LinkedIn Recruiter is a powerful platform for recruiters to connect with potential candidates. To assess your team's performance, we can analyse a LinkedIn usage report and derive valuable insights from four key KPIs.
You can of course use more KPIs or develop your own. I use ChatGPT Code Interpreter to generate these insights for me. It saves a lot of time and effort — and you can even ask follow-up questions based on the data.
By combining these KPIs and incorporating additional context, we are going to generate charts and identify the top performers as well as the team members who would benefit most from coaching and training.
Step 1: Export the LinkedIn Usage Report
- Log in to your LinkedIn Recruiter account.
- Navigate to the dashboard or reporting section.
- Find the option to export a usage report: for at least three, preferably six months.
- Export the report in a compatible format, such as Excel.
Step 2: Prepare the Data
- Open the exported LinkedIn usage report in spreadsheet software (e.g. Microsoft Excel or Google Sheets).
- Check the data to make sure it contains the necessary information and has no inconsistencies or missing values.
- Clean the data if needed by removing irrelevant columns or rows.
Step 3: Upload the Data in ChatGPT Code Interpreter
(You need ChatGPT Plus for this.)
Upload the file using the + icon, then add the following prompt:
Here is a report of a LinkedIn Recruiter account. I want you to analyse all the information there. Four key metrics: #1: The number of saved profiles divided by the number viewed should be as high as possible. Combine this with the number of searches performed. This indicates that recruiters can search for the right target audience. #2: The number of messages sent divided by saved profiles must equal or exceed 1. This indicates that all saved profiles are messaged at least once. #3: The number of messages sent is a good metric. Combine this with a high number of messages accepted, which indicates strong performance. Many messages sent with low acceptance shows they are spamming their talent pool. #4: The number of searches and search alerts saved indicates that they automate some of their work. Can you develop graphs based on the four metrics above and anything you can combine yourself? And give a list based on these metrics of the top 3 performers and the top 3 who need additional coaching and training. Please also indicate in which areas the top 3 need extra coaching.
