Customer Personality Analysis involves a thorough examination of a company's optimal customer profiles. This analysis facilitates a deeper understanding of customers, enabling businesses to tailor products to meet the distinct needs, behaviors, and concerns of various customer types.

By conducting a Customer Personality Analysis, businesses can refine their products based on the preferences of specific customer segments. Rather than allocating resources to market a new product to the entire customer database, companies can identify the segments most likely to be interested in the product. Subsequently, targeted marketing efforts can be directed toward those particular segments, optimizing resource utilization and increasing the likelihood of successful product adoption.

Details of Features are as below:

• Id: Unique identifier for each individual in the dataset.
• Year_Birth: The birth year of the individual.
• Education: The highest level of education attained by the individual.
• Marital_Status: The marital status of the individual.
• Income: The annual income of the individual.
• Kidhome: The number of young children in the household.
• Teenhome: The number of teenagers in the household.
• Dt_Customer: The date when the customer was first enrolled or became a part of the company's database.
• Recency: The number of days since the last purchase or interaction.
• MntWines: The amount spent on wines.
• MntFruits: The amount spent on fruits.
• MntMeatProducts: The amount spent on meat products.
• MntFishProducts: The amount spent on fish products.
• MntSweetProducts: The amount spent on sweet products.
• MntGoldProds: The amount spent on gold products.
• NumDealsPurchases: The number of purchases made with a discount or as part of a deal.
• NumWebPurchases: The number of purchases made through the company's website.
• NumCatalogPurchases: The number of purchases made through catalogs.
• NumStorePurchases: The number of purchases made in physical stores.
• NumWebVisitsMonth: The number of visits to the company's website in a month.
• AcceptedCmp3: Binary indicator (1 or 0) whether the individual accepted the third marketing campaign.
• AcceptedCmp4: Binary indicator (1 or 0) whether the individual accepted the fourth marketing campaign.
• AcceptedCmp5: Binary indicator (1 or 0) whether the individual accepted the fifth marketing campaign.
• AcceptedCmp1: Binary indicator (1 or 0) whether the individual accepted the first marketing campaign.
• AcceptedCmp2: Binary indicator (1 or 0) whether the individual accepted the second marketing campaign.
• Complain: Binary indicator (1 or 0) whether the individual has made a complaint.
• Z_CostContact: A constant cost associated with contacting a customer.
• Z_Revenue: A constant revenue associated with a successful campaign response.
• Response: Binary indicator (1 or 0) whether the individual responded to the marketing campaign.

Analysis of ‘Market Segmentation in Insurance Unsupervised’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jillanisofttech/market-segmentation-in-insurance-unsupervised on 28 January 2022.

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

WHAT IS MARKET SEGMENTATION?

In marketing, market segmentation is the process of dividing a broad consumer or business market, normally consisting of existing and potential customers, into subgroups of consumers based on some type of shared characteristics.

Objective :

This case requires developing a customer segmentation to give recommendations like saving plans, loans, wealth management, etc. on target customer groups.

Dataset

The sample Dataset summarizes the usage behavior of about 9000 active credit cardholders during the last 6 months. The file is at a customer level with 18 behavioral variables. Variables of Dataset Balance Balance Frequency Purchases One-off Purchases Installment Purchases Cash Advance Purchases Frequency One-off Purchases Frequency Purchases Installments Frequency Cash Advance Frequency Cash Advance TRX Purchases TRX Credit Limit Payments Minimum Payments PRC Full payment Tenure Cluster

The sample Dataset summarizes the usage behavior of about 9000 active credit cardholders during the last 6 months. The file is at a customer level with 18 behavioral variables.

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

1) Data Introduction • The Easy Analysis Of Company's Ideal Customers Dataset is a structured dataset designed to identify ideal customer segments and support the development of effective marketing strategies based on customer demographics, purchasing patterns, and campaign responses. It includes a wide range of features such as age, income, family composition, product spending, and discount usage, with a focus on the response variable indicating whether the customer responded to the last marketing campaign.

2) Data Utilization (1) Characteristics of the Easy Analysis Of Company's Ideal Customers Dataset: • The dataset includes diverse features useful for customer segmentation, such as education level, marital status, annual income, number of children, and marketing campaign participation history. The response field serves as a binary classification label indicating whether the customer responded to the final campaign.

(2) Applications of the Easy Analysis Of Company's Ideal Customers Dataset: • Marketing campaign response prediction: This dataset can be used to train machine learning classification models to predict the likelihood of a customer responding to a marketing campaign. • Customer segmentation and strategic planning: By identifying customer segments with high response potential, the dataset can support targeted marketing, personalized promotion design, and customer retention strategies.

This dataset contains supplementary data related to the manuscript entitled "A combined multicriteria and fuzzy sets approach to customer relationship management". It contains two files. The first presents data and evaluation of 21 potential customers (of a digital marketing company) using the multicriteria decision making (MCDM) method PROMETHEE II; it contains the implementation of VBA code of this method. The second presents data and evaluation of 8 customers (of the same digital marketing company) using the multicriteria decision making (MCDM) method fuzzy TOPSIS; it also contains the implementation of VBA code of the method. To better explain used data and evaluations: the potential customers are evaluated considering criteria "audience" (which means number of followers of the potential customers in social media), "used platform" (expresses which platform the customer uses to disseminate content) and "niche relevance" (importance of the niche in which customers produce content). Finally, applying the PROMETHEE II method results in a final score for each potential customer, allowing to rank them according to these scores. Current customers are evaluated using sets of criteria grouped in three performance dimension: growth potential, relationship and cost-effectiveness. Applying fuzzy TOPSIS method results in a score for each dimension. Thus, customers are classified using two portfolio matrices. Steps to reproduce the applications can be found in the respective worksheets.

The Nvidia Market Customer Segmentation dataset provides a comprehensive analysis of sales and market dynamics for Nvidia's product offerings across various global regions from 1993 to 2024. This synthetic dataset includes over 39,000 entries, capturing key variables such as product categories (Gaming, AI, Data Center, OEM), specific product names (e.g., RTX 3080, Tesla V100), customer segments (Gamers, AI Researchers, Cloud Providers, Educational Institutions), and regions (North America, Europe, APAC, and more). It details customer purchasing behavior, including revenue data, units sold, marketing expenditures, customer satisfaction scores, and customer retention rates.

In marketing and selling products or services, it is essential to put in mind that different customers have different preferences, needs, and behaviors, and it's crucial to understand these differences to effectively reach and engage with them. One powerful way to do this is by segmenting customers by age. By doing so, you can tailor your marketing strategies to better resonate with each group and ultimately drive more sales and customer loyalty. This dataset is intended for analysis to identify the effects of different Age Group on revenue and profit

Acknowledgements

https://skillsforall.com/

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This dataset provides detailed bibliographic metadata records for scholarly publications related to 'Customer Segmentation' (including Market Segmentation), as retrieved from Crossref.org. This metadata corpus facilitates in-depth exploration of the academic discourse surrounding this fundamental marketing strategy. Contextual Overview of Customer Segmentation: 1. Definition and Context: Customer Segmentation is the process of dividing a broad consumer or business market, normally consisting of existing and potential customers, into sub-groups of consumers (known as segments) based on shared characteristics. Its purpose is to enable more targeted and effective marketing strategies. A cornerstone of marketing theory and practice for decades, its application has become increasingly sophisticated with the advent of data analytics and digital marketing channels. 2. Strengths and Weaknesses: Strengths include improved marketing ROI through targeted messaging, enhanced customer understanding and satisfaction, better product development, and increased competitiveness. Weaknesses can involve the cost and complexity of data collection and analysis, difficulty in identifying meaningful and actionable segments, risk of over-segmentation or stereotyping, and challenges in implementing differentiated strategies across segments. The stability and relevance of segments can also change over time. 3. Relevance and Research Potential: Customer Segmentation remains highly relevant for personalization, targeted advertising, and value proposition design in both B2C and B2B markets. It is a foundational concept in marketing strategy and consumer behavior research. Current research opportunities include AI-driven and dynamic segmentation, behavioral segmentation based on digital footprints, ethical considerations in data-driven segmentation (e.g., fairness, privacy), and the integration of segmentation with customer journey mapping and experience design across omnichannel environments. Dataset Structure and Content: The dataset consists of one or more archives. Each archive contains a series of approximately 850 monthly folders (e.g., spanning from January 1950 to January 2025), reflecting a granular month-by-month process of metadata retrieval and curation for Customer Segmentation. Within each monthly folder, users will find several JSON files documenting the search and filtering process for that specific month: term_results/: A subfolder containing JSON files for results of initial broad keyword searches related to Customer Segmentation. merged_results.json: Aggregated results from these individual term searches before advanced filtering. filtered_results.json: Results after applying a more specific, complex Boolean query (e.g., ("customer segmentation" OR "market segmentation") AND ("marketing" OR ...)) and exact phrase matching to refine relevance. The exact query used is detailed within this file. final_results.json: This is the primary file of interest for most users. It contains the curated, deduplicated (by DOI) list of unique publication metadata records deemed most relevant to 'Customer Segmentation' for that specific month. Includes fields like Title, Authors, DOI, Publication Date, Source Title, Abstract (if available from Crossref). statistics_results.json: Summary statistics of the search and filtering process for the month. This granular monthly structure allows researchers to trace the evolution of academic discourse on Customer Segmentation and identify relevant publications with high temporal precision. For an overview of the general retrieval methodology, refer to the parent Dataverse description (Management Tool Bibliographic Metadata (Crossref)). Users interested in aggregated publication counts or trend analysis for Customer Segmentation should consult the corresponding datasets in the Raw Extracts Dataverse and the Comparative Indices Dataverse.

Analysis of ‘Customer Segmentation’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/abisheksudarshan/customer-segmentation 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.

You are required to help the manager to predict the right group of the new customers.

Acknowledgements

Credits to AV

Inspiration

Beginner dataset for multiclass classification

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

Customer Segmentation with K-Means

Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data. Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. A business task is to retain those customers. Another group might include customers from non-profit organizations. And so on.

Dataset donwloaded from - IBM Object Storage

dataset download link : https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv

Leverage our Medium dataset for diverse applications to improve business strategies and market insights. Analyzing this dataset can facilitate an understanding of reader preferences and trends within the content creation industry, empowering organizations to refine article offerings and marketing strategies. Access the entire dataset or customize a subset to align with your specific needs.

Popular use cases include optimizing article selections based on reader preferences, conducting detailed market analysis and segmentation, and identifying and predicting emerging trends in content creation and reader behavior.

The Big Data Marketing market is experiencing robust growth, driven by the increasing availability of consumer data, the proliferation of digital channels, and the rising need for personalized marketing strategies. The market's expansion is fueled by advancements in data analytics technologies, enabling businesses to derive actionable insights from vast datasets. This allows for more effective targeting, improved customer segmentation, and ultimately, enhanced return on investment (ROI) for marketing campaigns. While the provided data lacks specific figures for market size and CAGR, a reasonable estimate, considering the current industry trends, would be a 2025 market size of approximately $150 billion USD, growing at a CAGR of 15-20% through 2033. This growth is expected across all segments, including SaaS, PaaS, and consulting services, with strong demand from various sectors such as consumer electronics, finance, and retail. The market segmentation highlights the diverse applications of big data marketing across various industries. The SaaS segment is likely to dominate due to its scalability and accessibility, while the PaaS segment is poised for substantial growth as businesses increasingly seek to build customized data analytics solutions. The consulting segment plays a crucial role in guiding companies through the implementation and optimization of big data marketing strategies. Geographical expansion will be a key factor, with North America and Europe expected to maintain significant market share, but with rapid growth anticipated in Asia-Pacific regions, driven by increasing digital adoption and economic expansion. However, challenges remain, including data privacy concerns, the need for skilled data scientists, and the complexities of integrating various data sources. Overcoming these hurdles will be crucial to realizing the full potential of the big data marketing market.

Hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

Analysis of ‘Customer Clustering’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/dev0914sharma/customer-clustering on 28 January 2022.

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

Customer Segmentation is the subdivision of a market into discrete customer groups that share similar characteristics. Customer Segmentation can be a powerful means to identify unsatisfied customer needs. Using the above data companies can then outperform the competition by developing uniquely appealing products and services. You are owing a supermarket mall and through membership cards, you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. You want to understand the customers like who are the target customers so that the sense can be given to marketing team and plan the strategy accordingly.

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

The global Marketing Analytics Services market, currently valued at approximately $10.32 billion (2025), is poised for significant growth. While the precise CAGR is unavailable, considering the rapid adoption of data-driven marketing strategies and the increasing complexity of digital marketing landscapes, a conservative estimate would place the annual growth rate between 10-15% over the forecast period (2025-2033). This growth is fueled by several key drivers: the rising need for precise customer segmentation and targeting, the proliferation of marketing automation tools generating vast datasets, and a growing demand for measurable ROI on marketing investments. Businesses of all sizes, from large enterprises to SMEs, are increasingly relying on marketing analytics to optimize campaigns, personalize customer experiences, and improve overall marketing effectiveness. The market is segmented into online and offline services, catering to the diverse needs of businesses. Online services, leveraging sophisticated data analytics platforms and AI-powered insights, are experiencing faster growth compared to offline services. The dominance of North America and Europe is expected to continue, with the Asia-Pacific region witnessing strong growth potential due to increased digital adoption and a burgeoning middle class. However, challenges such as data privacy concerns, the need for skilled analytics professionals, and the high cost of implementation could potentially restrain market expansion. The competitive landscape is characterized by a mix of large consulting firms (Deloitte, Nielsen), specialized marketing analytics providers (Dun & Bradstreet, ClearPivot), and smaller niche players focusing on specific sectors or technologies. The market is witnessing a trend towards integrated solutions that combine marketing analytics with other marketing technologies, such as CRM and marketing automation platforms. Furthermore, the increasing availability of open-source tools and the emergence of cloud-based analytics solutions are democratizing access to marketing analytics, further fueling market growth. Over the next decade, the focus will likely shift toward predictive analytics, AI-driven insights, and the use of advanced analytics techniques to enhance customer lifetime value and improve business outcomes. This will lead to increased demand for skilled professionals specializing in data science and marketing analytics, driving further market expansion and shaping the competitive landscape.

Analysis of ‘U.S. Supermarket Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sindraanthony9985/marketing-data-for-a-supermarket-in-united-states on 28 January 2022.

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

Supermarket XYZ has been operating since 2008 and business flourished until 2016. They have a large database but they do not use them to achieve better business solutions. Their annual revenues have declined 10% and it seems to stay that way every year.

These datasets are used to analyse a supermarket in United States for the purpose of increasing revenue.

1. 50_Supermarket_Branches.csv contains the information of 50 supermarket branches such as their spending on the advertisement, administration and promotion, states and profits.

2. Ads_CTR_Optimisation.csv is based on the Click-Through Rates (CTR) from 10000 users in 10 different advertisements.

3. Market_Basket_Optimisation.csv . This dataset contains 7500 sales transactions in a week.

4. Supermarket_CustomerMembers.csv . This dataset can be used for customer segmentation.

These datasets in 'U.S. Supermarket Data' are available and legal for everyone who needs it for any kind of analytics project.

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

Success.ai’s B2B Contact Data for Marketing Professionals Worldwide equips businesses with precise and verified contact information to connect with marketing decision-makers globally. Sourced from over 170 million verified professional profiles, this dataset is tailored to meet the needs of sales, marketing, and recruitment teams, offering unparalleled accuracy, scalability, and relevance.

Why Choose Success.ai’s Marketing Professionals Data?

1. Comprehensive and Verified Contact Information:
2. Access work emails, direct phone numbers, and social profiles of marketing professionals, from strategists to CMOs.

3. Each profile is AI-validated to ensure 99% accuracy, reducing bounce rates and boosting engagement.

4. Global Coverage for Marketing Experts:

5. Includes data from key regions like North America, Europe, Asia-Pacific, South America, and more.

6. Connect with marketing leaders across industries, including e-commerce, SaaS, retail, and finance.

7. Real-Time Updates:

8. Stay ahead with continuously updated profiles that reflect role changes and career moves.

9. Compliance You Can Trust:

10. Fully adheres to data privacy laws, including GDPR and CCPA, ensuring ethical and legal use of information.

Data Highlights: - 170M+ Verified Professional Profiles: Spanning multiple industries, including marketing professionals worldwide. - 50M Work Emails: AI-validated for reliability and accuracy. - 30M Company Profiles: Rich insights into organizations, perfect for segmentation and targeting. - 700M Global Professional Profiles: A diverse dataset designed to enhance outreach and engagement efforts.

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8. Identify and engage top marketing talent for executive, mid-level, or specialist positions globally.

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11. Gain insights into marketing trends, strategies, and leadership across various sectors.

12. Identify opportunities for partnerships or strategic collaborations.

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The T-100 Domestic Market and Segment Data dataset was downloaded on April 08, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The database includes data obtained from a 100 percent census of BTS Form 41 schedule submissions by large certificated air carriers. It shows 2024 statistics for all domestic airports operated by US carriers, and all information are totals for the year. This dataset is a combination of both T-100 Market and T-100 Segments datasets. The T-100 Market includes enplanement data, and T-100 Segment data includes arrivals, departures, freight, and mail. Data is by origin airport. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529081

The heterogeneity of services in the freight transport market and the presence of imperfect information motivate the development of an empirical model for detecting market imperfection and bargaining powers of the two agents concluding a contract. The fact that agents typically bargain simultaneously on price and attributes leads to models without exogeneity assumption. The model is accordingly based on estimation of the support of the joint distribution of the price and of the attributes of the transport rather than on expectation of the price conditional to the attributes. The model proposes an innovative and integrated approach for measuring market imperfection and bargaining powers. Furthermore, the paper examines the sensitivity of the results to the choice of attributes and, in order to detect potential market segmentation, to selection of the data. The empirical work is based on a new dataset obtained from a survey based on face-to-face interviews, providing data on the price and qualitative attributes of a set of actual contracts negotiated on the Belgian freight market.

In order to enhance market share and competitiveness, large banks are increasingly focusing on promoting marketing strategies. However, the traditional bank marketing strategy often leads to the homogenization of customer demand, making it challenging to distinguish among various products. To address this issue, this paper presents a customer demand learning model based on financial datasets and optimizes the distribution model of bank big data channels through induction to rectify the imbalance in bank customer transaction data. By comparing the prediction models of random forest model and support vector machine (SVM), this paper analyzes the ability of the prediction model based on ensemble learning to significantly enhance the market segmentation of e-commerce banks. The empirical results reveal that the accuracy of random forest model reaches 92%, while the accuracy of SVM model reaches 87%. This indicates that the ensemble learning model has higher accuracy and forecasting ability than the single model. It enables the bank marketing system to implement targeted marketing, effectively maintain the relationship between customers and banks, and significantly improve the success probability of product marketing. Meanwhile, the marketing model based on ensemble learning has achieved a sales growth rate of 20% and improved customer satisfaction by 30%. This demonstrates that the implementation of the ensemble learning model has also significantly elevated the overall marketing level of bank e-commerce services. Therefore, this paper offers valuable academic guidance for bank marketing decision-making and holds important academic and practical significance in predicting bank customer demand and optimizing product marketing strategy.