• Other
  • 18 columns
  • 201 rows
  • 8 formats

Store Locations

This dataset offers a comprehensive view of retail store locations, including detailed address components, opening hours, manager contact information, physical size, and key performance metrics such as sales and customer traffic. It is ideal for retail chain management, geographic analysis, and store performance benchmarking across regions.

  • last updated 29 Oct 2025
  • by GoMask
The brief that made it

Store performance benchmarking and regional analysis

Sample rows

preview · 8 of 201 rows · all 18 columns
store_idstringmonthly_conversion_ratefloatactivebooleancitystringstore_namestringstreet_addressstringstatestringpostal_codestringcountrystringopening_datedateopening_hours_weekdaysstringopening_hours_weekendsstringstore_manager_idstringstore_manager_namestringstore_manager_emailstringsquare_footageintegermonthly_salesfloatmonthly_foot_trafficinteger
NYC00123.4trueNew YorkMetroMart Manhattan123 5th AveNY10001USA2017-03-1307:00-23:0008:00-22:00mgr102Paula Simmonspsimmons@metromart.com9500172500.4525400
LDN-00331.2trueLondonNextShop Oxford45 Oxford StLondonW1D 2DGUK2021-07-2209:00-21:0010:00-18:00mgr207Angela Thomasathomas@nextshop.co.uk320069450.96200
SFO_Uptown_127.1trueSan FranciscoSuperMart Uptown200 Market StCA94102USA2018-05-1808:00-22:0009:00-20:00mgr103Alex Wuawu@supermart.com600011780017300
BER_Stadt_225.6trueBerlinStadtmarkt Mitte12 AlexanderplatzBerlin10178Germany2019-01-0909:00-20:0010:00-18:00mgr210Tobias Kleintklein@stadtmarkt.de290048900.655400
JPN_Tokyo_S119.7trueTokyoShinjuku Mart3-7-1 ShinjukuTokyo160-0022Japan2023-02-0100:00-23:5900:00-23:59mgr301Yuki Nakamuraynakamura@shinjukumart.jp10500208560.3228900
DAL00229.9trueDallasSuperMart Dallas800 Elm StTX75201USA2020-11-2809:00-21:0010:00-18:00mgr103Alex Wuawu@supermart.com210049200.154800
BRI_CBD_432.3trueBrisbaneNextShop Brisbane99 Queen StQLD4000Australia2016-08-1608:00-22:0009:00-18:00mgr207Angela Thomasathomas@nextshop.co.uk3400537803900
SEA_Boutique_142trueSeattleHarvest Seattle512 Pine StWA98101USA2022-05-0410:00-19:0011:00-17:00mgr320Robin Leerlee@harvest.com70012150.22850

What the 201 rows show

from the 201-row sample
  • 81%active = true
  • 29.9median monthly_conversion_rate
  • 9countries
  • 28opening hours weekdayses
  • 31states
  • 32opening hours weekendses
monthly_conversion_rate201 rows, in bands of 10
03060385582110141161325active from 101050100monthly_conversion_rate →

Every row with monthly_conversion_rate of 10 or more has active = true. None below it.

city201 rows · top 10 of 32 values
  1. New York20
  2. Berlin17
  3. London15
  4. São Paulo14
  5. Chicago13
  6. Seattle11
  7. Tokyo10
  8. Paris10
  9. Dallas9
  10. Brisbane9
18 columns by typefrom the column list below
  • string 12
  • integer 2
  • float 2
  • date 1
  • boolean 1

Columns

18 columns in four groups
blueprint · 18 columns
columntypedescriptionexample
Text 12 columns
store_idstringUnique identifier for each retail store locationuniqueNYC001
store_namestringOfficial name of the retail storeMetroMart Manhattan
street_addressstringStreet address of the retail store123 5th Ave
citystringCity where the store is locatedNew York
statestringState or region where the store is locatedNY
postal_codestringPostal or ZIP code of the store location10001
countrystringCountry where the store is located9 countriesUSA
opening_hours_weekdaysstringStore opening hours for weekdays (e.g., '09:00-21:00')07:00-23:00
opening_hours_weekendsstringStore opening hours for weekends (e.g., '10:00-18:00')08:00-22:00
store_manager_idstringUnique identifier for the store managermgr102
store_manager_namestringFull name of the store managerPaula Simmons
store_manager_emailstringEmail address of the store managerpsimmons@metromart.com
Numbers 4 columns
square_footageintegerTotal square footage of the store100 or more9500
monthly_salesfloatTotal sales for the most recent month (in local currency)0 or more172500.45
monthly_foot_trafficintegerNumber of customer visits in the most recent month0 or more25400
monthly_conversion_ratefloatPercentage of visitors who made a purchase in the most recent month0 to 10023.4
Dates and times 1 column
opening_datedateDate when the store opened for business2017-03-13
True or false 1 column
activebooleanIndicates if the store is currently open and operatingtrue

Use it for

  • active81%163 of 201 rows

    A dashboard

    The active rate, monthly_conversion_rate and a breakdown of city. Excel, Power BI or Tableau.

  • Why do 163 of 201 rows have active = true?

    A class exercise

    Hand out the rows and one question. Everyone works from the same 201 rows.

  • A software demo

    Believable stores with store_name, street_address and city to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 18 columns, your size and your rules. See 20 rows before you pay.

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This dataset201 rows18 columns
Yours1,000 rows18 columnsstreet_address: UK only

blueprint · store-locations

Behind this dataset

Same schema. As many rows as you need.

These 201 rows came out of a blueprint — 18 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.

Rules it was built with
  • Store ID unique across chain
  • Opening hours vary by day of week
  • Square footage determines staffing levels
  • Manager assigned to each location
  • Performance metrics by location
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
store-locations

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