According to a survey conducted by CSP Magazine in 2019, ** percent of urban consumers stated that they are visiting convenience stores more often than they were two years ago, versus only ** percent of rural consumers and ** percent of suburban customers.

This simulated customer dataset provides a practical foundation for performing segmentation analysis and identifying distinct customer groups. The dataset encompasses a blend of demographic and behavioral information, equipping users with the necessary data to develop targeted marketing strategies, personalize customer experiences, and ultimately drive sales growth.

Dataset Schema: Customer Demographics and Behavior

This dataset is structured to provide a comprehensive view of each customer, combining demographic information with detailed purchasing behavior. The columns included are:

• id: A unique identifier assigned to each customer.
• age: The customer's age in years.
• gender: The gender of the customer (e.g., Male, Female).
• income: The customer's annual income, denominated in USD.
• spending_score: A score ranging from 1 to 100 that reflects a customer's spending habits and loyalty.
• membership_years: The total number of years the customer has held a membership.
• purchase_frequency: The total number of purchases the customer has made in the last 12 months.
• preferred_category: The shopping category most frequently chosen by the customer (e.g., Electronics, Clothing, Groceries, Home & Garden, Sports).
• last_purchase_amount: The monetary value (in USD) of the customer's most recent transaction.

Potential Applications and Use Cases

The insights derived from this dataset can be applied to several key business areas:

• Customer Segmentation: Group customers into distinct segments by analyzing their demographic and behavioral data to better understand the composition of your customer base.
• Targeted Marketing: Craft and execute bespoke marketing campaigns tailored to the specific characteristics and preferences of each customer segment.
• Customer Loyalty Programs: Develop and implement loyalty initiatives that are designed to reward desirable spending behaviors and align with customer preferences.
• Sales Analysis: Examine sales data to identify purchasing patterns, understand trends, and forecast future sales performance.

This profile is designed to accompany the Joint Strategic Needs Assessment (JSNA) chapter on Demographics, which looks at segmenting the borough’s population by their most significant health and social care need. This supplement looks at adults (aged 18 and over) instead of the overall population, because the health and social care need segments covered in this section are more common in adults.

GapMaps premium demographic data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

Demographic Data attributes include:

Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for AGS Demographic Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

With Versium REACH Demographic Append you will have access to many different attributes for enriching your data.

Basic, Household and Financial, Lifestyle and Interests, Political and Donor.

Here is a list of what sorts of attributes are available for each output type listed above:

Basic: - Senior in Household - Young Adult in Household - Small Office or Home Office - Online Purchasing Indicator
- Language - Marital Status - Working Woman in Household - Single Parent - Online Education - Occupation - Gender - DOB (MM/YY) - Age Range - Religion - Ethnic Group - Presence of Children - Education Level - Number of Children

Household, Financial and Auto: - Household Income - Dwelling Type - Credit Card Holder Bank - Upscale Card Holder - Estimated Net Worth - Length of Residence - Credit Rating - Home Own or Rent - Home Value - Home Year Built - Number of Credit Lines - Auto Year - Auto Make - Auto Model - Home Purchase Date - Refinance Date - Refinance Amount - Loan to Value - Refinance Loan Type - Home Purchase Price - Mortgage Purchase Amount - Mortgage Purchase Loan Type - Mortgage Purchase Date - 2nd Most Recent Mortgage Amount - 2nd Most Recent Mortgage Loan Type - 2nd Most Recent Mortgage Date - 2nd Most Recent Mortgage Interest Rate Type - Refinance Rate Type - Mortgage Purchase Interest Rate Type - Home Pool

Lifestyle and Interests: - Mail Order Buyer - Pets - Magazines - Reading
- Current Affairs and Politics
- Dieting and Weight Loss - Travel - Music - Consumer Electronics - Arts
- Antiques - Home Improvement - Gardening - Cooking - Exercise
- Sports - Outdoors - Womens Apparel
- Mens Apparel - Investing - Health and Beauty - Decorating and Furnishing

Political and Donor: - Donor Environmental - Donor Animal Welfare - Donor Arts and Culture - Donor Childrens Causes - Donor Environmental or Wildlife - Donor Health - Donor International Aid - Donor Political - Donor Conservative Politics - Donor Liberal Politics - Donor Religious - Donor Veterans - Donor Unspecified - Donor Community - Party Affiliation

GapMaps Segmentation Data from Applied Geographic Solutions (AGS) consists of 68 segments across the US and Canada. Panorama is paired with the industry leading GfK MRI survey and AGS Demographics to provide the essential link between neighborhood demographics and consumer preferences and attitudes.

Predicting Credit Card Customer Segmentation

Exploring Key Customer Characteristics

By [source]

About this dataset

This dataset contains a wealth of customer information collected from within a consumer credit card portfolio, with the aim of helping analysts predict customer attrition. It includes comprehensive demographic details such as age, gender, marital status and income category, as well as insight into each customer’s relationship with the credit card provider such as the card type, number of months on book and inactive periods. Additionally it holds key data about customers’ spending behavior drawing closer to their churn decision such as total revolving balance, credit limit, average open to buy rate and analyzable metrics like total amount of change from quarter 4 to quarter 1, average utilization ratio and Naive Bayes classifier attrition flag (Card category is combined with contacts count in 12months period alongside dependent count plus education level & months inactive). Faced with this set of useful predicted data points across multiple variables capture up-to-date information that can determine long term account stability or an impending departure therefore offering us an equipped understanding when seeking to manage a portfolio or serve individual customers

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How to use the dataset

This dataset can be used to analyze the key factors that influence customer attrition. Analysts can use this dataset to understand customer demographics, spending patterns, and relationship with the credit card provider to better predict customer attrition.

Research Ideas

• Using the customer demographics, such as gender, marital status, education level and income category to determine which customer demographic is more likely to churn.
• Analyzing the customer’s spending behavior leading up to churning and using this data to better predict the likelihood of a customer of churning in the future.
• Creating a classifier that can predict potential customers who are more susceptible to attrition based on their credit score, credit limit, utilization ratio and other spending behavior metrics over time; this could be used as an early warning system for predicting potential attrition before it happens

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: BankChurners.csv | Column name | Description | |:---------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------| | CLIENTNUM | Unique identifier for each customer. (Integer) | | Attrition_Flag | Flag indicating whether or not the customer has churned out. (Boolean) | | Customer_Age | Age of customer. (Integer) | | Gender | Gender of customer. (String) | | Dependent_count | Number of dependents that customer has. (Integer) | | Education_Level ...

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.

A global database of population segmentation data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date audience targeting data trends for market research, audience targeting, and sales territory mapping.

Self-hosted consumer data curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The Consumer Data is standardized, unified, and ready to use.

Use cases for the Global Population Database (Consumer Data Data/Segmentation data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Segmentation data export methodology

Our location data packages are offered in CSV format. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our Population Databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

Context This dataset contains detailed, anonymized information about a bank's customers. It includes demographic data such as age, income, and family size, as well as financial information like mortgage value, credit card ownership, and average spending habits. The data is well-suited for a variety of machine learning tasks, particularly in the domain of financial services and marketing.

Content The dataset consists of 5000 customer records with 14 attributes:

• Customer_ID: A unique identifier for each customer.
• Age: The customer's age in completed years.
• Years_Experience: Years of professional experience.
• Annual_Income: Annual income of the customer (in thousands of dollars).
• ZIP_Code: The customer's home address ZIP code.
• Family_size: The number of individuals in the customer's family.
• Avg_Spending: Average monthly spending on credit cards (in thousands of dollars).
• Education_Level: A categorical variable for education level (1: Undergraduate, 2: Graduate, 3: Advanced/Professional).
• Mortgage: The value of the customer's house mortgage if any (in thousands of dollars).
• Has_Consumer_Loan: Binary variable indicating if the customer accepted a personal loan in the last campaign (1: Yes, 0: No). This is a potential target variable.
• Has_Securities_Account: Binary variable indicating if the customer has a securities account with the bank.
• Has_CD_Account: Binary variable indicating if the customer has a certificate of deposit (CD) account with the bank.
• Uses_Online_Banking: Binary variable indicating if the customer uses online banking services.
• Has_CreditCard: Binary variable indicating if the customer uses a credit card issued by this bank.

Data Quality Note Some rows contain negative values for the Years_Experience column. This is a data quality issue that may require preprocessing (e.g., imputation by taking the absolute value or using the average of similar age groups).

Potential Use Cases This dataset is excellent for both educational and practical purposes. You can use it to:

1. Predict Loan Acceptance: Build a classification model to predict which customers are most likely to accept a personal loan (Has_Consumer_Loan).
2. Customer Segmentation: Use clustering algorithms (like K-Means) to identify distinct customer segments for targeted marketing campaigns.
3. Credit Card Adoption: Analyze the factors that influence a customer's decision to get a bank-issued credit card.
4. Exploratory Data Analysis (EDA): Practice your data analysis and visualization skills to uncover insights about customer behavior.

Demographic Analysis of Shopping Behavior: Insights and Recommendations

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

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

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

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

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

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

This service offers Esri's Updated Demographics, Census Data, Tapestry Segmentation, and Business Summary data for the United States. Updates are based on the decennial census, Infogroup business data, other public and proprietary data sources, and proprietary models.

All attributes are available at all geography levels: country, state, county, tract, block group, ZIP code, place, county subdivision, congressional district, core-based statistical area (CBSA), and designated market area (DMA).

There are over 2,100 attributes in categories such as: population, households, race and ethnicity, educational attainment, marital status, employment by industry and occupation, income, net worth, housing and home value, number of businesses and employees, sales, and many others. Key attributes from the 2010 Census such as population, are presented for reference. Some attributes such as population, income, and home value, are also projected five years to 2021.

Esri offers Updated Demographics for 2019 and 2024 and Tapestry Segmentation for 2019. Esri provides Census Data for geographies not supplied by the Census Bureau including ZIP Codes and DMAs.

To view ArcGIS Online items using this service, including the terms of use, visit http://goto.arcgisonline.com/demographics9/USA_Demographics_and_Boundaries_2019.

This dataset contains transaction details of users, including their demographics and purchasing behavior. It features information such as User ID, Age, Gender, Country, Purchase Amount, Purchase Date, and Product Category. This data can be useful for analyzing consumer trends, demographic influences on purchasing behavior, and market segmentation.

• User ID: A unique identifier assigned to each user for tracking their transactions.
• Age: The age of the user at the time of purchase, which may influence buying behavior.
• Gender: The gender of the user, allowing for demographic segmentation of purchasing patterns.
• Country: The country of residence for the user, useful for regional market analysis.
• Purchase Amount: The total amount spent by the user during a transaction.
• Purchase Date: The date when the purchase was made, allowing for temporal analysis of buying behavior.
• Product Category: The category of the product purchased, aiding in understanding consumer preferences.

Audience Segmentation for OTT Market Outlook

According to our latest research, the global Audience Segmentation for OTT market size reached USD 5.7 billion in 2024, reflecting robust expansion driven by the proliferation of digital media consumption and advanced data analytics. The market is expected to maintain a strong growth trajectory, registering a CAGR of 14.8% from 2025 to 2033, and is forecasted to reach USD 17.7 billion by 2033. This rapid growth is primarily fueled by the rising adoption of OTT platforms, the increasing importance of personalized content delivery, and the integration of AI-driven segmentation tools into OTT service ecosystems.

One of the most significant growth drivers in the Audience Segmentation for OTT market is the dramatic shift in consumer behavior towards digital streaming services. As traditional media consumption declines, OTT platforms are witnessing exponential user growth, leading to an increased demand for sophisticated audience segmentation tools. These solutions enable OTT providers to analyze vast datasets, extract actionable insights, and deliver hyper-personalized experiences. The evolution of machine learning and artificial intelligence has further enhanced the granularity and accuracy of audience segmentation, allowing platforms to cater to diverse viewer preferences, optimize content recommendations, and boost user engagement. The surge in smartphone penetration and affordable high-speed internet, especially in emerging markets, has also played a pivotal role in expanding the OTT audience base, necessitating more nuanced segmentation strategies.

Another crucial factor propelling market growth is the intensifying competition among OTT platforms. As the market becomes increasingly saturated, providers are leveraging audience segmentation to differentiate their offerings and maximize subscriber retention. Advanced segmentation strategies—spanning demographic, psychographic, behavioral, geographic, and technographic parameters—enable platforms to tailor marketing campaigns, enhance targeted advertising, and minimize churn rates. The integration of real-time analytics and predictive modeling empowers OTT services to anticipate viewer needs, optimize ad placements, and drive higher conversion rates. Moreover, the growing emphasis on privacy-compliant data collection and analysis is fostering trust among users, encouraging them to share more information that can be used to refine segmentation models further.

The ongoing digital transformation across industries has also contributed to the expansion of the Audience Segmentation for OTT market. Enterprises, particularly in the media, entertainment, and advertising sectors, are increasingly adopting advanced segmentation solutions to gain a competitive edge. The proliferation of smart TVs, connected devices, and multi-platform viewing experiences has created new touchpoints for data collection and audience analysis. As OTT platforms continue to diversify their content portfolios and expand into new geographies, the need for localized and contextually relevant segmentation becomes paramount. Regulatory developments, such as data protection laws and cross-border data transfer policies, are shaping the evolution of audience segmentation practices, compelling OTT providers to adopt more transparent and secure methodologies.

Regionally, North America remains the dominant market for Audience Segmentation in OTT, accounting for the largest revenue share in 2024. The region’s advanced digital infrastructure, high internet penetration, and mature OTT ecosystem have facilitated the widespread adoption of segmentation solutions. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid urbanization, increasing disposable incomes, and a burgeoning population of digital-first consumers. Europe continues to demonstrate steady growth, supported by robust regulatory frameworks and a strong focus on data privacy. Latin America and the Middle East & Africa are also witnessing increased adoption, albeit at a relatively nascent stage, as OTT platforms expand their reach and tailor their offerings to local preferences.

Segmentation Type Analysis

The Segmentation Type segment plays a pivotal role in the Audience Segmentation for OTT market, encompassing demographic, psychographic, behavioral, geographic, and technographic categorization. Demographic segmentation remains a foundational approach, enabling OTT platforms

Mall Shoppers Customer Segmentation Dataset

Overview:

The Mall Shoppers Customer Segmentation Dataset is a rich collection of data designed to provide insights into the shopping behaviors and demographic profiles of customers visiting a mall. This dataset is pivotal for businesses aiming to tailor their marketing strategies, improve customer engagement, and enhance the shopping experience through targeted offers and services.

Content:

The dataset includes information on several hundred mall visitors, encompassing a variety of features such as:

• Customer ID: A unique identifier for each customer.
• Age: The age of the customer.
• Gender: The gender of the customer.
• Annual Income (k$): The annual income of the customer, expressed in thousands of dollars.
• Spending Score (1-100): A score assigned to the customer based on their spending behavior and purchasing data. A higher score indicates higher spending.

Purpose:

The primary purpose of this dataset is to enable the identification of distinct customer segments within the mall's clientele. By analyzing patterns in age, income, spending score, and gender, businesses can uncover valuable insights into customer preferences and behaviors. This, in turn, allows for the development of targeted marketing strategies, personalized shopping experiences, and improved product offerings to meet the diverse needs of each customer segment.

Applications:

This dataset is an excellent resource for: - Customer Segmentation: Utilizing clustering techniques to categorize customers into meaningful groups based on their features. - Targeted Marketing: Crafting personalized marketing campaigns aimed at specific customer segments to increase engagement and sales. - Market Analysis: Understanding the demographic makeup and spending habits of mall visitors to inform business decisions and strategies. - Personalization: Enhancing the customer experience through personalized services, recommendations, and offers.

Conclusion:

The Mall Shoppers Customer Segmentation Dataset offers a foundational step towards a deeper understanding of customer dynamics in a retail environment. It serves as a valuable asset for retailers, marketers, and business analysts seeking to leverage data-driven insights for strategic advantage.

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 data prediction is powered by Demografy AI that extracts demographic data from names with 100% coverage, accuracy preview before purchase and GDPR-compliance.

Demografy is a privacy by design customer demographics prediction AI platform.

Use cases: - Social Media analytics and user segmentation - Competitor analysis - Actionable analytics about your customers to get demographic insights - Appending missing demographic data to your records for customer segmentation and targeted marketing campaigns - Enhanced personalization knowing you customer better

Core features: - Demographic segmentation - Demographic analytics - API integration - Data export

Key advantages: - 100% coverage of lists - Accuracy estimate before purchase - GDPR-compliance as no sensitive data is required. Demografy can work with only first names or masked last names

Unlike traditional solutions, you don’t need to know and disclose your customer or prospect addresses, emails or other sensitive information. You need only names of social media users. This makes Demografy privacy by design and enables you to get 100% coverage of your audience since all you need to know is names.

Metadata

Title: Customer Satisfaction and Demographics Dataset

Description: This dataset contains information related to customer satisfaction scores, demographics, and purchasing behavior across various product categories. The data was collected from 1,050 customer interactions and includes fields such as Customer ID, Product Category, Satisfaction Score, Days for Delivery, Customer Service Interaction, Purchase Amount, Customer Age, and Gender.

Dataset Columns: 1. Customer ID: Unique identifier for each customer. Contains 52 missing values. 2. Product Category: The category of the product purchased (e.g., Electronics, Books, Home Goods). Contains 59 missing values. 3. Satisfaction Score: A score from 1 to 12 representing the customer's satisfaction with their purchase. Contains 52 missing values. 4. Days for Delivery: Number of days taken for the product to be delivered. Contains 65 missing values. 5. Customer Service Interaction: Whether the customer had an interaction with customer service (Yes/No). Contains 205 missing values. 6. Purchase Amount: The total amount spent by the customer on the purchase. Contains 51 missing values. 7. Customer Age: The age of the customer at the time of purchase. Contains 40 missing values. 8. Gender: The gender of the customer (Male, Female, Other). Contains 212 missing values.

Additional Information: - Number of Duplicate Rows: The dataset contains 47 duplicate rows. - Total Number of Entries: 1,050. - Data Types: The dataset includes both numerical (e.g., Satisfaction Score, Days for Delivery, Purchase Amount, Customer Age) and categorical data (e.g., Customer ID, Product Category, Customer Service Interaction, Gender).

What Can Be Done with This Data: - Predictive Modeling: Develop predictive models to identify factors contributing to customer satisfaction. - Customer Segmentation: Perform clustering analysis to segment customers based on their demographic and purchasing behavior. - Sentiment Analysis: Explore the relationship between customer service interactions and satisfaction scores. - Time Series Analysis: Analyze trends in delivery times and their impact on satisfaction. - Demographic Analysis: Investigate how demographic factors such as age and gender affect purchasing decisions and satisfaction levels. - Exploratory Data Analysis (EDA): Perform EDA to uncover patterns, correlations, and anomalies within the dataset, including handling missing values and duplicates.