Tano Case Study: Strategic Resource Allocation in a High-Density Ward
A structural case study (placeholder pending real anonymized tenant data) showing how a campaign identified its most dense polling-station clusters and allocated volunteers to maximize coverage with limited resources.
Structural case study (placeholder). This outlines how to identify high-density polling-station clusters and allocate volunteers to maximize coverage with limited resources. It will be replaced with a real, anonymized tenant success story once platform usage accumulates.
The problem
A ward with 45 polling stations and a volunteer team of 15 canvassers cannot cover every voter equally. The challenge is identifying which stations and neighbourhoods deserve the most attention—and where limited volunteer hours will have the biggest impact on turnout.
The analysis
Start with voter density: which polling stations have the most registered voters per geographic area? High-density stations cluster in neighbourhoods where a single canvasser can reach hundreds of potential supporters in a single afternoon. Low-density stations are spread out—each conversation costs more travel time.
| Metric | High-density cluster | Low-density cluster |
|---|---|---|
| Voters per square km | 800+ | Under 200 |
| Stations within 1km | 5–8 | 1–2 |
| Canvasser efficiency | 3–4 conversations/hour | 1–2 conversations/hour |
The allocation strategy
Deploy 70% of volunteer capacity to high-density clusters, 30% to low-density areas. In high-density clusters, assign canvassers to specific polling-station catchments and rotate coverage. In low-density areas, group volunteers into teams to share transport and cover multiple stations in a single day.
The goal is not equal coverage everywhere—it is efficient coverage where it moves the most votes. Density is your efficiency metric.
Outcome metrics
A real case study would report: number of supporters confirmed in high-density vs. low-density areas, turnout uplift in targeted polling stations on election day, and cost-per-confirmed-supporter by cluster type. For now, treat these as the metrics to track when this framework is deployed in a real campaign.