Human resources always have to deal with a lot of data, so let’s imagine that it’s coming toward the end of the day, at the end of the week, and your boss comes in and says, “Look, we’ve got some data here that we need to look at. Our CEO was told by one of his friends that our Glassdoor.com comments aren’t the greatest, so let’s take a look through the data and see what it says.”
Here is the dataset: (click here)
After inputting the dataset into ChatGPT, you can simply ask:
{Prompt}
Let's look at employee reviews. I want to gain some insights from them.
{AI}
Summary of Key Employee Review Statistics
1. Total number of reviews: 22
2. Rating distribution:
● 5 stars: 8 reviews (36.4%)
● 4 stars: 6 reviews (27.3%)
● 3 stars: 2 reviews (9.1%)
● 2 stars: 2 reviews (9.1%)
● 1 star: 3 reviews (13.6%)
● Not rated: 1 review (4.5%)
3. Average rating: 3.7 out of 5 (based on 21 rated reviews)
4. Employment status:
● Current employees: 7 (31.8%)
● Former employees: 10 (45.5%)
● Not specified: 5 (22.7%)
5. Length of employment (where specified):
● Less than 1 year: 4
● 1 to 2 years: 5
● 3 to 4 years: 5
● 5 to 6 years: 1
● 7 to 8 years: 1
6. Job roles represented: Various, including executives, managers, developers, customer service roles, and interns.
7. Date range of reviews: June 2015 to September 2022 (approximately 7 years)
{Prompt}
Please group the roles by business unit and staff function and then provide the average years of service and an average rating.
{AI}
Certainly. I’ll group the roles by business unit and staff function, then calculate the average years of service and average rating for each group. This will give us a clearer picture of how different areas of the company compare.
Average Years of Employment by Role
1. AVP: 1.5 years (based on 2 entries)
2. Customer Service Team Manager: 7.5 years (based on 1 entry)
3. Policy Servicing Department: 1.5 years (based on 1 entry)
4. Retail Insurance Manager: 3.5 years (based on 1 entry)
5. Sales support executive: 2.25 years (based on 1 entry)
6. In-house Adjuster: 0.5 years (based on 1 entry)
7. Senior Actuarial Analyst: 3.5 years (based on 1 entry)
8. Data Specialist: 3.5 years (based on 1 entry)
9. Customer care service: 0.5 years (based on 1 entry)
10. Purchasing clerk: 1.5 years (based on 1 entry)
11. Financial advisor: 0.5 years (based on 1 entry)
12. Claim processor: 1.5 years (based on 1 entry)
13. Manager, Strategic Development and Engagement: 5.5 years (based on 1 entry)
14. Telesales executive: 0.5 years (based on 1 entry)
15. Retail Bancasurance Manager: 3.5 years (based on 1 entry)
16. Senior manager: 1.5 years (based on 1 entry)
17. Head of CSR: 3.5 years (based on 1 entry)
18. Java Developer: 3.5 years (based on 1 entry)
19. Intern: 0.5 years (based on 1 entry)
20. Data Scientist: 0.5 years (based on 1 entry)
Here are the prompts you can try out with the dataset.
{Prompt}
Which staff function should we dedicate more HR resources to improve their rating and years of service? What should we do?
This example came from the chapter "Using AI to Analyze Data: Tackling Last-Minute Human Resource" in the book "Know Decide Act: 10x Your Productivity at Work with AI and Automation"
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