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/

Customer Segmentation Analysis 📌 Project Overview This project aims to analyze sales and customer segmentation data using an interactive Power BI dashboard. The analysis provides insights into total sales, customer distribution, education levels, language preferences, and state-wise segmentation, helping businesses make data-driven decisions.

📊 Dashboard Features The project includes two key dashboards:

1️⃣ Sales Segmentation Analysis Total Sales: 162M | Total Customers: 40K Sales Distribution by Gender, Year, and Preferred Language Education Level vs. Sales Contribution State-wise Total Sales and Performance Trends

2️⃣ Customer Segmentation Analysis Total Customer Distribution across Different Metrics Customer Demographics: Gender, Education Level, and Language Preference State-wise Customer Count

🛠️ Tech Stack & Tools Used Power BI – Data visualization and interactive dashboard creation Excel / CSV Dataset – Raw data source DAX & Power Query – Data transformation and calculated measures

📈 Key Insights & Findings Top-performing languages: Customers who speak German (26.95%) and French (24.9%) contribute the most to sales. Education Level Impact: Associate Degree holders generate the highest sales and customer count. State-wise performance: Lakshadweep, Himachal Pradesh, and Bihar are the top-performing states.

We provide instructions, codes and datasets for replicating the article by Kim, Lee and McCulloch (2024), "A Topic-based Segmentation Model for Identifying Segment-Level Drivers of Star Ratings from Unstructured Text Reviews." This repository provides a user-friendly R package for any researchers or practitioners to apply A Topic-based Segmentation Model with Unstructured Texts (latent class regression with group variable selection) to their datasets. First, we provide a R code to replicate the illustrative simulation study: see file 1. Second, we provide the user-friendly R package with a very simple example code to help apply the model to real-world datasets: see file 2, Package_MixtureRegression_GroupVariableSelection.R and Dendrogram.R. Third, we provide a set of codes and instructions to replicate the empirical studies of customer-level segmentation and restaurant-level segmentation with Yelp reviews data: see files 3-a, 3-b, 4-a, 4-b. Note, due to the dataset terms of use by Yelp and the restriction of data size, we provide the link to download the same Yelp datasets (https://www.kaggle.com/datasets/yelp-dataset/yelp-dataset/versions/6). Fourth, we provided a set of codes and datasets to replicate the empirical study with professor ratings reviews data: see file 5. Please see more details in the description text and comments of each file. [A guide on how to use the code to reproduce each study in the paper] 1. Full codes for replicating Illustrative simulation study.txt -- [see Table 2 and Figure 2 in main text]: This is R source code to replicate the illustrative simulation study. Please run from the beginning to the end in R. In addition to estimated coefficients (posterior means of coefficients), indicators of variable selections, and segment memberships, you will get dendrograms of selected groups of variables in Figure 2. Computing time is approximately 20 to 30 minutes 3-a. Preprocessing raw Yelp Reviews for Customer-level Segmentation.txt: Code for preprocessing the downloaded unstructured Yelp review data and preparing DV and IVs matrix for customer-level segmentation study. 3-b. Instruction for replicating Customer-level Segmentation analysis.txt -- [see Table 10 in main text; Tables F-1, F-2, and F-3 and Figure F-1 in Web Appendix]: Code for replicating customer-level segmentation study with Yelp data. You will get estimated coefficients (posterior means of coefficients), indicators of variable selections, and segment memberships. Computing time is approximately 3 to 4 hours. 4-a. Preprocessing raw Yelp reviews_Restaruant Segmentation (1).txt: R code for preprocessing the downloaded unstructured Yelp data and preparing DV and IVs matrix for restaurant-level segmentation study. 4-b. Instructions for replicating restaurant-level segmentation analysis.txt -- [see Tables 5, 6 and 7 in main text; Tables E-4 and E-5 and Figure H-1 in Web Appendix]: Code for replicating restaurant-level segmentation study with Yelp. you will get estimated coefficients (posterior means of coefficients), indicators of variable selections, and segment memberships. Computing time is approximately 10 to 12 hours. [Guidelines for running Benchmark models in Table 6] Unsupervised Topic model: 'topicmodels' package in R -- after determining the number of topics(e.g., with 'ldatuning' R package), run 'LDA' function in the 'topicmodels'package. Then, compute topic probabilities per restaurant (with 'posterior' function in the package) which can be used as predictors. Then, conduct prediction with regression Hierarchical topic model (HDP): 'gensimr' R package -- 'model_hdp' function for identifying topics in the package (see https://radimrehurek.com/gensim/models/hdpmodel.html or https://gensimr.news-r.org/). Supervised topic model: 'lda' R package -- 'slda.em' function for training and 'slda.predict' for prediction. Aggregate regression: 'lm' default function in R. Latent class regression without variable selection: 'flexmix' function in 'flexmix' R package. Run flexmix with a certain number of segments (e.g., 3 segments in this study). Then, with estimated coefficients and memberships, conduct prediction of dependent variable per each segment. Latent class regression with variable selection: 'Unconstraind_Bayes_Mixture' function in Kim, Fong and DeSarbo(2012)'s package. Run the Kim et al's model (2012) with a certain number of segments (e.g., 3 segments in this study). Then, with estimated coefficients and memberships, we can do prediction of dependent variables per each segment. The same R package ('KimFongDeSarbo2012.zip') can be downloaded at: https://sites.google.com/scarletmail.rutgers.edu/r-code-packages/home 5. Instructions for replicating Professor ratings review study.txt -- [see Tables G-1, G-2, G-4 and G-5, and Figures G-1 and H-2 in Web Appendix]: Code to replicate the Professor ratings reviews study. Computing time is approximately 10 hours. [A list of the versions of R, packages, and computer...

The Artificial Intelligence (AI) in Big Data Analysis market is experiencing significant growth and transformation, shaped by the overwhelming volume of data generated globally and the necessity for real-time insights across various industries. As organizations increasingly rely on data-driven decision-making, AI te

Manipulating cluttered cables, hoses or ropes is challenging for both robots and humans. Humans often simplify these perceptually challenging tasks by pulling or pushing tangled cables and observing the resulting motions. We would like to build a similar system -- in accordance with the interactive perception paradigm -- to aid robotic cable manipulation. A cable motion segmentation method that densely labels moving cable image pixels is a key building block of such a system. We present MovingCables, a moving cable dataset, which we hope will motivate the development and evaluation of cable motion (or semantic) segmentation algorithms. The dataset consists of real-world image sequences automatically annotated with ground truth segmentation masks and optical flow.

The Community Engagement Software market has emerged as a critical tool for organizations looking to foster meaningful interactions and build lasting relationships with their audiences. With the digital landscape continually evolving, businesses, non-profits, and governmental agencies increasingly rely on community

Overview The Mall Customers Dataset provides data on 200 individuals who visit a mall, including demographic information, annual income, and spending habits. This dataset is useful for exploratory data analysis, customer segmentation, and clustering tasks (e.g., K-means clustering).

Dataset Summary - Rows: 200 - Columns: 5 - No missing values

Columns Description - CustomerID: A unique identifier for each customer (integer). - Genre: The gender of the customer (Male/Female). - Age: The age of the customer (integer). - Annual Income (k$): Annual income of the customer in thousands of dollars (integer). - Spending Score (1-100): A score assigned by the mall based on customer behavior and spending patterns (integer).

Potential Use Cases - Customer Segmentation: Group customers based on their income and spending habits. - Behavioral Analysis: Explore how factors like gender, age, and income influence spending scores. - Clustering: Apply algorithms such as K-means to identify clusters of customers with similar characteristics. - Targeted Marketing Campaigns: Use the insights to create personalized promotions for different customer segments.

Exploratory Questions - What is the relationship between annual income and spending score? - Does gender or age influence spending behavior? - Which customers have high spending scores but low incomes, or vice versa?

Suggested Analysis Techniques - EDA: Visualize income distribution, age groups, and spending patterns. - Clustering Algorithms: Use K-means or hierarchical clustering for segmentation. - Correlation Analysis: Investigate correlations between age, income, and spending score.

Licensing & Citation - License: Open for public use, suitable for educational and research purposes. - Citation: If you use this dataset in your project or research, please reference this dataset appropriately.

This dataset provides a great starting point for hands-on learning in customer analytics, marketing strategy, and machine learning. Perfect for beginners and data enthusiasts looking to explore clustering or segmentation techniques!

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The apple market segmentation analysis identifies Distribution Channel (offline and online) and Geographic Landscape (APAC, Europe, MEA, North America, and South America). The subsegments explored in the apple market research are as follows:

Apple Market Segmentation Analysis by Distribution ChannelofflineonlineApple Market Segmentation Analysis by Geographic LandscapeAPACEuropeMEANorth AmericaSouth America

The Note Taking App market has seen a significant evolution over the years, driven by the growing need for effective information management in both personal and professional settings. With individuals and teams increasingly relying on digital solutions to organize thoughts, tasks, and projects, these apps have emerg

Pandemics such as Covid-19 pose tremendous public health communication challenges in promoting protective behaviours, vaccination, and educating the public about risks. Segmenting audiences based on attitudes and behaviours is a means to increase the precision and potential effectiveness of such communication. The present study reports on such an audience segmentation effort for the population of England, sponsored by the United Kingdom Health Security Agency (UKHSA) and involving a collaboration of market research and academic experts. A cross-sectional online survey was conducted between 4 and 24 January 2022 with 5525 respondents (5178 used in our analyses) in England using market research opt-in panel. An additional 105 telephone interviews were conducted to sample persons without online or smartphone access. Respondents were quota sampled to be demographically representative. The primary analytic technique was k means cluster analysis, supplemented with other techniques including multi-dimensional scaling and use of respondent ‐ as well as sample-standardized data when necessary to address differences in response set for some groups of respondents. Identified segments were profiled against demographic, behavioural self-report, attitudinal, and communication channel variables, with differences by segment tested for statistical significance. Seven segments were identified, including distinctly different groups of persons who tended toward a high level of compliance and several that were relatively low in compliance. The segments were characterized by distinctive patterns of demographics, attitudes, behaviours, trust in information sources, and communication channels preferred. Segments were further validated by comparing the segmentation variable versus a set of demographic variables as predictors of reported protective behaviours in the past two weeks and of vaccine refusal; the demographics together had about one-quarter the effect size of the single seven-level segment variable. With respect to managerial implications, different communication strategies for each segment are suggested for each segment, illustrating advantages of rich segmentation descriptions for understanding public health communication audiences. Strengths and weaknesses of the methods used are discussed, to help guide future efforts.

Retail Customer Segmentation

Uncovering Interesting Patterns in Purchase Behavior

By Abhishek Sharma [source]

About this dataset

This dataset contains customer purchase data from a retail store, offering insight into customers' shopping habits. Compiling transactions across multiple invoices, this dataset offers an opportunity to analyze and measure customer behavior in order to understand buying patterns and devise strategies to drive increased sales. From individual items purchased to total spend by country of origin, this comprehensive dataset allows for detailed segmentation analysis of how customers shop, where they spend their money and which products are most popular. With this information businesses can tailor their services more precisely and adjust their prices accordingly for maximum benefit. Dive in now and uncover valuable insights about your customers!

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How to use the dataset

This dataset is a great resource for customer segmentation analysis. Customer segmentation is the process of dividing customers into different subgroups based on characteristics such as age, gender, income, and purchasing behavior. By understanding these characteristics and customer behaviors, businesses can make more informed decisions on how to best target their marketing efforts to reach the right people with the right message.

Research Ideas

• Estimating customer lifetime value by taking into account the frequency of purchases, unit price and country of origin.
• Analyzing customer purchase patterns to identify which items are popular with different customers segments and tailor product/marketing strategies accordingly.
• Using machine learning algorithms to cluster customers based on their purchasing behavior and preferences to effectively target marketing efforts

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: customer_segmentation.csv | Column name | Description | |:----------------|:---------------------------------------------| | InvoiceNo | Unique identifier for each invoice. (String) | | StockCode | Unique identifier for each product. (String) | | Description | Description of the product. (String) | | Quantity | Number of items purchased. (Integer) | | InvoiceDate | Date of the invoice. (Date) | | UnitPrice | Price of each item. (Float) | | Country | Country of origin of the purchase. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Abhishek Sharma.

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series.

We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

The Public Safety market is a vital sector that encompasses various technologies and services aimed at ensuring the security and well-being of communities and individuals. With the rise in urbanization, population growth, and the complexity of safety threats, the demand for robust public safety solutions has surged.

Trust in information sources re Covid-19 guidance by segment.

This dataset provides comprehensive customer data suitable for segmentation analysis. It includes anonymized demographic, transactional, and behavioral attributes, allowing for detailed exploration of customer segments. Leveraging this dataset, marketers, data scientists, and business analysts can uncover valuable insights to optimize targeted marketing strategies and enhance customer engagement. Whether you're looking to understand customer behavior or improve campaign effectiveness, this dataset offers a rich resource for actionable insights and informed decision-making.

Key Features:

Anonymized demographic, transactional, and behavioral data. Suitable for customer segmentation analysis. Opportunities to optimize targeted marketing strategies. Valuable insights for improving campaign effectiveness. Ideal for marketers, data scientists, and business analysts.

Usage Examples:

Segmenting customers based on demographic attributes. Analyzing purchase behavior to identify high-value customer segments. Optimizing marketing campaigns for targeted engagement. Understanding customer preferences and tailoring product offerings accordingly. Evaluating the effectiveness of marketing strategies and iterating for improvement. Explore this dataset to unlock actionable insights and drive success in your marketing initiatives!

The 6G Communication market is poised to revolutionize the way we connect and interact in an increasingly digital world. As industries across the globe rely on faster, more reliable communication technologies, the transition from 5G to 6G is gaining momentum. While 5G laid the foundation for ultra-low latency and hi

Goals :

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description).

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

5. Sales Performance Dashboard:

To track the sales performance of the online retail company, a sales performance dashboard can be created. This dashboard can include key metrics such as total sales, sales by product category, sales by customer segment, and sales by geographical location. By visualizing the sales data in an interactive dashboard, it becomes easier to identify trends, patterns, and areas for improvement.

Research Ideas ****:

1. Inventory Management: By analyzing the quantity and frequency of product sales, retailers can effectively manage their stock and predict future demand. This would help ensure that popular items are always available while less popular items aren't overstocked.
2. Customer Segmentation: Data from different countries can be used to understand buying habits across different geographical locations. This will allow the retail company to tailor its marketing strategy for each specific region or country, leading to more effective advertising campaigns.
3. Sales Trend Analysis: With data spanning almost a year, temporal patterns in purchasing behavior can be identified, including seasonality and other trends (like an increase in sales during holidays). Techniques like time-series analysis could provide insights into peak shopping times or days of the week when sales are typically high.
4. Predictive Analysis for Cross-Selling & Upselling: Based on a customer's previous purchase history, predictive algorithms can be utilized to suggest related products that might interest the customer, enhancing upsell and cross-sell opportunities.
5. Detecting Fraud: Analysing sale returns (marked with 'c' in InvoiceNo) across customers or regions could help pinpoint fraudulent activities or operational issues leading to those returns
6. RFM Analysis: By using the RFM (Recency, Frequency, Monetary) segmentation technique, the online retail company can gain insights into customer behavior and tailor their marketing strategies accordingly.

**************Steps :**************

1. Data manipulation and cleaning from raw data using SQL language Google Big Query
2. Data filtering, grouping, and slicing
3. Data Visualization using Tableau
4. Data visualization analysis and result

The Consumer Grade Genetic Sequencing market has been experiencing significant growth in recent years, driven by advancements in biotechnology and increased consumer interest in personal health and wellness. This innovative sector focuses on providing individuals with accessible genetic testing, allowing them to gai

Chest ST Segmentation of a pathology and anatomical regions

Dataset comprises 1,000+ studies, featuring 7 pathologies and covering 8 anatomical regions. It includes a variety of CT scans that facilitate research in lung segmentation and disease detection. Researchers can leverage this dataset for clinical practice, studying imaging data for better early detection methods and computer-aided screening.

The data is provided in nii format and includes both volumetric data and the corresponding masks for each study, facilitating comprehensive analysis and segmentation tasks. - Get the data

Example of data

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Researchers can leverage this dataset to explore automated segmentation techniques, utilizing deep learning and machine learning models for improved image analysis.The dataset is ideal for medical research, disease detection, and classification tasks, particularly for developing computer-aided diagnosis and machine learning models

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About data

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F2954f658cf5d63becab24b81739d686d%2FFrame%20169%20(1).png?generation=1732241020560152&alt=media" alt=""> By utilizing this dataset, researchers can contribute to the development of more accurate and efficient diagnosis systems, ultimately improving patient outcomes in clinical practice.

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The Spring-Loaded Test Probe market is a vital segment within the electronics and manufacturing industries, playing a crucial role in the efficient testing of electronic components and circuit boards. These probes are designed to make reliable contact with electronic circuits, facilitating precise measurements and e