Prompt
“Turn this data/spreadsheet into a narrative story that a non-technical executive would understand, with the key insight as the headline.”
Source
I used this IBM HR Analytics Employee Attrition & Performance dataset from Kaggle to test the prompt.
Model
I tested this prompt using Claude 4.1 Opus.
Results
Looking at this HR Employee Attrition dataset, here’s the narrative story:
“Your Company Tracks 35 Employee Metrics—But Which Ones Actually Predict Who Will Quit?”
The Executive Summary:
We have data on 1,470 employees across your organization, tracking everything from their commute distance to their stock options. This dataset tells the story of workplace satisfaction, career progression, and ultimately, who stays and who leaves.
The Big Picture:
Think of this as your company’s employee health dashboard. Just like a doctor measures blood pressure, cholesterol, and heart rate to predict health issues, we’re measuring job satisfaction, work-life balance, and years since last promotion to predict attrition.
What We’re Actually Measuring:
Your HR system captures three types of employee stories:
The Journey Story: How far employees travel to work, how many companies they’ve worked for before joining yours, and how long they’ve been in their current role. These tell us about employee commitment and career stability.
The Satisfaction Story: Job satisfaction, environment satisfaction, relationship satisfaction, and work-life balance ratings. These are like employee vital signs—when multiple satisfaction scores drop, attrition risk rises.
The Growth Story: Years since last promotion, training times, salary hike percentages, and job levels. These reveal whether employees see a future at your company or feel stuck.
The Hidden Patterns:
Some surprising factors matter more than expected. That “OverTime” field? It’s not just about extra hours—it’s about sustainable work patterns. The “YearsSinceLastPromotion” combined with “PerformanceRating” tells us who feels overlooked despite good performance. And “NumCompaniesWorked” reveals whether you’re hiring job-hoppers or career builders.
The Actionable Insight:
Instead of treating all 35 metrics equally, we can identify the 5-7 factors that actually predict attrition. This means HR can stop playing whack-a-mole with retention programs and instead focus on the specific levers that keep your talent from walking out the door.
What This Means for Your Business:
Every unexpected departure costs you 50-200% of that employee’s annual salary in recruitment, training, and lost productivity. By identifying at-risk employees early using these predictive patterns, you can intervene before they update their LinkedIn profiles.
This dataset is essentially your early warning system for talent retention—if you know how to read the signals.