About Prompt
- Prompt Type – Dynamic
- Prompt Platform – ChatGPT, Grok, Deepseek, Gemini, Copilot, Midjourney, Meta AI and more
- Niche – Compensation Benchmarking
- Language – English
- Category – HR Analytics
- Prompt Title – AI Prompt for Predicting Optimal Compensation Packages Based on Market Data
Prompt Details
**Prompt Type:** Dynamic
**Purpose:** HR Analytics – Compensation Benchmarking
**Target Platform:** All AI Platforms (optimized for adaptability)
**Instructions:**
This prompt aims to leverage market data to predict optimal compensation packages for specific roles within an organization. You will be provided with structured data and parameters, and your task is to analyze this information and generate recommended compensation ranges and structures. The output should be actionable insights that HR professionals can use for salary benchmarking, offer creation, and overall compensation strategy.
**Input Data Format:**
The input data will be provided in one or more of the following formats (specify format when providing data):
* **CSV/Excel:** Including columns for `Job Title`, `Location`, `Years of Experience`, `Education Level`, `Skills`, `Industry`, `Company Size`, `Base Salary`, `Bonus`, `Equity`, `Total Compensation`, and other relevant compensation components. Ensure clear column headers and consistent data formats.
* **JSON:** A structured JSON object containing similar information as the CSV/Excel format, with nested objects for complex compensation components if needed.
* **Natural Language (Descriptive Text):** A textual description of the job role, including details like responsibilities, required skills, location, and desired experience level. In this case, you should extract relevant features and request clarification if necessary.
**Input Parameters:**
Along with the data, the prompt will include the following parameters:
* **Target Job Title:** The specific job title for which the optimal compensation needs to be predicted.
* **Target Location:** The geographic location for the role.
* **Target Years of Experience:** The desired experience level for the candidate.
* **Target Education Level:** The required education level for the role.
* **Target Skills (optional):** Specific skills required for the role (comma-separated or as a list).
* **Company Size (optional):** The size of the organization (e.g., Small, Medium, Large).
* **Industry (optional):** The industry in which the organization operates.
* **Percentile Target (optional):** The desired market percentile for the compensation (e.g., 50th percentile for median compensation, 75th for top quartile). Default is 50th percentile.
* **Focus (optional):** Specify if the prediction should prioritize any specific aspect, such as “Cost-effectiveness,” “Competitiveness,” or “Attracting top talent.”
**Output Format:**
The output should be structured and easy to understand. It should include:
* **Recommended Salary Range:** Provide a minimum, midpoint, and maximum salary range for the base salary.
* **Recommended Total Compensation Range:** Provide a minimum, midpoint, and maximum range for the total compensation, including base salary, bonus, and equity.
* **Breakdown of Compensation Components:** Specify the recommended proportions for each compensation component (e.g., Base Salary: 70%, Bonus: 15%, Equity: 15%).
* **Market Percentile:** Indicate the market percentile that the recommended compensation represents.
* **Confidence Level (optional):** Provide a confidence level for the prediction, if possible.
* **Key Market Insights (optional):** Provide any relevant insights derived from the market data, such as current market trends or competitive landscape.
* **Data Sources Used (optional):** If the AI platform can identify the data sources used for the analysis, it should list them.
**Example Prompt:**
“Predict the optimal compensation package for a Senior Software Engineer in San Francisco, California, with 5-7 years of experience, a Bachelor’s degree in Computer Science, and skills in Java, Python, and AWS. The company is a medium-sized enterprise in the Technology industry. Aim for the 75th percentile for competitiveness. Input data is provided in the attached CSV file ‘SoftwareEngineerSalaries.csv’.”
**Error Handling:**
If the input data is insufficient or ambiguous, request clarification. Specify what additional information is needed and why. If the requested analysis is beyond the capability of the AI, clearly state the limitations.
**Optimization Considerations:**
* **Be as specific as possible** with the input parameters and desired output format.
* **Use clear and concise language.**
* **Provide context** for the analysis by including relevant details about the organization and the job role.
* **Iterate and refine the prompt** based on the initial results.
By following these instructions, you can effectively leverage AI to predict optimal compensation packages and inform data-driven compensation decisions.