Retail In-Store Foot Traffic Counts
This dataset provides anonymized, daily foot traffic counts for physical retail locations, enriched with store details, holiday indicators, weather conditions, and special event annotations. It enables robust sales forecasting, staffing optimization, and marketing campaign impact analysis by correlating footfall with external factors and store attributes.
Sample rows
preview · 8 of 200 rows · all 15 columns| foot_traffic_idstring | day_of_weekstring | foot_traffic_countinteger | is_holidayboolean | store_statestring | store_idstring | store_namestring | store_street_addressstring | store_citystring | store_postal_codestring | store_countrystring | datedate | weather_conditionstring | special_eventstring | data_sourcestring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| FT00001A | Friday | 1298 | false | CA | S1001X | FreshMart Downtown | 101 Market St. | San Francisco | 94103 | USA | 2023-11-24 | Clear | Black Friday Sale | sensor |
| FT00002B | Saturday | 3402 | false | IL | S1002Z | MegaMart Uptown | 2201 Lincoln Ave. | Chicago | 60614 | USA | 2023-12-23 | Overcast | Last-Minute Holiday Shopping | sensor |
| FT00003C | Friday | 875 | true | IDF | S1003K | Boutique Parisienne | 27 Rue de Rivoli | Paris | 75001 | France | 2023-07-14 | Sunny | Bastille Day | manual count |
| FT00004D | Saturday | 780 | true | ON | S1004R | Maple Leaf Goods | 88 King St E | Toronto | M5C 1G3 | Canada | 2023-07-01 | Rainy | Canada Day | sensor |
| FT00005E | Friday | 1042 | true | NSW | S1005L | Sydney Style | 12 Pitt St | Sydney | 2000 | Australia | 2024-01-26 | Sunny | Australia Day Parade | sensor |
| FT00006F | Monday | 117 | true | NY | S1006V | QuickMart Midtown | 401 6th Ave | New York | 10011 | USA | 2023-12-25 | Snowy | Christmas Day | manual count |
| FT00007G | Sunday | 643 | false | BC | S1007Q | Urban Threads | 310 Granville St | Vancouver | V6C 1S4 | Canada | 2023-12-24 | Foggy | Christmas Eve | sensor |
| FT00008H | Saturday | 1218 | false | QLD | S1008M | CityStyle Mall | 360 Queen St | Brisbane | 4000 | Australia | 2023-11-25 | Humid | Post-Black Friday Deals | sensor |
| FT00009I | Saturday | 938 | true | blank | S1009T | blank | 15 Raffles Blvd | Singapore | 039803 | Singapore | 2024-02-10 | Rainy | Chinese New Year | sensor |
| FT00010J | Thursday | 1223 | true | TX | S1010N | ShopSmart Center | 700 Main St | Dallas | 75202 | USA | 2023-11-23 | Clear | Thanksgiving Day | sensor |
| FT00011K | Friday | 811 | false | CA | S1001X | FreshMart Downtown | 101 Market St. | San Francisco | 94103 | USA | 2023-12-29 | Rainy | blank | sensor |
| FT00012L | Monday | 704 | true | IL | S1002Z | MegaMart Uptown | 2201 Lincoln Ave. | Chicago | 60614 | USA | 2024-02-19 | Snow Showers | Presidents Day | video analytics |
| FT00013M | Sunday | 412 | true | 03 | S1011Q | Nordic Boutique | 12 Karl Johans gate | Oslo | 0154 | Norway | 2023-12-24 | Snowy | Christmas Eve | manual count |
| FT00014N | Saturday | 105 | false | blank | S1009T | blank | 15 Raffles Blvd | Singapore | 039803 | Singapore | 2023-10-14 | Thunderstorm | blank | manual count |
| FT00015O | Tuesday | 777 | true | ENG | S1012F | Boutique Londres | 200 Oxford St. | London | W1D 1NN | UK | 2023-12-26 | Cloudy | Boxing Day | sensor |
| FT00016P | Wednesday | 231 | true | BE | S1013G | Berlin Trend | 45 Friedrichstr. | Berlin | 10117 | Germany | 2023-12-06 | Snowy | St. Nicholas Day | manual count |
| FT00017Q | Thursday | 892 | true | NSW | S1005L | Sydney Style | 12 Pitt St | Sydney | 2000 | Australia | 2024-04-25 | Clear | ANZAC Day | sensor |
| FT00018R | Friday | 465 | true | MI | S1014J | Boutique Milano | 10 Via Torino | Milan | 20123 | Italy | 2023-12-08 | Foggy | Immaculate Conception | manual count |
| FT00019S | Sunday | 1324 | false | TX | S1010N | ShopSmart Center | 700 Main St | Dallas | 75202 | USA | 2023-12-31 | Clear | New Year's Eve Sale | video analytics |
| FT00020T | Monday | 721 | true | CABA | S1015K | Mercado Central | 555 Avenida Corrientes | Buenos Aires | C1043AAB | Argentina | 2024-02-12 | Sunny | Carnival Monday | sensor |
What the 200 rows show
from the 200-row sampleThursday (day of week) stands out: mean foot_
- 50%is_
holiday = true - 703median foot_
traffic_ count - 3data sources
- 16weather conditions
- 18store countries
- 37store cities
Median 703, from 0 to 10,023.
- string 12
- integer 1
- date 1
- boolean 1
Columns
15 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 12 columns | |||
foot_traffic_id | string | Unique identifier for each foot traffic recordunique | FT00001A |
store_id | string | Unique identifier for the retail store location | S1001X |
store_name | string | Name of the retail store locationoptional | FreshMart Downtown |
store_street_address | string | Street address of the retail store locationoptional | 101 Market St. |
store_city | string | City where the retail store is locatedoptional | San Francisco |
store_state | string | State or province where the retail store is locatedoptional | CA |
store_postal_code | string | Postal or ZIP code for the retail store locationoptional | 94103 |
store_country | string | Country where the retail store is locatedoptional | USA |
day_of_week | string | Day of the week corresponding to the date (e.g., Monday, Tuesday)7 values · optional | Friday |
weather_condition | string | General weather condition on the recorded date (e.g., Sunny, Rainy, Snowy)optional | Clear |
special_event | string | Name or description of any special event or campaign affecting foot traffic (if applicable)optional | Black Friday Sale |
data_source | string | Source or method used to collect the foot traffic data (e.g., sensor, manual count)3 sources · optional | sensor |
| Numbers 1 column | |||
foot_traffic_count | integer | Total number of people who entered the store on the given date0 or more | 1298 |
| Dates and times 1 column | |||
date | date | Date for which the foot traffic count is recorded | 2023-11-24 |
| True or false 1 column | |||
is_holiday | boolean | Indicates if the date is a public holiday in the store's locationoptional | false |
Use it for
A dashboard
The is_
holiday rate, foot_ traffic_ count by day_ of_ week and a breakdown of store_ state. Excel, Power BI or Tableau. Why do the 18 Thursday rows have a mean foot_
traffic_ count of 2,880? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Foot traffics200FT00001A1298FridayFT00003C875FridayFT00004D780Saturday
A software demo
Believable foot traffics with store_
id, store_ name and store_ street_ address to fill a screen in front of a buyer.
blueprint · retail-in-store-foot-traffic-counts
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 15 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
- Counts vary by day-of-week
- Promotions spike traffic patterns
- Weather events impact store visits
- Store type influences baseline counts
- Special events produce outlier values
1 credit per row. New accounts start with 25 free credits.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- retail-in-store-foot-traffic-counts