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How I Use Power BI for Gig Worker Tracking
Power BI has been my go-to tool for tracking gig worker performance at Swiggy—bringing data to life with real-time dashboards, shift-wise insights, and SLA tracking.
It’s not just reporting, it’s making decisions smarter and faster every single day.
How I Use Power BI for Gig Worker Tracking
In the fast-paced world of food delivery, real-time performance tracking isn’t just helpful—it’s critical. As a Business Analyst at Swiggy, I rely on Power BI to monitor, analyze, and improve the performance of our gig workforce.
Here’s a step-by-step breakdown of how I use it to bring value to daily operations:
🔍 What’s Being Tracked?
| 🧩 Metric | 📌 Use Case |
|---|---|
| ✅ Total Orders per Day | Tracks activity level and daily trends |
| ⏱️ Avg. Delivery Time | Helps gauge punctuality and efficiency |
| 💸 Payout per Order | Monitors incentive costs per gig |
| 🌙 Shift-wise Performance | Identifies under/over-performing shifts |
| ❌ Missed/Rejected Orders | Flags operational or behavioral gaps |
| 🗺️ Zone-wise Distribution | Optimizes coverage and zone assignments |
| 📈 Hourly Trends | Reveals demand surges and off-peak patterns |
| 📊 Order-to-Incentive Ratio | Evaluates payout fairness and ROI |
| 🔁 Repeat Gigs per Worker | Measures reliability and retention |
| ⚠️ SLA Breaches | Tracks service level commitment failures |
💡 Power BI Features I Use Daily:
| Feature | Why I Use It |
|---|---|
| Live Data Sync | Connects to Excel/Sheets/SQL for real-time updates |
| Slicers & Filters | Enable dynamic comparisons across date, zone, or worker |
| Custom DAX Measures | Calculate metrics like Avg. payout/hr, Completion Ratio |
| Conditional Formatting | Highlights red flags (e.g. high rejections or SLA breaches) |
| Drill-through Pages | Lets me view a gig worker’s detailed history |
| Bookmarks | Saves view states for recurring presentations |
| Tooltips | Provides quick contextual info while hovering over visuals |
💥 Real Impact Achieved:
- 📊 Saved 8+ hours per week of manual report building and formatting
- 🔍 Identified low-performing shifts and rebalanced incentives accordingly
- 📍 Localized delivery issues to specific zones using heat maps
- 🧠 Shared insights seamlessly with stakeholders using exported visuals
- ⏱️ Improved SLA compliance through weekly performance visibility
- 💬 Fostered data-driven decision-making in daily huddles with Ops/HR
💬 Pro Tip for Beginners:
“Focus on building one actionable visual at a time. Think like a decision-maker: What would help you act faster? Then layer in complexity.”
📥
In the next post, I’ll take you through a real-world case where I dealt with surge pricing data, filtered key pain points, and delivered insights that drove change. Stay tuned!
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