Retail Foot Traffic Estimator

Retail Foot Traffic Estimator predicts daily visitors using location, store size, population, marketing, and seasonality for optimized retail planning.

Nearby Population (thousands):

Location Type:

Store Size:

Conversion Rate (%):

Seasonal Factor:

Number of Marketing Campaigns:

Formulas Used in Retail Foot Traffic Estimator

The calculator uses the following formulas to estimate foot traffic:

Base Foot Traffic:

\\[ T_{\text{base}} = P \cdot L \cdot S \\]

Marketing-Adjusted Traffic:

\\[ T_{\text{mkt}} = T_{\text{base}} \cdot (1 + \sum M_i \cdot E_i) \\]

Conversion-Adjusted Traffic:

\\[ T_{\text{conv}} = T_{\text{mkt}} \cdot C \cdot F \\]

Traffic Efficiency Score:

\\[ S = \min\left(100 \cdot \frac{T_{\text{conv}}}{T_{\text{max}}}, 100\right) \\]

Where:

\\( T_{\text{base}} \\): Base daily foot traffic (visitors/day)
\\( P \\): Nearby population (thousands)
\\( L \\): Location factor (Urban: 0.05, Suburban: 0.03, Rural: 0.01)
\\( S \\): Store size factor (Small: 0.5, Medium: 1.0, Large: 1.5)
\\( T_{\text{mkt}} \\): Marketing-adjusted daily foot traffic (visitors/day)
\\( M_i \\): Marketing effort for campaign \\( i \\) (USD/thousand)
\\( E_i \\): Marketing effectiveness factor (Social Media: 0.1, Local Ads: 0.08, Promotions: 0.06)
\\( T_{\text{conv}} \\): Conversion-adjusted daily foot traffic (potential buyers/day)
\\( C \\): Conversion rate (decimal)
\\( F \\): Seasonal factor (Normal: 1.0, Holiday: 1.5, Off-Season: 0.8)
\\( S \\): Traffic efficiency score (%)
\\( T_{\text{max}} \\): Maximum reference traffic (1000 visitors/day)

Example Calculations

Example 1: Small Store, No Campaigns

Input: Population = 50 thousand, Location = Urban, Store Size = Small, Conversion Rate = 15%, Seasonal Factor = Normal, Campaigns = 0

\\[ T_{\text{base}} = P \cdot L \cdot S = 50 \cdot 0.05 \cdot 0.5 = 1.25 \ \text{visitors/day} \\] \\[ T_{\text{mkt}} = T_{\text{base}} \cdot (1 + 0) = 1.25 \cdot 1 = 1.25 \ \text{visitors/day} \\] \\[ T_{\text{conv}} = T_{\text{mkt}} \cdot C \cdot F = 1.25 \cdot 0.15 \cdot 1.0 = 0.1875 \ \text{potential buyers/day} \\] \\[ S = \min\left(100 \cdot \frac{T_{\text{conv}}}{T_{\text{max}}}, 100\right) = \min\left(100 \cdot \frac{0.1875}{1000}, 100\right) = 0.01875 \ \% \\]

Result: Base Traffic: 1.25 visitors/day, Marketing-Adjusted Traffic: 1.25 visitors/day, Conversion-Adjusted Traffic: 0.19 potential buyers/day, Efficiency Score: 0.02%

Example 2: Medium Store, Two Campaigns

Input: Population = 100 thousand, Location = Suburban, Store Size = Medium, Conversion Rate = 20%, Seasonal Factor = Holiday, Campaigns = 2 (Social Media: 5 USD/thousand; Local Ads: 3 USD/thousand)

\\[ T_{\text{base}} = P \cdot L \cdot S = 100 \cdot 0.03 \cdot 1.0 = 3 \ \text{visitors/day} \\] \\[ T_{\text{mkt}} = T_{\text{base}} \cdot (1 + \sum M_i \cdot E_i) = 3 \cdot (1 + (5 \cdot 0.1 + 3 \cdot 0.08)) = 3 \cdot (1 + 0.5 + 0.24) = 3 \cdot 1.74 = 5.22 \ \text{visitors/day} \\] \\[ T_{\text{conv}} = T_{\text{mkt}} \cdot C \cdot F = 5.22 \cdot 0.2 \cdot 1.5 = 1.566 \ \text{potential buyers/day} \\] \\[ S = \min\left(100 \cdot \frac{T_{\text{conv}}}{T_{\text{max}}}, 100\right) = \min\left(100 \cdot \frac{1.566}{1000}, 100\right) = 0.1566 \ \% \\]

Result: Base Traffic: 3 visitors/day, Marketing-Adjusted Traffic: 5.22 visitors/day, Conversion-Adjusted Traffic: 1.57 potential buyers/day, Efficiency Score: 0.16%

Example 3: Large Store, Three Campaigns

Input: Population = 200 thousand, Location = Urban, Store Size = Large, Conversion Rate = 25%, Seasonal Factor = Holiday, Campaigns = 3 (Social Media: 10 USD/thousand; Local Ads: 8 USD/thousand; Promotions: 5 USD/thousand)

\\[ T_{\text{base}} = P \cdot L \cdot S = 200 \cdot 0.05 \cdot 1.5 = 15 \ \text{visitors/day} \\] \\[ T_{\text{mkt}} = T_{\text{base}} \cdot (1 + \sum M_i \cdot E_i) = 15 \cdot (1 + (10 \cdot 0.1 + 8 \cdot 0.08 + 5 \cdot 0.06)) = 15 \cdot (1 + 1 + 0.64 + 0.3) = 15 \cdot 2.94 = 44.1 \ \text{visitors/day} \\] \\[ T_{\text{conv}} = T_{\text{mkt}} \cdot C \cdot F = 44.1 \cdot 0.25 \cdot 1.5 = 16.5375 \ \text{potential buyers/day} \\] \\[ S = \min\left(100 \cdot \frac{T_{\text{conv}}}{T_{\text{max}}}, 100\right) = \min\left(100 \cdot \frac{16.5375}{1000}, 100\right) = 1.65375 \ \% \\]

Result: Base Traffic: 15 visitors/day, Marketing-Adjusted Traffic: 44.1 visitors/day, Conversion-Adjusted Traffic: 16.54 potential buyers/day, Efficiency Score: 1.65%