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.

The majority, almost ********** of UK shoppers, stated they visited shopping and town centers for hospitality related facilities, such as restaurants, bars or pubs in 2024. Only about ******* of respondents said they visited non-food retail establishments.

Germany has *** shopping malls. German shopping centers are an expected fixture of urban life and a popular retail destination for both locals and visitors alike. The share of malls located in rural areas and the outskirts of cities has remained very low in comparison to urban spaces. From * to *** malls Currently the malls occupy a national total of around ***** million square meters of retail space. Back in 1965, the number of German shopping centers was recorded at just 2 for the whole country. Since then figures have changed significantly. Within just 5 years, there were ** shopping centers open to consumers. Such a location offers not only actual shopping opportunities in numerous stores, but may include other establishments such as restaurants, cafes, venues for activities and games, rides. It all depends on the size of the shopping center. The fall of the mall? While the number of shopping centers in Germany has indeed exploded during the last four decades, it has also remained relatively stable as of late, reflecting the rise of online retail and e-commerce. Shoppers see advantages both in physical shopping and purchasing online, with combinations of the two still remaining common in consumers' national retail activities. In 2022, ** percent of respondents to a survey rated in-store shopping as the best overall experience, compared to ** percent who said the same about online shopping.

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/

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.

Comprehensive demographic dataset for Concord Mills Mall, Concord, NC, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

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.

About this dataset

Context

The dataset tabulates the Arcade town population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Arcade town. The dataset can be utilized to understand the population distribution of Arcade town by age. For example, using this dataset, we can identify the largest age group in Arcade town.

Key observations

The largest age group in Arcade Town, New York was for the group of age 20 to 24 years years with a population of 367 (8.74%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Arcade Town, New York was the 80 to 84 years years with a population of 112 (2.67%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Arcade town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Arcade town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Arcade town Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Arcade Town, New York population pyramid, which represents the Arcade town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Arcade Town, New York, is 20.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Arcade Town, New York, is 34.2.
• Total dependency ratio for Arcade Town, New York is 54.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Arcade Town, New York is 2.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Arcade town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Arcade town for the selected age group is shown in the following column.
• Population (Female): The female population in the Arcade town for the selected age group is shown in the following column.
• Total Population: The total population of the Arcade town for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Arcade town Population by Age. You can refer the same here

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.

This statistic shows the share of consumers who visited shopping malls in a ***** month period in the United States in 2016, by age. In 2016, ** percent of all U.S. adults reported that they had visited a shopping mall in the past three months.

Libraries Import:

Importing necessary libraries such as pandas, seaborn, matplotlib, scikit-learn's KMeans, and warnings. Data Loading and Exploration:

Reading a dataset named "Mall_Customers.csv" into a pandas DataFrame (df). Displaying the first few rows of the dataset using df.head(). Conducting univariate analysis by calculating descriptive statistics with df.describe(). Univariate Analysis:

Visualizing the distribution of the 'Annual Income (k$)' column using sns.distplot. Looping through selected columns ('Age', 'Annual Income (k$)', 'Spending Score (1-100)') and plotting individual distribution plots. Bivariate Analysis:

Creating a scatter plot for 'Annual Income (k$)' vs 'Spending Score (1-100)' using sns.scatterplot. Generating a pair plot for selected columns with gender differentiation using sns.pairplot. Gender-Based Analysis:

Grouping the data by 'Gender' and calculating the mean for selected columns. Computing the correlation matrix for the grouped data and visualizing it using a heatmap. Univariate Clustering:

Applying KMeans clustering with 3 clusters based on 'Annual Income (k$)' and adding the 'Income Cluster' column to the DataFrame. Plotting the elbow method to determine the optimal number of clusters. Bivariate Clustering:

Applying KMeans clustering with 5 clusters based on 'Annual Income (k$)' and 'Spending Score (1-100)' and adding the 'Spending and Income Cluster' column. Plotting the elbow method for bivariate clustering and visualizing the cluster centers on a scatter plot. Displaying a normalized cross-tabulation between 'Spending and Income Cluster' and 'Gender'. Multivariate Clustering:

Performing multivariate clustering by creating dummy variables, scaling selected columns, and applying KMeans clustering. Plotting the elbow method for multivariate clustering. Result Saving:

Saving the modified DataFrame with cluster information to a CSV file named "Result.csv". Saving the multivariate clustering plot as an image file ("Multivariate_figure.png").

Comprehensive demographic dataset for Mall of the Bluffs, Council Bluffs, IA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

This dataset contains shopping behavior information for 200 customers of a mall. Each customer record includes five key attributes:

• CustomerID: A unique identifier for each customer
• Gender: The customer's gender (Male/Female)
• Age: The customer's age
• Annual Income (k$): The customer's yearly income in thousands of dollars
• Spending Score (1-100): A score assigned by the mall based on customer behavior and spending nature, where 1 represents the lowest spending and 100 represents the highest spending

The dataset is commonly used for customer segmentation analysis, helping retail businesses understand their customer base and develop targeted marketing strategies based on demographics and spending patterns.

About this dataset

Context

The dataset tabulates the Arcade town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Arcade town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Arcade town was 4,203, a 0.50% decrease year-by-year from 2022. Previously, in 2022, Arcade town population was 4,224, an increase of 0.19% compared to a population of 4,216 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Arcade town increased by 35. In this period, the peak population was 4,224 in the year 2022. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Arcade town is shown in this column.
• Year on Year Change: This column displays the change in Arcade town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Arcade town Population by Year. You can refer the same here

Market Size statistics on the Shopping Centre Operators industry in Australia

Due to the coronavirus (COVID-19) crisis and social distancing measures the UK government took, retail footfall after March 2020 saw an unprecedented fall. Not unexpectedly, visitor numbers to shopping centers took the biggest hit, declining by almost ** percent in May 2020. Most recently reported numbers showed fluctuations. In September 2025, for example, shopping center footfall decreased by *** percent compared to the levels observed in the same month of the previous year.

Argentina Shopping Center Survey: Sales: GBA: 24 Districts data was reported at 4,407,421.928 ARS th in Aug 2019. This records a decrease from the previous number of 5,104,718.612 ARS th for Jul 2019. Argentina Shopping Center Survey: Sales: GBA: 24 Districts data is updated monthly, averaging 407,704.000 ARS th from Apr 2000 (Median) to Aug 2019, with 233 observations. The data reached an all-time high of 5,367,671.920 ARS th in Dec 2018 and a record low of 37,821.000 ARS th in Jan 2002. Argentina Shopping Center Survey: Sales: GBA: 24 Districts data remains active status in CEIC and is reported by National Institute of Statistics and Censuses. The data is categorized under Global Database’s Argentina – Table AR.H003: Shopping Centre Survey: Sales: Old Methodology.

Comprehensive demographic dataset for Greenwood Park Mall, Greenwood, IN, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

About this dataset

Context

The dataset tabulates the Arcade town population by year. The dataset can be utilized to understand the population trend of Arcade town.

Content

The dataset constitues the following datasets

• Arcade Town, New York Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.