Foot Traffic Analysis 101

Foot traffic data has fundamentally changed how CRE professionals evaluate locations. Before platforms like Placer.ai, site selection relied on traffic counts from state DOTs, manual clicker surveys, and educated guesses.

Today, mobile device data provides granular insight into exactly how many people visit a location, when they come, where they come from, and where else they shop. This transforms site selection from an art to a science.

This guide covers how to source, interpret, and apply foot traffic data in your site selection analyses.

Total visits measure the raw volume of foot traffic over a given period (typically monthly). This is your starting point — does the location have enough traffic to support the business?

Visits by day of week reveal the trading pattern. A location that peaks on weekdays is different from one that peaks on weekends. Match the pattern to your concept's peak hours.

Visits by hour are critical for concepts tied to specific dayparts. A breakfast/lunch concept needs morning and midday traffic; a dinner concept needs evening traffic.

Dwell time indicates how long visitors stay. High dwell time suggests a destination location; low dwell time suggests pass-through traffic. Both can be positive depending on your concept.

Visitor origin (true trade area) is perhaps the most powerful metric. By analyzing where visitors actually come from, you can define the real trade area rather than using arbitrary radius rings.

Cross-visitation shows what other businesses visitors frequent. If visitors to a nearby gym also visit smoothie shops and health food stores, that's a signal about the customer profile.

Raw traffic numbers need context to be useful. Here's how to interpret common patterns:

High weekday, low weekend: Indicates an office/employment-driven location. Good for lunch concepts, professional services, and weekday retail. Less suitable for family-oriented concepts.

High weekend, low weekday: Destination or leisure-driven location. Strong for entertainment, specialty retail, and experiential concepts.

Consistent daily traffic: Often indicates a neighborhood center anchored by a grocery store. Good for convenience-driven concepts like quick-service restaurants, dry cleaners, and pharmacies.

Seasonal spikes: Look for locations with extreme seasonality (beach towns, ski areas, college towns) and consider whether your concept can withstand low-season drops.

Year-over-year trends: Is traffic growing or declining? A location losing 10% of its traffic annually is a red flag regardless of current volume.

Foot traffic data is most powerful when used to compare candidate sites objectively.

Side-by-side comparison framework: 1. Normalize for trade area population — traffic per 1,000 residents 2. Compare peak-hour alignment with your concept's trading hours 3. Evaluate visitor origin overlap — are you reaching unique customers? 4. Assess cross-visitation compatibility with your target customer 5. Weight for quality: 100 visits from your target demographic outweigh 1,000 from a non-target group

Practical example: Site A has 50,000 monthly visits with 70% from within 5 miles and strong lunch-hour peaks. Site B has 80,000 monthly visits but they're heavily weekend-skewed with visitors driving 15+ miles. For a fast-casual lunch concept, Site A is likely stronger despite lower total traffic.

Slant automates this comparison by pulling Placer traffic data directly into side-by-side site analysis reports.

The most powerful site selection analyses layer foot traffic data on top of demographic data.

The intersection of traffic and demographics tells you: - Whether the people visiting the area match your target customer profile - Whether high traffic translates to purchasing power - Whether the trade area is growing in both population and visits

Example: A location with moderate foot traffic (30,000/month) but a high-income, growing population may outperform a location with 60,000 monthly visits in a declining, low-income trade area — because the quality of traffic matters as much as the quantity.

By combining Esri demographics with Placer traffic data and Google Places competitive analysis, modern platforms like Slant give CRE professionals a complete picture of location quality that would take days to assemble manually.