Delivery Tip

Context

I wanted to know which shifts and neighborhoods lead to better tips.

Approach

• Built a geospatial heat map and pivot filters from 1,251 deliveries.
• Compared tips by daypart, zone, and order size.

Impact

• Wednesday had the highest average tip per delivery ($8.07).
• Friday had the best tips per hour ($10.34/hour).
• Using the changes, I increased my weekly earnings by about 12%.

Dataset and Cleaning

I analyzed delivery tickets and built an Excel workflow that drivers can keep updating.

• Normalized timestamps and derived delivery time (minutes) and tip percentage.
• Standardized location fields (city and neighborhood) for mapping and rollups.
• Used Power Query so refresh doesn't require manual cleanup.

Geo-Analytics Dashboard

• Built a tip heatmap by neighborhood to spot strong zones.
• Added pivot filters for housing type, gated communities, city, and order size.
• Added weekday and shift-level summaries for scheduling.

Key Findings

• Wednesday had the highest average tip per delivery ($8.07); Friday had the best tips per hour ($10.34/hour).
• Tips varied a lot by housing type and neighborhood, which helped with zone choices.
• Tracked baseline stats (average tip and delivery time) to measure changes over time.

What I'd Improve

• Add distance and drive-time estimates to separate 'better zones' from 'shorter routes'.
• Control for order size to avoid confusing high tips with high bills.
• Turn insights into a simple 'where to go next' recommendation view for live shifts.

Links

PDF Excel