Retail Foot Traffic Analytics

Leveraging Placer.ai’s location-analytics tools, I need a detailed examination of foot-traffic patterns across a selection of retail stores. The goal is to turn raw visit data into clear, actionable insights that help me understand when, how often, and for how long shoppers show up at each location.

Scope of work
• Pull historical and recent foot-traffic figures for the specified store list (I will provide addresses and brand names).
• Clean and normalize the data so day-to-day and store-to-store comparisons are reliable.
• Visualize trends—peak hours, weekday vs. weekend shifts, seasonality, and any anomalies—using Placer.ai dashboards or an external BI tool if it exports cleanly.
• Summarize findings in a concise report highlighting notable traffic spikes, lulls, and possible drivers (e.g., promotions, holidays, nearby events).

Deliverables
1. Exported data set (CSV or Excel) pulled directly from Placer.ai.
2. Interactive dashboard or slide deck that walks through key metrics and visuals.
3. Written summary (2-3 pages) outlining insights and recommended next steps.

Acceptance criteria
• All figures traceable to Placer.ai queries shared in read-only mode.
• Time ranges and location filters clearly documented.
• Charts load without errors and match the numbers in the export.

If you have hands-on experience with Placer.ai’s API, that’s a big plus, as automated pulls will speed up future refreshes. Please confirm familiarity with the platform and outline your typical turnaround time.