https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17636898%2F184b6d8052250ba87a287246aa21df07%2FScreenshot%202024-04-15%20at%209.37.34%20AM.png?generation=1713155898550914&alt=media" alt=""> This dataset is designed for learning customer segmentation concepts, such as market basket analysis. It includes basic customer data such as Customer ID, age, gender, annual income, and spending score, which is assigned based on customer behavior and purchasing data. The goal is to help a supermarket mall owner understand their customers better, identify target customers who are likely to converge, and provide insights to the marketing team for strategic planning.

To this day, the Geodatindustry database is the world's most complete and accurate in the retail, commercial and industry area, with 25 years of experience and a qualified teams.

Geodatindustry Database is the perfect tool to lead your decision making, market analytics, strategy building, prospecting, advertizing compaigns, etc.

By purchasing this dataset, you gain access to more than 18,000 shopping malls all over the World, hosting millions of stores and welcoming millions of visitors each year.

Included Points of Interest in this dataset : -Shopping Malls and Centers -Outlets -Big Supermakets and Hypermarkets.

Information (if known) : shopping mall's name, physical address, number of shops, x,y coordinates, annual visitors counts (in millions), owner and managers, global area and GLA (in ranges), the website.

Global area and GLA Ranges : A = 0-2 500 m² B = 2 500-5 000 m² C = 5 000-10 000 m² D = 10 000-25 000 m²
E = 25 000-50 000 m² F = 50 000-75 000 m² G = 75 000-100 000 m² H = 100 000-1M m² I = 1M-10M m² J = 10M m² and +

Prices depend on the amount of Shopping Malls for each country. It goes from 59€ to 3990€ per country.

Demographic Analysis of Shopping Behavior: Insights and Recommendations

Dataset Information: The Shopping Mall Customer Segmentation Dataset comprises 15,079 unique entries, featuring Customer ID, age, gender, annual income, and spending score. This dataset assists in understanding customer behavior for strategic marketing planning.

Cleaned Data Details: Data cleaned and standardized, 15,079 unique entries with attributes including - Customer ID, age, gender, annual income, and spending score. Can be used by marketing analysts to produce a better strategy for mall specific marketing.

Challenges Faced: 1. Data Cleaning: Overcoming inconsistencies and missing values required meticulous attention. 2. Statistical Analysis: Interpreting demographic data accurately demanded collaborative effort. 3. Visualization: Crafting informative visuals to convey insights effectively posed design challenges.

Research Topics: 1. Consumer Behavior Analysis: Exploring psychological factors driving purchasing decisions. 2. Market Segmentation Strategies: Investigating effective targeting based on demographic characteristics.

Suggestions for Project Expansion: 1. Incorporate External Data: Integrate social media analytics or geographic data to enrich customer insights. 2. Advanced Analytics Techniques: Explore advanced statistical methods and machine learning algorithms for deeper analysis. 3. Real-Time Monitoring: Develop tools for agile decision-making through continuous customer behavior tracking. This summary outlines the demographic analysis of shopping behavior, highlighting key insights, dataset characteristics, team contributions, challenges, research topics, and suggestions for project expansion. Leveraging these insights can enhance marketing strategies and drive business growth in the retail sector.

References OpenAI. (2022). ChatGPT [Computer software]. Retrieved from https://openai.com/chatgpt. Mustafa, Z. (2022). Shopping Mall Customer Segmentation Data [Data set]. Kaggle. Retrieved from https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data Donkeys. (n.d.). Kaggle Python API [Jupyter Notebook]. Kaggle. Retrieved from https://www.kaggle.com/code/donkeys/kaggle-python-api/notebook Pandas-Datareader. (n.d.). Retrieved from https://pypi.org/project/pandas-datareader/

This dataset captures observations of consumer visits to three major shopping malls in Johannesburg, South Africa, from 2022 to 2023. The data, sourced from Fetch Analytics, utilizes ​​​​smartphone​​​​ signal tracking to provide insights into consumer behavior. Key variables include mall name, visit frequency, distance traveled, and demographic indicators such as income and Living Standard Measure (LSM). The dataset allows for a granular analysis of how spatial and socioeconomic factors influence shopping patterns in a fragmented retail landscape. This dataset is valuable for researchers investigating consumer behavior, spatial economics, and urban retail planning.

Comprehensive dataset containing 49,219 verified Shopping mall businesses in United States with complete contact information, ratings, reviews, and location data.

In July 2025, the visits to indoor malls in the United States compared to the previous year increased by *** percent. This growth was especially high in May, at *** percent. Visits to open-air shopping centers and outlet malls also peaked in April and May 2025, while the lowest number of visits were observed in February.

Dataset

This dataset was created by sewonghwang

Contents

Dataset Descriptions This analysis involves three main datasets—Sales Data, Customer Data, and Shopping Mall Data—which provide information on transactions, customer demographics, and shopping mall characteristics. Each dataset contributes unique aspects that, when combined, offer valuable insights into sales patterns, customer behavior, and the impact of mall features on sales.

Sales Data: This dataset records transaction-level details for products sold across shopping malls. Key columns include:

invoice_no: Unique identifier for each transaction. customer_id: Identifier for the customer making the purchase. category: Product category (e.g., Clothing, Shoes). quantity: Quantity of each product purchased. invoice date: Date of transaction. price: Price of each product purchased. shopping_mall: Mall where the transaction took place. Purpose: Analyzing this dataset allows us to understand product sales across different malls and track how sales change over time or by category.

Customer Data: This dataset provides demographic details for each customer, including:

customer_id: Unique identifier for each customer. gender: Customer’s gender. age: Customer’s age. payment_method: Preferred payment method for transactions. Purpose: This dataset supports customer segmentation by demographics, such as age and gender, and helps identify spending patterns and payment preferences.

Shopping Mall Data: This dataset contains details of various shopping malls in California where the transactions occur. The columns include:

shopping_mall: Name of the mall. construction_year: Year the mall was established. area_sqm: Total area of the mall in square meters. location: City in California where the mall is located. stores_count: Number of stores within the mall. Purpose: This dataset provides context on mall attributes and enables analysis of how mall features—such as size, store count, and location—might influence customer traffic, sales, and purchasing behaviors.

Goal of Analysis The goal of analyzing this data is to uncover patterns and insights that can inform decisions for optimizing sales strategies, enhancing customer engagement, and understanding the effects of mall characteristics on customer behavior. By exploring connections among sales performance, customer demographics, and mall attributes, this analysis seeks to answer questions like:

Which mall characteristics (e.g., size, age, store count) are most strongly associated with higher sales volumes? How do customer demographics, such as age and gender, impact spending patterns across malls? What product categories are more popular in specific malls, and how does this vary with mall characteristics?

Expected Outcomes With this analysis, we aim to develop actionable insights into the sales dynamics in California's shopping malls, identify customer preferences by mall characteristics, and understand how mall attributes drive retail success. These insights can be valuable for mall operators, retailers, and marketing teams looking to improve customer experience, tailor product offerings, and maximize sales performance across different mall locations.

According to a survey held among Southeast Asian consumers in February 2022, ** percent of the respondents visited a shopping mall in the last few days. Comparatively, another **** percent of the consumers did not visit shopping malls in over three months in 2022.

For the three displayed shopping center types, the median household income of their captured markets, i.e. the population who actually visits the malls, was higher in 2024 than it was in 2025.

The statistic shows the reasons why U.S. mall shoppers' shop at malls instead of online as of 2018. As of 2018, ** percent of respondents said that they were more likely to shop for apparel in a mall as opposed to online.

Comprehensive dataset containing 1,069 verified Shopping mall businesses in TG with complete contact information, ratings, reviews, and location data.

Xtract.io's comprehensive location data for European malls provides international retail strategists. Researchers, investors, and business developers can utilize this dataset to analyze retail landscape, identify market trends, and develop sophisticated strategies for European shopping center markets.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

-Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ? At LocationsXYZ, we: -Deliver POI data with 95% accuracy -Refresh POIs every 30, 60, or 90 days to ensure the most recent information -Create on-demand POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide POI and polygon data in multiple file formats

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

LocationsXYZ has empowered businesses with geo-spatial insights, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge POI data.

Context

Welcome to the shopping world of Istanbul! Our dataset contains shopping information from 10 different shopping malls between 2021 and 2023. We have gathered data from various age groups and genders to provide a comprehensive view of shopping habits in Istanbul. The dataset includes essential information such as invoice numbers, customer IDs, age, gender, payment methods, product categories, quantity, price, order dates, and shopping mall locations. We hope that this dataset will serve as a valuable resource for researchers, data analysts, and machine learning enthusiasts who want to gain insights into shopping trends and patterns in Istanbul. Explore the dataset and discover the fascinating world of Istanbul shopping!

Content

Attribute Information:

• invoice_no: Invoice number. Nominal. A combination of the letter 'I' and a 6-digit integer uniquely assigned to each operation.
• customer_id: Customer number. Nominal. A combination of the letter 'C' and a 6-digit integer uniquely assigned to each operation.
• gender: String variable of the customer's gender.
• age: Positive Integer variable of the customers age.
• category: String variable of the category of the purchased product.
• quantity: The quantities of each product (item) per transaction. Numeric.
• price: Unit price. Numeric. Product price per unit in Turkish Liras (TL).
• payment_method: String variable of the payment method (cash, credit card or debit card) used for the transaction.
• invoice_date: Invoice date. The day when a transaction was generated.
• shopping_mall: String variable of the name of the shopping mall where the transaction was made.

Number of Businesses statistics on the Shopping Mall Management industry in the US

This location dataset offers a detailed geographical representation of shopping centers across North America focusing on Canada. Retail strategists, real estate investors, and market researchers can leverage precise location information to analyze retail landscapes, identify market trends, and develop targeted strategies for shopping center markets.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

-Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ? At LocationsXYZ, we: -Deliver POI data with 95% accuracy -Refresh POIs every 30, 60, or 90 days to ensure the most recent information -Create on-demand POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide POI and polygon data in multiple file formats

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

LocationsXYZ has empowered businesses with geospatial insights, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge POI data.

As surveyed in 2022 in Vietnam, around ** percent of respondents visited the shopping malls at least once every three months to go shopping. In comparison, around **** percent of respondents went shopping at least once a week at the malls.

Comprehensive dataset containing 135 verified Shopping mall businesses in ME with complete contact information, ratings, reviews, and location data.

China Shopping Mall Development Index: City Type data was reported at 68.600 % in Sep 2018. This records an increase from the previous number of 66.300 % for Jun 2018. China Shopping Mall Development Index: City Type data is updated quarterly, averaging 66.100 % from Dec 2016 (Median) to Sep 2018, with 8 observations. The data reached an all-time high of 69.300 % in Mar 2017 and a record low of 62.700 % in Dec 2016. China Shopping Mall Development Index: City Type data remains active status in CEIC and is reported by Ministry of Commerce. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HSA: Shopping Mall Development Index.

China Shopping Mall Development Index data was reported at 67.100 % in Sep 2018. This records an increase from the previous number of 66.100 % for Jun 2018. China Shopping Mall Development Index data is updated quarterly, averaging 67.150 % from Dec 2016 (Median) to Sep 2018, with 8 observations. The data reached an all-time high of 68.300 % in Mar 2017 and a record low of 64.800 % in Jun 2017. China Shopping Mall Development Index data remains active status in CEIC and is reported by Ministry of Commerce. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HSA: Shopping Mall Development Index.