E-Commerce Customer Segmentation Dataset This synthetic dataset contains information about 20 customers of an e-commerce platform, designed for customer segmentation and classification tasks.

Dataset Overview Each record represents a unique customer with demographic and behavioral features that help classify them into different customer segments.

Features: customer_id: Unique identifier for each customer

age: Age of the customer (years)

annual_income_k$: Annual income in thousands of dollars

spending_score: A score between 0 and 100 indicating customer spending habits (higher means more spending)

membership_years: Length of membership in years

segment: Customer segment label; possible values are:

Low (low-value customers)

Medium (medium-value customers)

High (high-value customers)

Potential Use Cases Customer segmentation

Targeted marketing campaigns

Customer lifetime value prediction

Behavioral analytics and profiling

Clustering and classification algorithm testing

Dataset Size 20 samples

6 columns

License This dataset is provided under the Apache 2.0 License.

The User Profile Data is a structured, anonymized dataset designed to help organizations understand who their users are, what devices they use, and where they are located. Each record provides privacy-compliant linkages between user IDs, demographic profiles, device intelligence, and geolocation data, offering deep context for analytics, segmentation, and personalization.

Built for privacy-safe analytics, the dataset uses hashed identifiers like phone number and email and standardized formats, making it easy to integrate into big-data platforms, AI pipelines, and machine learning models for advanced analytics.

Demographic insights include gender, age, and age group, essential for audience profiling, marketing optimization, and consumer intelligence. All gender data is user-declared and AI-verified through image-based avatar validation, ensuring data accuracy and authenticity.

The dataset’s Device Intelligence Layer includes rich technical attributes such as device brand, model, OS version, user agent, RAM, language, and timezone, enabling technical segmentation, performance analytics, and targeted ad delivery across diverse device ecosystems.

On the location and POI front, the dataset combines GPS-based and IP-based coordinates—including country, region, city, latitude, longitude —to provide high-precision geospatial insights. This enables mobility pattern analysis, market expansion planning, and POI clustering for advanced location intelligence.

Each user record contains onboarding and lifecycle fields like unique IDs, and profile update timestamps, allowing accurate tracking of user acquisition trends, data freshness, and activity duration.

🔍 Key Features • 1st-party, consent-based demographic & device data • AI-verified gender insights via avatar recognition • OS-level app data with 120+ daily sessions per user • Global coverage across APAC and emerging markets • GPS + IP-based geolocation & POI intelligence • Privacy-compliant, hashed identifiers for safe integration

🚀 Use Cases • Audience segmentation & lookalike modeling • Ad-tech and mar-tech optimization • Geospatial & POI analytics • Fraud detection & risk scoring • Personalization & recommendation engines • App performance & device compatibility insights

🏢 Industries Served Ad-Tech • Mar-Tech • FinTech • Telecom • Retail Analytics • Consumer Intelligence • AI & ML Platforms

Bank Customer Segmentation

Most banks have a large customer base - with different characteristics in terms of age, income, values, lifestyle, and more. Customer segmentation is the process of dividing a customer dataset into specific groups based on shared traits.

According to a report from Ernst & Young, “A more granular understanding of consumers is no longer a nice-to-have item, but a strategic and competitive imperative for banking providers. Customer understanding should be a living, breathing part of everyday business, with insights underpinning the full range of banking operations.

About this Dataset

This dataset consists of 1 Million+ transaction by over 800K customers for a bank in India. The data contains information such as - customer age (DOB), location, gender, account balance at the time of the transaction, transaction details, transaction amount, etc.

Interesting Analysis Ideas

The dataset can be used for different analysis, example -

1. Perform Clustering / Segmentation on the dataset and identify popular customer groups along with their definitions/rules
2. Perform Location-wise analysis to identify regional trends in India
3. Perform transaction-related analysis to identify interesting trends that can be used by a bank to improve / optimi their user experiences
4. Customer Recency, Frequency, Monetary analysis
5. Network analysis or Graph analysis of customer data.

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!

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.

Here's a step-by-step guide on how to approach user segmentation for FitTrackr:

Define your segmentation goals: Start by determining what you want to achieve with user segmentation. For example, you might want to identify the most engaged users, understand the demographics of your user base, or target specific user groups with personalized promotions.

Gather data: Collect relevant data about your app users. This can include demographic information (age, gender, location), app usage data (frequency of app usage, time spent on different features), user behavior (types of workouts, goals set, achievements unlocked), and any other relevant data points available to you.

Identify relevant segmentation variables: Based on the goals you defined, identify the key variables that will help you segment your user base effectively. For FitTrackr, potential variables could include age, gender, fitness goals (e.g., weight loss, muscle gain), workout preferences (e.g., cardio, strength training), and user engagement level.

Segment the user base: Use clustering techniques or segmentation algorithms to divide your user base into distinct segments based on the identified variables. You can employ methods such as k-means clustering, hierarchical clustering, or even machine learning algorithms like decision trees or random forests.

Analyze and profile each segment: Once the segmentation is done, analyze each segment to understand their characteristics, preferences, and needs. Create detailed user profiles for each segment, including demographic information, app usage patterns, fitness goals, and any other relevant attributes. This will help you tailor your marketing messages and app features to each segment's specific requirements.

Develop targeted strategies: Based on the insights gained from user profiles, develop targeted marketing strategies and app features for each segment. For example, if you have a segment of users who primarily focus on weight loss, you might create personalized workout plans or send them motivational content related to weight management.

Implement and evaluate: Implement the targeted strategies and monitor their effectiveness. Continuously evaluate and refine your segmentation approach based on user feedback, engagement metrics, and the achievement of your goals.

Success.ai’s Consumer Marketing Data API empowers your marketing, analytics, and product teams with on-demand access to a vast and continuously updated dataset of consumer insights. Covering detailed demographics, behavioral patterns, and purchasing histories, this API enables you to go beyond generic outreach and craft tailored campaigns that truly resonate with your target audiences.

With AI-validated accuracy and support for precise filtering, the Consumer Marketing Data API ensures you’re always equipped with the most relevant data. Backed by our Best Price Guarantee, this solution is essential for refining your strategies, improving conversion rates, and driving sustainable growth in today’s competitive consumer landscape.

Why Choose Success.ai’s Consumer Marketing Data API?

1. Tailored Consumer Insights for Precision Targeting

   • Access verified demographic, behavioral, and purchasing data to understand what consumers truly value.
   • AI-driven validation ensures 99% accuracy, minimizing wasted spend and improving engagement outcomes.

2. Comprehensive Global Reach

   • Includes consumer profiles from diverse regions and markets, enabling you to scale campaigns and discover emerging opportunities.
   • Adapt swiftly to new markets, product launches, and shifting consumer preferences with real-time data at your fingertips.

3. Continuously Updated and Real-Time Data

   • Receive ongoing updates that reflect evolving consumer behaviors, interests, and market trends.
   • Respond quickly to seasonal changes, competitor moves, and industry disruptions, ensuring your campaigns remain timely and relevant.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, guaranteeing responsible and lawful data usage.

Data Highlights:

• Detailed Demographics: Age, gender, location, and income levels to refine targeting and messaging.
• Behavioral Insights: Interests, browsing patterns, and content consumption habits to anticipate consumer needs.
• Purchasing History: Understand consumer spending, brand loyalty, and product preferences to tailor promotions effectively.
• Real-Time Updates: Keep pace with evolving consumer tastes, ensuring your strategies remain forward-focused and competitive.

Key Features of the Consumer Marketing Data API:

1. Granular Targeting and Segmentation

   • Query the API to segment consumers by demographics, interests, past purchases, or engagement patterns.
   • Focus campaigns on the most receptive audiences, enhancing conversion rates and ROI.

2. Flexible and Seamless Integration

   • Easily integrate the API into CRM systems, marketing automation tools, or analytics platforms.
   • Streamline workflows and eliminate manual data imports, freeing resources for strategic initiatives.

3. Continuous Data Enrichment

   • Refresh consumer profiles with the latest data, ensuring every decision is backed by current insights.
   • Reduce data decay and maintain top-notch data hygiene to maximize long-term marketing effectiveness.

4. AI-Driven Validation

   • Rely on advanced AI validation techniques to guarantee high-quality data accuracy and reliability.
   • Increase confidence in your campaigns and decrease budget wasted on irrelevant targets.

Strategic Use Cases:

1. Highly Personalized Marketing Campaigns

   • Deliver tailored offers, recommendations, and content that align with individual consumer preferences.
   • Boost engagement and loyalty by making every touchpoint relevant and meaningful.

2. Market Expansion and Product Launches

   • Identify segments most receptive to new products or services, ensuring successful market entry.
   • Stay ahead of consumer demands, evolving your product line and marketing mix to meet changing preferences.

3. Competitive Analysis and Trend Forecasting

   • Leverage consumer insights to anticipate emerging trends and outpace competitors in capturing new markets.
   • Adjust marketing strategies proactively to capitalize on seasonal, cultural, or economic shifts.

4. Customer Retention and Loyalty Programs

   • Use historical purchase and engagement data to identify at-risk customers and implement retention strategies.
   • Cultivate brand advocates by delivering personalized offers and exclusive perks to loyal consumers.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality consumer marketing data at unmatched prices, ensuring maximum ROI for your outreach efforts.

2. Seamless Integration

   • Easily incorporate the API into existing workflows, eliminating data silos and manual data management.

3. Data Accuracy with AI Validation

   • Depend on 99% accuracy to guide data-driven decisions, refine targeting, and elevate your marketing initiatives.

4. Customizable and Scalable Solutions

   • Tailor datasets to focus on specific demog...

Project Overview: Customer Segmentation Using K-Means Clustering

Introduction In this project, I analysed customer data from a retail store to identify distinct customer segments. The dataset includes key attributes such as age, city, and total sales of the customers. By leveraging K-Means clustering, an unsupervised machine learning technique, I aim to group customers based on their age and sales metrics. These insights will enable the creation of targeted marketing campaigns tailored to the specific needs and behaviours of each customer segment.

Objectives - Cluster Customers: Use K-Means clustering to group customers based on age and total sales. - Analyse Segments: Examine the characteristics of each customer segment. - Targeted Marketing: Develop strategies for personalized marketing campaigns targeting each identified customer group.

Data Description The dataset comprises:

• Age: The age of the customers.
• City: The city where the customers reside.
• Total Sales: The total sales generated by each customer.

Methodology - Data Preprocessing: Clean and preprocess the data to handle any missing or inconsistent entries. - Feature Selection: Focus on age and total sales as primary features for clustering. - K-Means Clustering: Apply the K-Means algorithm to identify distinct customer segments. - Cluster Analysis: Analyse the resulting clusters to understand the demographic and sales characteristics of each group. - Marketing Strategy Development: Create targeted marketing strategies for each customer segment to enhance engagement and sales.

Expected Outcomes - Customer Segments: Clear identification of customer groups based on age and purchasing behaviour. - Insights for Marketing: Detailed understanding of each segment to inform targeted marketing efforts. - Business Impact: Enhanced ability to tailor marketing campaigns, potentially leading to increased customer satisfaction and sales.

By clustering customers based on age and total sales, this project aims to provide actionable insights for personalized marketing, ultimately driving better customer engagement and higher sales for the retail store.

Living Identity™ Asia delivers 401M verified profiles across 7 high-growth Asian markets: Bangladesh, Indonesia, Malaysia, Myanmar, Philippines, Thailand, and Vietnam. This dataset combines identity, lifestyle, demographic, and location signals — ideal for KYC, segmentation, and marketing expansion.

➤ Optimized For: ・Real-time KYC and identity verification ・Location-based audience analytics ・Data-driven market expansion strategy ・Cross-sell/upsell strategy based on lifestyle and affluence ・Customer segmentation and campaign design

➤ Designed For: Marketing & Media Agencies Plan hyper-targeted, region-specific campaigns

Retailers, E-Commerce & Payment Firms Expand across Asia using verified consumer intelligence

Customer Analytics & Intelligence Teams Enrich identity data with lifestyle and location layers

Audience Modeling & AI Teams Train segmentation and targeting models with ground-truth attributes

Financial Services Firms Improve onboarding, scoring, and customer profiling in underbanked markets

➤ Key Highlights: ・401M+ structured profiles across 7 countries ・6 months of refreshed historical activity ・Geo-coded data with lifestyle and demographic detail ・Core identity fields: name, ID, phone, email, address, government ID (where available) ・Delivered securely via on-premise systems

Delivered by 1datapipe®, the global leader in structured identity and lifestyle intelligence. Pricing and additional samples available upon request.

Archetype Data’s B2C Consumer File is one of the most comprehensive and data-rich consumer datasets in the United States, encompassing over 260 million verified individuals and households. Designed for precision marketing, analytics, and customer intelligence, this dataset delivers unparalleled depth across lifestyle, demographic, financial, and behavioral dimensions enabling businesses to understand, segment, and engage consumers with accuracy and confidence.

Each consumer record includes fundamental demographic elements such as name, age, gender, location, household composition, and contact information. Building upon that, Archetype Data enriches every profile with 400+ lifestyle, financial, and behavioral variables that capture consumer intent, spending capacity, purchasing habits, media preferences, and digital engagement patterns. This multidimensional view empowers marketers, insurers, and data-driven enterprises to identify not just who a consumer is—but how they live, shop, and connect.

What truly differentiates Archetype Data’s B2C file is its integration with our Linq360™ B2B2C dataset, which links consumers to the businesses they own or operate. This linkage provides a powerful bridge between professional and personal identity, offering unparalleled insight into small business owners, entrepreneurs, and professionals as both business decision-makers and consumers.

Whether activating audiences across CTV, programmatic display, social, or direct mail, our data seamlessly maps into today’s leading marketing and advertising ecosystems, including LiveRamp, The Trade Desk, and other major platforms.

The B2C Consumer File supports a wide range of applications; audience segmentation, modeling, CRM enrichment, lookalike development, and attribution measurement—across industries such as retail, finance, insurance, media, and healthcare. Whether you’re building a custom audience for a digital campaign, enriching customer records, or analyzing lifestyle trends within a region, Archetype Data’s file provides the scale and precision needed to deliver meaningful results.

Food festivals have been a growing tourism sector in recent years due to their contributions to a region’s economic, marketing, brand, and social growth. This study analyses the demand for the Bahrain food festival. The stated objectives were: i) To identify the motivational dimensions of the demand for the food festival, (ii) To determine the segments of the demand for the food festival, and (iii) To establish the relationship between the demand segments and socio-demographic aspects. The food festival investigated was the Bahrain Food Festival held in Bahrain, located on the east coast of the Persian Gulf. The sample consisted of 380 valid questionnaires and was taken using social networks from those attending the event. The statistical techniques used were factorial analysis and the K-means grouping method. The results show five motivational dimensions: Local food, Art, Entertainment, Socialization, and Escape and novelty. In addition, two segments were found; the first, Entertainment and novelties, is related to attendees who seek to enjoy the festive atmosphere and discover new restaurants. The second is Multiple motives, formed by attendees with several motivations simultaneously. This segment has the highest income and expenses, making it the most important group for developing plans and strategies. The results will contribute to the academic literature and the organizers of food festivals.

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.

Overview This product, with over 100 actual and modelled variables, is designed to help you gain better insight into your customers and prospects. The Enhance dataset provides users with a set of predictive and descriptive attributes which support more informed, targeted and relevant marketing to consumers.

What is it? Enhance Core is an individual level data set, containing self-declared, freely given socio-demographic data on over 90m individuals. The data is obtained from a range of sources, including; Satisfaction & Lifestyle surveys, Website Registrations, Newsletter & Service subscriptions, Offers & Competition websites and public Social Media feeds.

Use cases -Using key information, appended from Enhance, to create personalised messaging for direct mail & digital marketing campaigns - Using Profiling & Predictive messaging to identify important cohorts within the customer base, and those that can be “Forgotten” - Seeing how the current customer base compares to the UK base, so you can identify which potential audiences you are missing and also those that your business excels in. - Segment your customers into distinct groups so that you can offer them the right products through the most appropriate channels

Additional Insights Enhance Core, Property & Geo (Individual, Property & Postcode level data) can all be used modularly, allowing you to understand the full picture of your customer base, considering not only their individual variance but also where they live & those around them.

🛒 E-Commerce Customer Behavior and Sales Dataset 📊 Dataset Overview This comprehensive dataset contains 5,000 e-commerce transactions from a Turkish online retail platform, spanning from January 2023 to March 2024. The dataset provides detailed insights into customer demographics, purchasing behavior, product preferences, and engagement metrics.

🎯 Use Cases This dataset is perfect for:

Customer Segmentation Analysis: Identify distinct customer groups based on behavior Sales Forecasting: Predict future sales trends and patterns Recommendation Systems: Build product recommendation engines Customer Lifetime Value (CLV) Prediction: Estimate customer value Churn Analysis: Identify customers at risk of leaving Marketing Campaign Optimization: Target customers effectively Price Optimization: Analyze price sensitivity across categories Delivery Performance Analysis: Optimize logistics and shipping 📁 Dataset Structure The dataset contains 18 columns with the following features:

Order Information Order_ID: Unique identifier for each order (ORD_XXXXXX format) Date: Transaction date (2023-01-01 to 2024-03-26) Customer Demographics Customer_ID: Unique customer identifier (CUST_XXXXX format) Age: Customer age (18-75 years) Gender: Customer gender (Male, Female, Other) City: Customer city (10 major Turkish cities) Product Information Product_Category: 8 categories (Electronics, Fashion, Home & Garden, Sports, Books, Beauty, Toys, Food) Unit_Price: Price per unit (in TRY/Turkish Lira) Quantity: Number of units purchased (1-5) Transaction Details Discount_Amount: Discount applied (if any) Total_Amount: Final transaction amount after discount Payment_Method: Payment method used (5 types) Customer Behavior Metrics Device_Type: Device used for purchase (Mobile, Desktop, Tablet) Session_Duration_Minutes: Time spent on website (1-120 minutes) Pages_Viewed: Number of pages viewed during session (1-50) Is_Returning_Customer: Whether customer has purchased before (True/False) Post-Purchase Metrics Delivery_Time_Days: Delivery duration (1-30 days) Customer_Rating: Customer satisfaction rating (1-5 stars) 📈 Key Statistics Total Records: 5,000 transactions Date Range: January 2023 - March 2024 (15 months) Average Transaction Value: ~450 TRY Customer Satisfaction: 3.9/5.0 average rating Returning Customer Rate: 60% Mobile Usage: 55% of transactions 🔍 Data Quality ✅ No missing values ✅ Consistent formatting across all fields ✅ Realistic data distributions ✅ Proper data types for all columns ✅ Logical relationships between features 💡 Sample Analysis Ideas Customer Segmentation with K-Means Clustering

Segment customers based on spending, frequency, and recency Sales Trend Analysis

Identify seasonal patterns and peak shopping periods Product Category Performance

Compare revenue, ratings, and return rates across categories Device-Based Behavior Analysis

Understand how device choice affects purchasing patterns Predictive Modeling

Build models to predict customer ratings or purchase amounts City-Level Market Analysis

Compare market performance across different cities 🛠️ Technical Details File Format: CSV (Comma-Separated Values) Encoding: UTF-8 File Size: ~500 KB Delimiter: Comma (,) 📚 Column Descriptions Column Name Data Type Description Example Order_ID String Unique order identifier ORD_001337 Customer_ID String Unique customer identifier CUST_01337 Date DateTime Transaction date 2023-06-15 Age Integer Customer age 35 Gender String Customer gender Female City String Customer city Istanbul Product_Category String Product category Electronics Unit_Price Float Price per unit 1299.99 Quantity Integer Units purchased 2 Discount_Amount Float Discount applied 129.99 Total_Amount Float Final amount paid 2469.99 Payment_Method String Payment method Credit Card Device_Type String Device used Mobile Session_Duration_Minutes Integer Session time 15 Pages_Viewed Integer Pages viewed 8 Is_Returning_Customer Boolean Returning customer True Delivery_Time_Days Integer Delivery duration 3 Customer_Rating Integer Satisfaction rating 5 🎓 Learning Outcomes By working with this dataset, you can learn:

Data cleaning and preprocessing techniques Exploratory Data Analysis (EDA) with Python/R Statistical analysis and hypothesis testing Machine learning model development Data visualization best practices Business intelligence and reporting 📝 Citation If you use this dataset in your research or project, please cite:

E-Commerce Customer Behavior and Sales Dataset (2024) Turkish Online Retail Platform Data (2023-2024) Available on Kaggle ⚖️ License This dataset is released under the CC0: Public Domain license. You are free to use it for any purpose.

🤝 Contribution Found any issues or have suggestions? Feel free to provide feedback!

📞 Contact For questions or collaborations, please reach out through Kaggle.

Happy Analyzing! 🚀

Keywords: e-c...

Success.ai’s Audience Targeting Data API empowers your marketing, sales, and product teams with on-demand access to a vast dataset of over 700 million verified global profiles. By delivering rich demographic, firmographic, and behavioral insights, this API enables you to hone in on precisely the right audiences for your campaigns.

Whether you’re exploring new markets, optimizing ABM strategies, or refining personalization techniques, Success.ai’s data ensures your message reaches the most relevant prospects. Backed by our Best Price Guarantee, this solution is indispensable for maximizing engagement, conversion, and ROI in a competitive global environment.

Why Choose Success.ai’s Audience Targeting Data API?

1. Vast, Verified Global Coverage

   • Access a broad range of professional and consumer profiles spanning industries, regions, and roles.
   • Expand confidently into new markets and segments, supported by accurate, continuously updated data.

2. AI-Validated Accuracy

   • Depend on 99% accuracy through AI-driven validation processes, reducing wasted spend and improving campaign performance.
   • Trust that your targeting efforts are always based on the most current and reliable information available.

3. Continuous Data Refreshes

   • Receive real-time updates to ensure your contact lists remain relevant and reflective of evolving market conditions.
   • Swiftly adapt strategies to seasonal shifts, product launches, or changing buyer behaviors, maintaining long-term effectiveness.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, guaranteeing responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with diverse, high-quality audiences from any industry or market segment.
• Demographic, Firmographic, and Behavioral Insights: Tailor campaigns with nuanced targeting strategies.
• Real-Time Data: Dynamically adjust outreach as market conditions evolve, ensuring relevance and timeliness.
• Best Price Guarantee: Achieve premium results at highly competitive prices, optimizing your ROI.

Key Features of the Audience Targeting Data API:

1. Granular Segmentation and Query

   • Filter audiences by demographics, industry, location, job role, purchasing patterns, and more.
   • Zero in on precisely the profiles that match your ideal customer profile (ICP) criteria, driving conversion and efficiency.

2. Instant Data Enrichment

   • Enhance existing CRM or marketing automation systems with continuously updated audience data, removing manual data imports and guesswork.
   • Maintain pristine data hygiene, ensuring your team always works with actionable intelligence.

3. Seamless Integration and Flexibility

   • Effortlessly incorporate the API into your existing workflows, marketing tools, or analytics platforms.
   • Adjust parameters, queries, and segmentation strategies as your business objectives evolve or market conditions shift.

4. AI-Driven Validation and Reliability

   • Leverage AI-powered verification to confirm data accuracy, reducing bounce rates and improving engagement outcomes.
   • Confidently invest resources in campaigns backed by verified, real-time data.

Strategic Use Cases:

1. Highly Personalized Campaigns

   • Use demographic and behavioral insights to craft tailored messages and offers.
   • Improve engagement, open rates, and conversions by delivering content that resonates with targeted segments.

2. ABM Strategies and Market Expansion

   • Identify and target key accounts or emerging market opportunities with precision.
   • Develop ABM campaigns that focus on decision-makers and influencers, accelerating deal velocity.

3. Product Launches and Seasonal Promotions

   • Quickly adapt targeting parameters to match seasonal trends, promotional periods, or new product introductions.
   • Engage ideal audiences at the most opportune moments, ensuring campaign relevance and impact.

4. Enhanced Competitive Advantage

   • Monitor audience shifts and competitor moves, refining segmentation and messaging proactively.
   • Stay a step ahead by anticipating audience needs and adjusting campaigns for maximum market resonance.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access top-tier audience targeting data at competitive prices, ensuring unparalleled value and ROI.

2. Seamless Integration

   • Incorporate the API into your current marketing stacks, simplifying workflows and improving team productivity.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide data-driven decisions, enhance targeting, and achieve exceptional campaign performance.

4. Customizable and Scalable Solutions

   • Tailor datasets and segmentation parameters to align perfectly with your evolving business needs, product lines, and strategic imperatives.

Additional...

Demographic, radiological, and cancer staging sample statistics of the training, validation, and testing cohorts from 219 HCC patients included in this study.

Distribution of demographics in the US (n = 476) and German (n = 491) online sample compared to census data from both countries.

Abstract

The BreastDCEDL_ISPY2 dataset is a curated, deep learning–ready resource that integrates pretreatment 3D Dynamic Contrast-Enhanced MRI (DCE-MRI) scans from 982 breast cancer patients enrolled in the I-SPY2 TRIAL, sourced from The Cancer Imaging Archive (TCIA). Imaging data has been standardized from raw DICOM to 3D NIfTI volumes, preserving signal integrity and spatial resolution. The dataset includes extensive non-imaging supporting data, such as tumor annotations, DICOM metadata, and demographics. To facilitate reproducible research, fixed benchmark train/validation/test splits are provided, stratified by biomarker subtypes and response outcomes. This dataset enables diverse research applications, including the development of deep learning models for predicting treatment response, radiomics-based analyses, and hormone receptor (HR) and HER2 status classification. It also facilitates benchmarking of advanced architectures such as Vision Transformers, and supports clinical translation efforts in the field of precision oncology

Introduction

Breast cancer remains one of the most prevalent causes of cancer-related mortality worldwide, and early detection coupled with accurate treatment response monitoring is essential for improving outcomes. Dynamic Contrast-Enhanced MRI (DCE-MRI) is a cornerstone modality for breast cancer imaging, offering unique insights into tumor vascularity, morphology, and treatment response. Despite its clinical importance, progress in computational and deep learning–based analysis of DCE-MRI has been hindered by the lack of large, standardized, and publicly available datasets. The BreastDCEDL_ISPY2 dataset was created to address this gap by consolidating and harmonizing imaging and clinical data from the I-SPY2 TRIAL. With 982 patients across more than 22 institutions, it represents one of the largest publicly accessible collections of pre-treatment DCE-MRI scans for breast cancer. Importantly, the dataset includes standardized 3D NIfTI volumes, tumor annotations, voxel-based tumor volumes, and harmonized clinicopathologic metadata such as hormone receptor status, HER2 status, and pathologic complete response outcomes. What makes BreastDCEDL_ISPY2 unique is its deep learning–ready structure and benchmark design. By providing consistent preprocessing, unified annotations, and predefined training/validation/test splits, the dataset enables reproducible research and direct comparison of computational methods. It lowers the technical barriers to working with heterogeneous MRI data, facilitates the development and validation of advanced machine learning models—including transformer-based architectures—and supports clinically relevant investigations into treatment response prediction and personalized therapy planning. The dataset includes extensive non-imaging supporting data:

• Tumor annotations include both segmentation masks and region-of-interest (ROI) delineations.
• Accompanying DICOM metadata encompasses voxel dimensions, signal enhancement ratio (SER) time points, and contrast agent injection timestamps.
• Clinical metadata provides comprehensive patient information, including demographic variables (age, race, menopausal status), hormone receptor (HR) and HER2 receptor status, as well as treatment outcomes, specifically pathologic complete response (pCR).

Methods

Subject Inclusion and Exclusion Criteria

The BreastDCEDL_ISPY2 dataset integrates patient data from the I-SPY2 TRIAL (2010–2016), yielding 985 patients with pretreatment DCE-MRI scans. Inclusion required at least three acquisitions (pre-contrast, early post-contrast, late post-contrast). Patients with incomplete imaging or missing essential metadata were excluded (3 cases), leaving 982 patients. The cohort reflects a clinically diverse population, with a mean age of ~50 years, racial composition (majority White, ~17% Black, others underrepresented), and tumor subtypes spanning HR+/HER2−, HER2+, and triple-negative cancers. pCR status is available for the majority of patients. Treatment histories reflect standardized neoadjuvant chemotherapy protocols. While the dataset includes multicenter acquisitions (22+ institutions), potential biases include predominance of U.S.-based populations, underrepresentation of some ethnic groups, and the trial setting, which may differ from community practice.

Data Acquisition

• MRI Acquisition: Pretreatment 3D DCE-MRI acquired on 1.5T and 3T scanners. Protocols varied across institutions but consistently included pre-contrast, early post-contrast, and late post-contrast acquisitions after gadolinium administration. Key technical parameters (TR, TE, slice thickness, voxel size, FOV) are preserved in metadata.
• Clinical Data: Captured through electronic trial databases. Variables include demographics (age, race, menopausal status), receptor status (HR, HER2), tumor volume, and treatment outcome (pCR).
• Other Data: Signal Enhancement Ratio (SER) maps and voxel-based tumor volumes are provided.
• Missing Data: 3 patients were excluded due to incomplete imaging or metadata.

Data Analysis

• File Format Conversions: Raw DICOM images were converted into standardized 3D NIfTI volumes using a custom pipeline. Conversion preserved 16-bit dynamic range by storing as 64-bit floating-point data.
• Manual Annotation and Segmentation Protocols: Tumor segmentations and ROI delineations provided by I-SPY2 radiologists; converted to binary 3D masks aligned to imaging volumes. Only the primary tumor was annotated if multiple tumors were present.
• Quality Control and Validation: Tumor annotations were reviewed for alignment with MRI volumes. Consistency checks ensured tumor masks aligned across temporal phases. Patients with fewer than three valid acquisitions were excluded.
• Scripts, Code, and Software Versions: Pipelines and Vision Transformer implementation are available on GitHub:https://github.com/naomifridman/BreastDCEDL" rel="nofollow"> https://github.com/naomifridman/BreastDCEDL

Usage Notes

Data Organization and Naming Conventions All imaging data are provided in standardized 3D NIfTI format, converted from original DICOM files while preserving full signal integrity. File names follow the structure:

• Training set: 784 patients (32.1% pCR rate).
• Validation set: 99 patients (32.3% pCR rate).
• Test set: 99 patients (32.3% pCR rate).

Partitioning was stratified by biomarker status (HR, HER2) and pCR outcomes to ensure balanced distributions across subsets. Users are encouraged to adopt these predefined splits when developing predictive models to enable fair comparisons across studies. Clinical Data Files Clinical and pathologic metadata are distributed in standardized TSV format. Variables include demographics (age, race, menopausal status), biomarker status (HR, HER2), tumor volume, and pCR outcomes. TSV files can be opened with standard spreadsheet software (e.g., Microsoft Excel, LibreOffice Calc) or programmatically accessed using Python (pandas) or R. Software Recommendations

• NIfTI images: Compatible with common medical imaging platforms such as 3D Slicer, ITK-SNAP, and FSL. Python users may rely on nibabel for loading and handling imaging volumes.
• Segmentation masks: Provided as binary 3D NIfTI volumes (1 = tumor, 0 = background), directly loadable in the same software.

Potential Sources of Error or Variability

• Inter-cohort heterogeneity: Imaging protocols (field strength, TR/TE, slice thickness) varied across centers, potentially introducing site effects.
• Only the largest lesion was annotated for multifocal disease.
• Population bias (predominantly U.S., underrepresentation of minorities).

External Resources

The source code for converting MRI data from DICOM to NIfTI format, along with usage examples, is available in the project’s GitHub repository:https://github.com/naomifridman/BreastDCEDL" rel="nofollow"> https://github.com/naomifridman/BreastDCEDL.

Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...

Living Identity™ LATAM delivers 379M verified identity and lifestyle profiles across Brazil, Mexico, and Ecuador. This dataset combines core identity fields with geo-coded behavioral, demographic, and affluence signals — enabling precise audience analytics, segmentation, and KYC compliance. Designed for agencies, retailers, and financial institutions expanding across LATAM, the data is privacy-first, updated monthly, and securely hosted on-premise.

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