Figures' Recommendations tool allows you to create tailored salary increase suggestions for each employee based on their performance and market or salary band positioning. This article guides you through the process of setting up these recommendations.
Recommendation options
Your options for creating recommendations depend on your choice in the Budget Definition step:
If you selected Smart Allocation in the Budget step:
You must use Figures' built-in matrix tool to generate recommendations based on performance and positioning.
If you did not select Smart Allocation in the Budget step, you have two options:
Upload Recommendations: Import your own pre-defined recommendations via Excel.
No Recommendations: Skip this step if you prefer not to provide initial recommendations.
Using smart allocation
If you've chosen Smart Allocation in the Budget Definition step, follow these steps:
Select adjustment method
Choose between:
Market Positioning
Salary Bands Positioning (if you use the Salary Bands feature)
Set budget allocation
Decide what portion of your total budget to allocate for system-generated recommendations. This allows you to reserve some budget for managerial discretion or exceptional cases.
Adjust performance and positioning focus
Use the sliders to balance between:
Performance reward: How much to emphasize performance ratings
Bands/Market adjustment: How much to focus on aligning salaries with market or band positioning
Fine-tune the recommendation matrix
The matrix shows recommended increases based on performance and positioning. Use the forbidden icon to exclude certain categories from increases (e.g., low performers or those already above range)
Apply recommendations
Click "Apply recommendations distribution" to see how these settings affect individual employee recommendations.
Review and adjust
Examine the employee table to see personalized recommendations. Figures uses a technique called interpolation to create more precise recommendations beyond the basic matrix values. Here's how it works:
The matrix provides increase percentages for specific combinations of performance and positioning (e.g., "Meets Expectations" and "Bottom range").
However, employees' actual situations often fall between these defined points. For example, an employee might be slightly above the bottom of their range, but not quite at the midpoint.
Interpolation allows Figures to calculate a more accurate recommendation by considering exactly where an employee falls within their range and their specific performance rating.
This results in smoother, more nuanced recommendations that better reflect each employee's unique situation.
For example:
The matrix might show 6% for "Meets Expectations" at "Bottom range" and 4% for "Mid range".
An employee who is 25% of the way from "Bottom range" to "Mid range" might receive a recommendation of 5.5%, reflecting their position between these two points.
This approach ensures that small differences in positioning or performance translate into appropriately scaled recommendations, promoting fairness and precision in the review process.
Optional: Fine-tuning recommendations
After applying the recommendations distribution, you have the option to make manual adjustments for specific cases:
Export current recommendations:
At the bottom of the page, you'll find an option to download the current recommendations as an Excel file.
This allows you to review all recommendations in detail outside of the Figures interface.
Manual adjustments (if needed):
Open the exported Excel file.
Make any necessary adjustments to individual employee recommendations.
This can be useful for handling unique situations that the matrix doesn't fully capture.
Import adjusted recommendations:
If you've made changes in Excel, you can import your modified file back into Figures.
This will update the recommendations with your manual adjustments.
Remember, this step is entirely optional. It's designed for situations where you need to fine-tune recommendations for specific employees or groups. In most cases, the smart allocation and matrix should provide suitable recommendations without needing manual intervention.