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!

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.

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

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...

🛒 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...

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.

GapMaps GIS 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.

GIS Data attributes include:

1. 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.

2. 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.

3. 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.

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

5. 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.

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

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

8. 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 GapMaps GIS 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

** Inputs related to Analysis for additional reference:** 1. Why do we need customer Segmentation? As every customer is unique and can be targeted in different ways. The Customer segmentation plays an important role in this case. The segmentation helps to understand profiles of customers and can be helpful in defining cross sell/upsell/activation/acquisition strategies. 2. What is RFM Segmentation? RFM Segmentation is an acronym of recency, frequency and monetary based segmentation. Recency is about when the last order of a customer. It means the number of days since a customer made the last purchase. If it’s a case for a website or an app, this could be interpreted as the last visit day or the last login time. Frequency is about the number of purchases in a given period. It could be 3 months, 6 months or 1 year. So we can understand this value as for how often or how many customers used the product of a company. The bigger the value is, the more engaged the customers are. Alternatively We can define, average duration between two transactions Monetary is the total amount of money a customer spent in that given period. Therefore big spenders will be differentiated with other customers such as MVP or VIP. 3. What is LTV and How to define it? In the current world, almost every retailer promotes its subscription and this is further used to understand the customer lifetime. Retailer can manage these customers in better manner if they know which customer is high life time value. Customer lifetime value (LTV) can also be defined as the monetary value of a customer relationship, based on the present value of the projected future cash flows from the customer relationship. Customer lifetime value is an important concept in that it encourages firms to shift their focus from quarterly profits to the long-term health of their customer relationships. Customer lifetime value is an important metric because it represents an upper limit on spending to acquire new customers. For this reason it is an important element in calculating payback of advertising spent in marketing mix modelling. 4. Why do need to predict Customer Lifetime Value? The LTV is an important building block in campaign design and marketing mix management. Although targeting models can help to identify the right customers to be targeted, LTV analysis can help to quantify the expected outcome of targeting in terms of revenues and profits. The LTV is also important because other major metrics and decision thresholds can be derived from it. For example, the LTV is naturally an upper limit on the spending to acquire a customer, and the sum of the LTVs for all of the customers of a brand, known as the customer equity, is a major metric forbusiness valuations. Similarly to many other problems of marketing analytics and algorithmic marketing, LTV modelling can be approached from descriptive, predictive, and prescriptive perspectives. 5. How Next Purchase Day helps to Retailers? Our objective is to analyse when our customer will purchase products in the future so for such customers we can build strategy and can come up with strategies and marketing campaigns accordingly. a. Group-1: Customers who will purchase in more than 60 days b. Group-2: Customers who will purchase in 30-60 days c. Group-3: Customers who will purchase in 0-30 days 6. What is Cohort Analysis? How it will be helpful? A cohort is a group of users who share a common characteristic that is identified in this report by an Analytics dimension. For example, all users with the same Acquisition Date belong to the same cohort. The Cohort Analysis report lets you isolate and analyze cohort behaviour. Cohort analysis in e-commerce means to monitor your customers’ behaviour based on common traits they share – the first product they bought, when they became customers, etc. - - to find patterns and tailor marketing activities for the group.

Transaction data has been provided for the period of 1st Jan 2019 to 31st Dec 2019. The below data sets have been provided. Online_Sales.csv: This file contains actual orders data (point of Sales data) at transaction level with below variables. CustomerID: Customer unique ID Transaction_ID: Transaction Unique ID Transaction_Date: Date of Transaction Product_SKU: SKU ID – Unique Id for product Product_Description: Product Description Product_Cateogry: Product Category Quantity: Number of items ordered Avg_Price: Price per one quantity Delivery_Charges: Charges for delivery Coupon_Status: Any discount coupon applied Customers_Data.csv: This file contains customer’s demographics. CustomerID: Customer Unique ID Gender: Gender of customer Location: Location of Customer Tenure_Months: Tenure in Months Discount_Coupon.csv: Discount coupons have been given for different categories in different months Month: Discount coupon applied in that month Product_Category: Product categor...

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.

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.

This dataset provides a comprehensive collection of consumer behavior data that can be used for various market research and statistical analyses. It includes information on purchasing patterns, demographics, product preferences, customer satisfaction, and more, making it ideal for market segmentation, predictive modeling, and understanding customer decision-making processes.

The dataset is designed to help researchers, data scientists, and marketers gain insights into consumer purchasing behavior across a wide range of categories. By analyzing this dataset, users can identify key trends, segment customers, and make data-driven decisions to improve product offerings, marketing strategies, and customer engagement.

Key Features: Customer Demographics: Understand age, income, gender, and education level for better segmentation and targeted marketing. Purchase Behavior: Includes purchase amount, frequency, category, and channel preferences to assess spending patterns. Customer Loyalty: Features like brand loyalty, engagement with ads, and loyalty program membership provide insights into long-term customer retention. Product Feedback: Customer ratings and satisfaction levels allow for analysis of product quality and customer sentiment. Decision-Making: Time spent on product research, time to decision, and purchase intent reflect how customers make purchasing decisions. Influences on Purchase: Factors such as social media influence, discount sensitivity, and return rates are included to analyze how external factors affect purchasing behavior.

Columns Overview: Customer_ID: Unique identifier for each customer. Age: Customer's age (integer). Gender: Customer's gender (categorical: Male, Female, Non-binary, Other). Income_Level: Customer's income level (categorical: Low, Middle, High). Marital_Status: Customer's marital status (categorical: Single, Married, Divorced, Widowed). Education_Level: Highest level of education completed (categorical: High School, Bachelor's, Master's, Doctorate). Occupation: Customer's occupation (categorical: Various job titles). Location: Customer's location (city, region, or country). Purchase_Category: Category of purchased products (e.g., Electronics, Clothing, Groceries). Purchase_Amount: Amount spent during the purchase (decimal). Frequency_of_Purchase: Number of purchases made per month (integer). Purchase_Channel: The purchase method (categorical: Online, In-Store, Mixed). Brand_Loyalty: Loyalty to brands (1-5 scale). Product_Rating: Rating given by the customer to a purchased product (1-5 scale). Time_Spent_on_Product_Research: Time spent researching a product (integer, hours or minutes). Social_Media_Influence: Influence of social media on purchasing decision (categorical: High, Medium, Low, None). Discount_Sensitivity: Sensitivity to discounts (categorical: Very Sensitive, Somewhat Sensitive, Not Sensitive). Return_Rate: Percentage of products returned (decimal). Customer_Satisfaction: Overall satisfaction with the purchase (1-10 scale). Engagement_with_Ads: Engagement level with advertisements (categorical: High, Medium, Low, None). Device_Used_for_Shopping: Device used for shopping (categorical: Smartphone, Desktop, Tablet). Payment_Method: Method of payment used for the purchase (categorical: Credit Card, Debit Card, PayPal, Cash, Other). Time_of_Purchase: Timestamp of when the purchase was made (date/time). Discount_Used: Whether the customer used a discount (Boolean: True/False). Customer_Loyalty_Program_Member: Whether the customer is part of a loyalty program (Boolean: True/False). Purchase_Intent: The intent behind the purchase (categorical: Impulsive, Planned, Need-based, Wants-based). Shipping_Preference: Shipping preference (categorical: Standard, Express, No Preference). Payment_Frequency: Frequency of payment (categorical: One-time, Subscription, Installments). Time_to_Decision: Time taken from consideration to actual purchase (in days).

Use Cases: Market Segmentation: Segment customers based on demographics, preferences, and behavior. Predictive Analytics: Use data to predict customer spending habits, loyalty, and product preferences. Customer Profiling: Build detailed profiles of different consumer segments based on purchase behavior, social media influence, and decision-making patterns. Retail and E-commerce Insights: Analyze purchase channels, payment methods, and shipping preferences to optimize marketing and sales strategies.

Target Audience: Data scientists and analysts looking for consumer behavior data. Marketers interested in improving customer segmentation and targeting. Researchers are exploring factors influencing consumer decisions and preferences. Companies aiming to improve customer experience and increase sales through data-driven decisions.

This dataset is available in CSV format for easy integration into data analysis tools and platforms such as Python, R, and Excel.

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.

Dataset Description: E-commerce Customer Behavior

Overview: This dataset provides a comprehensive view of customer behavior within an e-commerce platform. Each entry in the dataset corresponds to a unique customer, offering a detailed breakdown of their interactions and transactions. The information is crafted to facilitate a nuanced analysis of customer preferences, engagement patterns, and satisfaction levels, aiding businesses in making data-driven decisions to enhance the customer experience.

Columns:

1. Customer ID:

   • Type: Numeric
   • Description: A unique identifier assigned to each customer, ensuring distinction across the dataset.

2. Gender:

   • Type: Categorical (Male, Female)
   • Description: Specifies the gender of the customer, allowing for gender-based analytics.

3. Age:

   • Type: Numeric
   • Description: Represents the age of the customer, enabling age-group-specific insights.

4. City:

   • Type: Categorical (City names)
   • Description: Indicates the city of residence for each customer, providing geographic insights.

5. Membership Type:

   • Type: Categorical (Gold, Silver, Bronze)
   • Description: Identifies the type of membership held by the customer, influencing perks and benefits.

6. Total Spend:

   • Type: Numeric
   • Description: Records the total monetary expenditure by the customer on the e-commerce platform.

7. Items Purchased:

   • Type: Numeric
   • Description: Quantifies the total number of items purchased by the customer.

8. Average Rating:

   • Type: Numeric (0 to 5, with decimals)
   • Description: Represents the average rating given by the customer for purchased items, gauging satisfaction.

9. Discount Applied:

   • Type: Boolean (True, False)
   • Description: Indicates whether a discount was applied to the customer's purchase, influencing buying behavior.

10. Days Since Last Purchase:

    • Type: Numeric
    • Description: Reflects the number of days elapsed since the customer's most recent purchase, aiding in retention analysis.

11. Satisfaction Level:

    • Type: Categorical (Satisfied, Neutral, Unsatisfied)
    • Description: Captures the overall satisfaction level of the customer, providing a subjective measure of their experience.

Use Cases:

1. Customer Segmentation:

   • Analyze and categorize customers based on demographics, spending habits, and satisfaction levels.

2. Satisfaction Analysis:

   • Investigate factors influencing customer satisfaction and identify areas for improvement.

3. Promotion Strategy:

   • Assess the impact of discounts on customer spending and tailor promotional strategies accordingly.

4. Retention Strategies:

   • Develop targeted retention strategies by understanding the time gap since the last purchase.

5. City-based Insights:

   • Explore regional variations in customer behavior to optimize marketing efforts based on location-specific trends.

Note: This dataset is synthetically generated for illustrative purposes, and any resemblance to real individuals or scenarios is coincidental.

VisitIQ™ Consumer Data is a robust B2C dataset that empowers businesses to identify and connect with their target audiences effectively. This data set offers an extensive and detailed identity graph, providing you with the tools needed to link, model, and train your AI to understand and reach the right prospect audience for your marketing and sales campaigns.

Key Features of VisitIQ™ Consumer Behavior Data:

Comprehensive Coverage: Includes a wide array of U.S. consumer behavior data, covering millions of contacts and households in the US. This expansive dataset ensures that you have access to the most up-to-date and reliable identity graph available for audience prospecting.

Rich Demographic Data: Understand and identify your prospect audience and customers on a deeper level with linking and modeling B2C data points such as age, gender, income level, education, marital status, occupation, and household size. This granular demographic information allows for more precise segmentation. linking, modeling, and AI training and targeting, helping you to tailor your campaigns to the specific characteristics of your desired audience.

In-Depth Psychographic Data: Go beyond basic demographics with psychographic data that captures consumer interests, lifestyle choices, purchasing behavior, and brand affinities. This information allows for creating highly personalized marketing strategies, tapping into the motivations, preferences, and values that drive consumer decisions.

Enhanced Data Accuracy: The identity graph audience is meticulously collected, verified, and regularly updated to ensure accuracy and relevance. This commitment to data integrity helps to minimize bounce rates, reduce wasted marketing spend, and improve overall campaign performance.

Diverse Use Cases: Whether you're looking to launch a new product, conduct targeted email marketing, run a direct mail campaign, or optimize digital advertising efforts, VisitIQ's™ Consumer Behavior Data can be used across multiple channels to drive more effective marketing and sales efforts.

Customizable Data Solutions: Tailor the dataset to suit your specific business needs. Whether you need highly targeted lists for niche markets or broader segments for mass marketing, the flexibility of VisitIQ's™ data ensures that you can access the most relevant information for your unique objectives.

Compliance and Privacy: VisitIQ™ is committed to maintaining the highest standards of data privacy and compliance. All consumer data is ethically sourced and complies with data protection regulations, giving you peace of mind when using the dataset for your marketing campaigns.

US State Education Analysis for Tech Product Launch

About This Dataset

This comprehensive dataset provides detailed educational attainment and demographic analysis across all 50 US states from 2021-2023, specifically designed for tech companies planning strategic market entry and product launch decisions.

Dataset Overview

• 150 rows of data (50 states × 3 years)
• 17 columns of educational, demographic, and economic indicators
• Complete coverage of all US states from 2021-2023
• Ready-to-analyze format with calculated percentages and rankings

Key Features

🎯 Strategic Market Intelligence

• Educational attainment levels by degree type (Bachelor's, Master's, Professional, Doctoral)
• Calculated education scores and state rankings for quick market prioritization
• Median household income data for purchasing power assessment

📊 Comprehensive Demographics

• Population data for adults 25+ (primary tech consumer demographic)
• Household count data for market sizing
• College graduate percentages for targeted marketing

🔢 Advanced Analytics Ready

• Pre-calculated composite education scores
• State rankings based on education levels
• Percentage breakdowns for immediate insights

Column Definitions

Use Cases

🚀 Tech Product Launches

• Identify states with highest concentrations of educated early adopters
• Prioritize markets based on education levels and income
• Size potential customer segments by state

📈 Market Research & Analysis

• Compare educational demographics across regions
• Analyze trends in educational attainment over time
• Correlate education levels with income potential

🎯 Customer Segmentation

• Target high-value customer segments (graduate degree holders)
• Develop region-specific marketing strategies
• Plan B2B tech sales territories

📊 Business Intelligence

• Regional expansion planning
• Competitive market analysis
• Investment and resource allocation decisions

Data Quality & Sources

• Primary Sources: US Census Bureau American Community Survey (ACS), Bureau of Labor Statistics
• Data Validation: Cross-referenced against multiple official sources
• Calculation Methodology: All percentages and scores calculated using consistent formulas
• Update Frequency: Annual updates as new official data becomes available

Sample Insights

The dataset reveals that Massachusetts consistently ranks #1 in education metrics with: - 47.78% college graduation rate (2023) - 22.42% graduate degree holders - $99,858 median household income - Education score of 28.76

Perfect for identifying premium tech markets and highly-educated consumer bases for sophisticated technology products.

This dataset is ideal for data scientists, market researchers, business analysts, and tech companies looking to make data-driven decisions about market entry, customer targeting, and regional strategy.

Dataset Description

The dataset contains sales transactions captured at various retail stores across the United States during the Black Friday shopping event. It includes a comprehensive set of features that provide insights into customer demographics, product categories, and sales patterns. The dataset is designed to help retailers and e-commerce businesses optimize their sales strategies and maximize profits during this critical shopping period.

Key Features

1. User Information: Includes user demographics such as age, gender, marital status, and occupation.
2. Product Information: Covers product categories, subcategories, and specific product details.
3. Purchase Information: Contains the amount spent by each user for each purchase, in U.S. dollars.
4. Geographical Information: Includes city categories and the number of years the buyer has lived in their city.

Dataset Size

The dataset consists of approximately 550,000 records, providing a robust and representative sample of Black Friday sales data.

Purpose

The primary goal of this dataset is to help retailers and e-commerce businesses predict sales and optimize their pricing strategies to maximize profits during the Black Friday shopping event. This dataset can be used to develop machine learning models that can accurately forecast sales and identify trends in customer behavior.

Potential Use Cases

• Sales Forecasting: Use machine learning algorithms to predict sales based on historical data and optimize pricing strategies.
• Customer Segmentation: Identify and analyze customer demographics and purchasing patterns to tailor marketing campaigns and promotions.
• Product Recommendations: Develop personalized product recommendations based on customer preferences and purchase history.
• Store Optimization: Analyze sales data to optimize store layouts, product placement, and inventory management.

Detailed Description:

This dataset records detailed information about ticket sales and customer behavior at a cinema hall, offering insights into various aspects such as demographics, movie genre preferences, seat selection, ticket pricing, and customer retention patterns. It is designed to help analyze customer engagement, spending behavior, and factors that influence repeat visits to the cinema. The data is useful for predictive modeling and can support decision-making processes related to customer retention, marketing strategies, and optimizing cinema operations.

Columns Overview:

1. Ticket_ID (Categorical):

   • Description: A unique alphanumeric identifier for each ticket purchase. The ID consists of a random uppercase letter (A-Z) followed by a 4-digit number (e.g., B7539, Y1344).
   • Significance: This column helps identify each individual transaction. It's a categorical variable, essential for tracking specific customer purchases but not related to other variables directly.

2. Age (Numerical):

   • Description: The age of the customer who purchased the ticket, ranging between 18 and 60 years.
   • Significance: Age is an important demographic feature, providing insights into customer segments. For example, younger audiences might prefer different movie genres or seating types compared to older customers. Analyzing age data can help cinema halls cater to the needs of various age groups.

3. Ticket_Price (Numerical):

   • Description: The price the customer paid for the ticket, typically ranging from $10 to $25. The price varies based on factors like movie time, seat type, or cinema location.
   • Significance: Ticket price reflects customer spending and the cinema's pricing strategy. Understanding how ticket pricing impacts customer behavior can help optimize ticket sales and maximize revenue.

4. Movie_Genre (Categorical):

   • Description: The genre of the movie the customer attended, which can include one of the following: Action, Comedy, Horror, Drama, or Sci-Fi.
   • Significance: Genre preferences are crucial for understanding customer interests. Analyzing which genres are most popular can guide movie scheduling, marketing strategies, and even help in curating personalized recommendations for customers.

5. Seat_Type (Ordinal):

   • Description: The type of seat selected by the customer, with three ordinal categories:
     • Standard (Basic seating option)
     • Premium (Enhanced seating with added comfort)
     • VIP (Exclusive seating, offering premium features like extra legroom and priority service)
   • Significance: Seat type provides insights into customer spending behavior. Premium and VIP seat types typically correlate with higher ticket prices, and understanding seat preferences can help in optimizing cinema layout and pricing strategies. Additionally, this column can be used to gauge the popularity of high-end seating options.

6. Number_of_Person (Mixed Variable):

   • Description: The number of people accompanying the customer. This can either be:
     • Alone: The customer attended alone.
     • 2–7: The customer attended with a group of 2 to 7 people.
   • Significance: Group size is an important factor in understanding customer preferences and behavior. For example, groups might purchase more tickets or opt for different movie genres and seat types than solo attendees. This column is crucial for analyzing social dynamics and group behavior in cinema attendance.

7. Purchase_Again (Target - Binary):

   • Description: A binary target variable indicating whether the customer is likely to return and purchase another ticket. It has two possible values:
     • Yes: The customer is likely to return for another movie.
     • No: The customer is not likely to return.
   • Significance: This is the key column for predictive modeling. It is used to assess customer retention and predict the likelihood of future ticket purchases. Analyzing the factors that influence repeat purchases (e.g., age, genre preferences, seat types) helps cinema halls optimize marketing and customer engagement strategies.

Use Cases & Applications:

1. Customer Segmentation:
   By analyzing variables like Age, Movie_Genre, and Seat_Type, cinema halls can identify different customer segments. For example, young customers may prefer Action or Comedy genres, while older customers may prefer Drama or Sci-Fi. This segmentation can guide personalized marketing campaigns, ticket discounts, and loyalty programs.

2. Customer Retention Analysis:
   The Purchase_Again column is crucial for assessing customer loyalty. By correlating it with other factors like **Ticket_Price...

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...

During a 2024 survey among marketers worldwide, around 86 percent reported using Facebook for marketing purposes. Instagram and LinkedIn followed, respectively mentioned by 79 and 65 percent of the respondents.

              The global social media marketing segment

              According to the same study, 59 percent of responding marketers intended to increase their organic use of YouTube for marketing purposes throughout that year. LinkedIn and Instagram followed with similar shares, rounding up the top three social media platforms attracting a planned growth in organic use among global marketers in 2024. Their main driver is increasing brand exposure and traffic, which led the ranking of benefits of social media marketing worldwide.

              Social media for B2B marketing

              Social media platform adoption rates among business-to-consumer (B2C) and business-to-business (B2B) marketers vary according to each subsegment's focus. While B2C professionals prioritize Facebook and Instagram – both run by Meta, Inc. – due to their popularity among online audiences, B2B marketers concentrate their endeavors on Microsoft-owned LinkedIn due to its goal to connect people and companies in a corporate context.