CCBM Course - Certified Compensation and Benefits Management

Total Rewards Redesign for a Multi-Site Cross-Industry Organisation

Participants take on the role of a newly appointed Compensation and Benefits Manager for a mid-sized organisation operating across three business units in Banking, Retail, and Professional Services. The organisation has grown rapidly through acquisition, leaving behind a fragmented pay structure with inconsistent job grades, no formal benchmarking process, and a benefits package that varies arbitrarily by site. Employee relations concerns about pay fairness are escalating, and the CFO has mandated a cost-neutral total rewards review within one quarter. Participants must diagnose the current state, identify the most critical equity and compliance gaps, and agree on a redesign scope before the simulation clock starts.

Working in cross-industry teams, participants apply point-factor job evaluation to a set of benchmark roles, build a unified salary structure with five pay grades, and model the cost impact of moving out-of-range employees to the new bands. They then design a short-term incentive framework for the sales unit and a flexible benefits menu for the retail workforce, selecting vendors and estimating total cost. The simulation concludes with each team presenting a board-ready Compensation and Benefits Proposal, including a compliance checklist and a two- audience communication plan, to a panel of facilitators acting as senior leadership. Feedback is given in real time, mirroring the stakeholder scrutiny participants will face in their actual roles.

Compensation Benchmarking and Pay Equity Automation Practice Environment

Participants enter a simulated compensation technology environment pre-loaded with anonymised salary survey data, job grading outputs, and a live HR analytics dashboard. Using AI-assisted tools including Microsoft Copilot and a compensation modelling platform, they automate the benchmarking workflow from data ingestion through market-position calculation to pay range recommendation. The sandbox is structured so that participants can test multiple positioning strategies, lead, lag, or match, and immediately see the cost and equity implications of each choice without risk to real payroll data. Each participant works through a guided scenario that mirrors the benchmarking cycle they will manage back in their organisation.

In the second phase of the sandbox, participants use generative AI prompts to draft merit matrix templates, model increment budget scenarios, and generate a pay equity summary report flagging compression and outlier cases. Automated workflows are built to pull updated survey data, apply ageing factors, and refresh pay range midpoints on a defined schedule. Governance checkpoints are embedded throughout: every AI-generated output requires human review and sign-off before it is accepted into the compensation record, reinforcing the principle that technology supports, but does not replace, professional judgment. Participants leave with a reusable automation workflow they can adapt and deploy in their own compensation administration cycle.