediction scenario and gain valuable insights to improve compensation decisions and strengthen the strategic impact of compensation on employee engagement, retention, and recruitment. 
The key benefits of using beqom Machine Learning are:

Support recruitment and retention

• Use data to design competitive pay strategies for each of your markets.
• Remove bias to ensure fair pay decisions.
• Identify at-risk employees due to pay gaps and remediate.

Control costs and risk

• Design data-driven pay strategies that optimize costs for each location.
• Prevent costly attrition.
• Reduce risk and ensure compliance with fair pay regulations.

Improve the user experience

• Help managers make good pay decisions.
• Improve the efficiency of the compensation cycle.
• Boost employee trust and the employer brand.