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!

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/

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...

Customer Segmentation Market Outlook 2032

The global customer segmentation market size was USD XX Billion in 2023 and is likely to reach USD XX Billion by 2032, expanding at a CAGR of XX % during 2024–2032. The market growth is attributed to the increasing demand for personalized marketing strategies and targeted customer engagement.



Increasing globalization and digitalization is projected to drive the market in the assessment year. Businesses are leveraging segmentation techniques to better understand their diverse customer base and tailor marketing strategies accordingly. One growing trend is the use of advanced analytics and machine learning algorithms to segment customers based on intricate criteria such as behavior, preferences, and purchase history.

Growing competition in the marketplace is a significant factor fueling the market. Businesses are increasingly relying on segmentation to gain a competitive edge by identifying and targeting niche market segments. Moreover, with the rise of e-commerce and digital marketing channels, there is a rising demand for segmentation solutions that optimizes marketing strategies.

Impact of Artificial Intelligence (AI) in Customer Segmentation Market

The use of artificial intelligence is revolutionizing the customer segmentation market by enabling businesses to unlock deeper insights and enhance targeting precision. AI-powered algorithms analyze vast amounts of customer data with unprecedented speed and accuracy, allowing businesses to identify subtle patterns and correlations that traditional segmentation methods may overlook. By leveraging AI, companies segment their customer base effectively based on diverse criteria such as behavior, preferences, and purchase history.<

Dataset

This dataset was created by Priscilla Rajadurai

Contents

Success demands customer-centric models – a shift towards this approach is essential in order to create products that consumers want to buy and brands that consumers want to associate themselves with. Attitudinal traits are rarely mutually exclusive – products or services that cater to a trend crossover offer consumers multiple benefits and will be attractive to a wider range of consumers. Trust is key. A sense of honesty from an insurance provider is not only the most widely sought factor among consumers – regardless of customer group – but also drives among the strongest sources of sentiment. Read More

Analysis of ‘Automobile Customer’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/akashdeepkuila/automobile-customer on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending habits.

Companies employing customer segmentation operate under the fact that every customer is different and that their marketing efforts would be better served if they target specific, smaller groups with messages that those consumers would find relevant and lead them to buy something. Companies also hope to gain a deeper understanding of their customers' preferences and needs with the idea of discovering what each segment finds most valuable to more accurately tailor marketing materials toward that segment.

Content

An automobile company has plans to enter new markets with their existing products (P1, P2, P3, P4 and P5). After intensive market research, they’ve deduced that the behavior of new market is similar to their existing market.

In their existing market, the sales team has classified all customers into 4 segments (A, B, C, D ). Then, they performed segmented outreach and communication for different segment of customers. This strategy has work exceptionally well for them. They plan to use the same strategy on new markets and have identified 2627 new potential customers.

The dataset provides the details of the existing and potential customers of the company based on the purchase history and the corresponding segments they have been classified into.

Variable description

• CustomerID : unique customer ID
• Gender : gender of the customer
• Married : marital status of the customer
• Age : age of the customer
• Graduated : specifies whether the customer a graduate?
• Profession : profession of the customer
• WorkExperience : work experience of the customer in years
• SpendingScore : spending score of the customer
• FamilySize : number of family members of the customer (including the customer)
• Category : anonymised category for the customer
• Segmentation : (target variable) customer segment of the customer

Inspiration

The dataset is ideal for anyone looking to practice customer segmentation.

--- Original source retains full ownership of the source dataset ---

This is a dataset containing information of customers such as buying behavior, id, purchased items etc. You can use this dataset for customer segmentation, analytics etc.

This dataset provides a comprehensive view of customer purchase frequency patterns, including total purchases, recency, spending, and lapsed status. It is designed to support marketing optimization, retention analysis, and win-back campaign targeting by offering actionable insights into customer engagement and churn risk.

Download API

kaggle datasets download -d kunalgupta2616/hackerearth-customer-segmentation-hackathon

Marketing campaigns are characterized by focusing on customer needs and their overall satisfaction. Nevertheless, there are different variables that determine whether a marketing campaign will be successful or not. Some important aspects of a marketing campaign are as follows:

Segment of the Population: To which segment of the population is the marketing campaign going to address and why? This aspect of the marketing campaign is extremely important since it will tell which part of the population should most likely receive the message of the marketing campaign.

Distribution channel to reach the customer's place: Implementing the most effective strategy in order to get the most out of this marketing campaign. What segment of the population should we address? Which instrument should we use to get our message out? (Ex: Telephones, Radio, TV, Social Media Etc.)

Promotional Strategy: This is the way the strategy is going to be implemented and how are potential clients going to be addressed. This should be the last part of the marketing campaign analysis since there has to be an in-depth analysis of previous campaigns (If possible) in order to learn from previous mistakes and to determine how to make the marketing campaign much more effective.

You are leading the marketing analytics team for a banking institution. There has been a revenue decline for the bank and they would like to know what actions to take. After investigation, it was found that the root cause is that their clients are not depositing as frequently as before. Term deposits allow banks to hold onto a deposit for a specific amount of time, so banks can lend more and thus make more profits. In addition, banks also hold a better chance to persuade term deposit clients into buying other products such as funds or insurance to further increase their revenues.

You are provided a dataset containing details of marketing campaigns done via phone with various details for customers such as demographics, last campaign details etc. Can you help the bank to predict accurately whether the customer will subscribe to the focus product for the campaign - Term Deposit after the campaign?

Data Description

Train Set

Train set contains the data to be used for model building. It has the true labels for whether the customer subscribed for term deposit (1) or not (0)

Test Set

Set of calls for which the prediction needs to be done regarding the subscription status of the customer for term deposit post campaign.

Sample Submission:

Format for making the submission for predictions on the test set

id: Unique id for each call

term_deposit_subscribed: whether term deposit was subscribed post call. (1/0)

Evaluation Metric

The evaluation metric for this hackathon is binary F1 Score.

This dataset provides detailed logs of checking account overdraft fee events, capturing transaction details, account and customer identifiers, fee amounts, and customer segmentation. It enables financial institutions to identify high-risk customer segments, analyze patterns of overdraft occurrences, and develop targeted strategies to reduce fee events and improve customer financial health.

ABSTRACT: In recent years, increasing interest in natural and traditional plants, which are an integral part of rural life, has been observed because of health concerns and new social trends. In this regard, medicinal and aromatic plants (MAPs) are becoming more popular among consumers. The purpose of this research is to investigate consumers’ attitudes and behaviors toward MAPs in order to identify possible distinct consumer group and examine its potential linkage to the characteristics of the consumers’ demographic and socio-economic status. To detect the perceived differences among consumers, the principal component and k-means cluster analysis were performed using the data from a face-to-face survey (n=420) conducted in five major cities in the Mediterranean region of Turkey. The analysis allows segmenting the market into three homogenous clusters that have distinctive behavioral, attitudinal, and socio-demographic profiles. This segmentation is particularly effective for the dynamics and further expansion of the MAP sector as an important source for rural life.

Dataset

This dataset was created by Sumeet Sawant

Contents

It contains the following files:

The Report Covers US Furniture Industry Analysis and it is Segmented by Material (Wood, Metal, Plastic and Other Materials), by Application (Home Furniture, Office Furniture, Hospitality Furniture, and Other Furniture) and by Distribution Channel (Supermarkets, Specialty Stores, Online, and Other Distribution Channels).

The Customer Service Business Process Outsourcing (BPO) market has evolved into a pivotal sector within the global economy, redefining how companies manage customer interactions and enhance user experiences. By delegating customer service operations to specialized providers, businesses can focus on their core compet

The Customer Success Management (CSM) Platform market is experiencing robust growth, driven by the increasing need for businesses to improve customer retention, enhance customer lifetime value, and foster long-term relationships. The shift towards subscription-based business models and the rising adoption of cloud-based solutions are key catalysts. Companies are recognizing the strategic importance of proactively managing customer success, leading to significant investment in CSM platforms. This market is segmented by deployment (cloud-based and on-premises) and application (reporting and analytics, customer segmentation, risk and compliance management, customer service, and others). Cloud-based solutions dominate due to their scalability, accessibility, and cost-effectiveness. The reporting and analytics segment holds a significant market share, as businesses prioritize data-driven insights to understand customer behavior and identify potential churn risks. North America currently holds the largest market share, followed by Europe and Asia-Pacific, reflecting the higher adoption rates in mature economies. However, the Asia-Pacific region is projected to witness the fastest growth rate due to increasing digitalization and a growing number of software-as-a-service (SaaS) companies. Competitive intensity is high, with established players like Salesforce and Gainsight alongside emerging companies constantly innovating to provide advanced features and integrations. The market's future growth trajectory is influenced by factors like increasing adoption of AI and machine learning in CSM, the growing importance of personalized customer experiences, and the need for greater integration with other business applications. The competitive landscape continues to evolve with mergers and acquisitions, strategic partnerships, and product enhancements shaping the market dynamics. The market faces challenges like the high initial investment costs associated with implementing CSM platforms and the need for skilled personnel to effectively manage these systems. Despite these challenges, the long-term outlook for the CSM platform market remains positive, driven by the growing recognition of its importance in achieving sustainable business growth and improving overall customer satisfaction. Companies are increasingly adopting proactive strategies to enhance customer engagement, reduce churn rates, and drive revenue growth through effective customer success management. The market is expected to continue expanding at a healthy CAGR, fueled by technological advancements, evolving business models, and the ongoing need for businesses to differentiate themselves in competitive markets.

Key Features: • Sourced from first-party, verified, consent-based data • Contains hashed email identifiers (SHA-256) • Privacy-respecting and activation-ready • APAC and EMEA coverage for global or regional reach

Use Cases: • Identify and reach niche consumer segments • Build scalable, high-performance lookalike audiences • Increase campaign efficiency with precise targeting • Power audience enrichment and data modeling strategies

Data Format: Hashed Emails (SHA-256)

Data Delivery: SFTP

Perfect For: • Media Agencies • Ad Tech Platforms • Retail & E-Commerce Brands • Data Enrichment Providers • Customer Intelligence Teams

This statistic shows the market segmentation of the bulk logistics sector in Europe in 2016. That year, electricity/coal shipping made up the highest share, with ** percent. Ranked second was construction with ** percent of the market share.