
Event studies, difference-in-differences, and instrumented A/B tests can infer impact while staying aggregated. We will avoid person-level tracking, instead leaning on store-level time series, randomized signage rotations, and cross-street controls. This preserves privacy and still yields compelling evidence about which interventions boost visits or increase dwell. With disciplined experimentation, you can prove what works, retire what does not, and maintain ethical clarity that customers and regulators will appreciate.

Every block has a personality. Establishing a multi-week baseline, normalized by weather and school calendars, prevents false alarms and disappointment. We will build peer benchmarks by corridor type, transit proximity, and mix of uses, then separate trend from noise using rolling averages and holiday adjustments. These disciplined comparisons transform foot traffic from noisy anecdotes into a sturdy compass that guides predictable planning even through complicated seasonal swings or festival-heavy months.

Analytics earn their keep when they change behavior on the ground. We will propose small, reversible tests: signage repositioning, playlist tempo changes, lighting adjustments, curbside pickup windows, and sample tables aligned to peak flows. Each experiment gets a clear hypothesis, duration, and success metric tied to both footfall and sales. Over time, this habit builds a culture of learning where teams celebrate evidence, not hunches, and customers feel the thoughtful improvements.
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