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.

1) Data Introduction • The Retail Sales Dataset is data designed to analyze retail sales and customer behavior in a virtual retail environment, including transaction history, customer demographics, and product information.

2) Data Utilization (1) Retail Sales Dataset has characteristics that: • This dataset details retail sales and customer characteristics such as transaction ID, date, customer ID, gender, age, product category, purchase volume, unit price, total amount. (2) Retail Sales Dataset can be used to: • Customer Segmentation and Marketing Strategy: By analyzing purchase patterns by age, gender, and product category, you can use them to establish a customized marketing strategy. • Sales Trends and Inventory Management: It can be used to streamline retail operations such as inventory management and promotion planning by analyzing sales trends by period and product.

🛒 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...

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

Overview

This dataset contains 1,000 retail transaction records after cleaning and preprocessing.

This synthetic dataset has been meticulously crafted to simulate a dynamic retail environment, providing an ideal playground for those eager to sharpen their data analysis skills through exploratory data analysis (EDA). With a focus on retail sales and customer characteristics, this dataset invites you to unravel intricate patterns, draw insights, and gain a deeper understanding of customer behaviour.

It includes customer demographics, product categories, transaction details, and derived analytics, such as the daily percentage change in sales.

Original dataset (Uncleaned):- https://www.kaggle.com/datasets/mohammadtalib786/retail-sales-dataset

The dataset can be used for:

• Sales trend analysis
• Customer segmentation
• Revenue forecasting
• Data visualisation projects
• Teaching SQL, Pandas, or AWS analytics pipelines

File Information

• Filename: cleaned_retail_sales_dataset.csv
• Records (rows): 1,000
• Columns (features): 10
• Missing values: Minimal (only 1 missing in Daily Percent Change)

Column Descriptions

• Transaction ID – Unique identifier for each transaction (range: 1–1000).
• Date – Purchase date in DD-MM-YYYY format (345 unique dates).
• Customer ID – Unique identifier for each customer (1,000 unique customers).
• Gender – Customer gender: Male / Female (~51% Female, ~49% Male).
• Age – Customer’s age (range: 18–64, average ≈ 41 years).
• Product Category – Purchased product category (Clothing, Electronics, Groceries).
• Quantity – Number of items purchased per transaction (range: 1–4, average ≈ 2.5).
• Price per Unit – Price of a single item (range: ₹25 – ₹500, average ≈ ₹180).
• Total Amount – Transaction value = Quantity × Price per Unit (range: ₹25 – ₹2000, average ≈ ₹456).
• Daily Percent Change – Day-over-day percentage change in sales (range: -98.75% to 7900%).

Features

• Transaction ID: Unique identifier for each transaction.
• Date: Purchase date in DD-MM-YYYY format.
• Customer ID: Unique identifier for each customer.
• Gender: Customer gender (Male / Female).
• Age: Customer’s age.
• Product Category: Purchased product category (Clothing, Electronics, Groceries).
• Quantity: Number of items purchased in the transaction.
• Price per Unit: Price of a single item.
• Total Amount: Transaction value (Quantity × Price per Unit).
• Daily Percent Change: Day-over-day percentage change in sales.

**💬 Feedback & Suggestions ** If you find this dataset helpful for your research or projects, feel free to upvote and share your feedback or suggestions. Your support is appreciated — thank you! 😉

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

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

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

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

Unlike traditional solutions, you don’t need to know and disclose your customer or prospect addresses, emails or other sensitive information. You can provide even masked last names keeping personal data in-house. This makes Demografy privacy by design and enables you to get 100% coverage of your audience since all you need to know is names.

Success.ai’s Retail Data for the Retail Sector in North America offers a comprehensive dataset designed to connect businesses with key players across the diverse retail industry. Covering everything from department stores and supermarkets to specialty shops and e-commerce platforms, this dataset provides verified contact details, business locations, and leadership profiles for retail companies in the United States, Canada, and Mexico.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, marketing, and business development efforts are powered by accurate, continuously updated, and AI-validated data.

Backed by our Best Price Guarantee, this solution empowers businesses to thrive in North America’s competitive retail landscape.

Why Choose Success.ai’s Retail Data for North America?

1. Verified Contact Data for Precision Outreach

   • Access verified phone numbers, work emails, and LinkedIn profiles of retail executives, store managers, and decision-makers.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and efficient campaign execution.

2. Comprehensive Coverage Across Retail Segments

   • Includes profiles of retail businesses across major markets, from large department stores and grocery chains to boutique retailers and online platforms.
   • Gain insights into the operational dynamics of retail hubs in cities such as New York, Los Angeles, Toronto, and Mexico City.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, new store openings, market expansions, and shifts in consumer preferences.
   • Stay aligned with evolving industry trends and emerging opportunities in the North American retail sector.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other privacy regulations, ensuring responsible and lawful use of data in your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with executives, marketing directors, and operations managers across the North American retail sector.
• 30M Company Profiles: Access firmographic data, including revenue ranges, store counts, and geographic footprints.
• Store Location Data: Pinpoint retail outlets, regional offices, and distribution centers to refine supply chain and marketing strategies.
• Leadership Contact Details: Connect with CEOs, CMOs, and procurement officers influencing retail operations and vendor selections.

Key Features of the Dataset:

1. Retail Decision-Maker Profiles

   • Identify and engage with store owners, category managers, and marketing directors shaping customer experiences and product strategies.
   • Target professionals responsible for inventory planning, vendor contracts, and store performance.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (luxury, grocery, e-commerce), geographic location, company size, or revenue range.
   • Tailor outreach to align with regional market trends, customer demographics, and operational priorities.

3. Market Trends and Operational Insights

   • Analyze trends such as online shopping growth, sustainability practices, and supply chain optimization.
   • Leverage insights to refine product offerings, identify partnership opportunities, and design effective campaigns.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and enhance engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Present products, services, or technology solutions to retail procurement teams, marketing departments, and operations managers.
   • Build relationships with retailers seeking innovative tools, efficient supply chain solutions, or unique product offerings.

2. Market Research and Consumer Insights

   • Analyze retail trends, customer behaviors, and seasonal demands to inform marketing strategies and product launches.
   • Benchmark against competitors to identify gaps, emerging niches, and growth opportunities.

3. E-Commerce and Digital Strategy Development

   • Target e-commerce managers and digital transformation teams driving online retail initiatives and omnichannel integration.
   • Offer solutions to enhance online shopping experiences, logistics, and customer loyalty programs.

4. Recruitment and Workforce Solutions

   • Engage HR professionals and hiring managers in recruiting talent for store operations, customer service, or marketing roles.
   • Provide workforce optimization tools, training platforms, or staffing services tailored to retail environments.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality retail data at competitive prices, ensuring strong ROI for your marketing and outreach efforts in North America.

2. Seamless Integration
   ...

Description:

This dataset provides insights into consumer electronics sales, featuring product categories, brands, prices, customer demographics, purchase behavior, and satisfaction metrics. It aims to analyze factors influencing purchase intent and customer satisfaction in the consumer electronics market.

Features:

• ProductID: Unique identifier for each product.
• ProductCategory: Category of the consumer electronics product (e.g., Smartphones, Laptops).
• ProductBrand: Brand of the product (e.g., Apple, Samsung).
• ProductPrice: Price of the product ($).
• CustomerAge: Age of the customer.
• CustomerGender: Gender of the customer (0 - Male, 1 - Female).
• PurchaseFrequency: Average number of purchases per year.
• CustomerSatisfaction: Customer satisfaction rating (1 - 5).
• PurchaseIntent (Target Variable): Intent to purchase.

Conclusion:

This dataset facilitates analysis on consumer behavior and purchase patterns in the consumer electronics sector, aiding insights into market dynamics and customer preferences.

Dataset Usage and Attribution Notice

This dataset, shared by Rabie El Kharoua, is original and has never been shared before. It is made available under the CC BY 4.0 license, allowing anyone to use the dataset in any form as long as proper citation is given to the author. A DOI is provided for proper referencing. Please note that duplication of this work within Kaggle is not permitted.

Exclusive Synthetic Dataset

This dataset is synthetic and was generated for educational purposes, making it ideal for data science and machine learning projects. It is an original dataset, owned by Mr. Rabie El Kharoua, and has not been previously shared. You are free to use it under the license outlined on the data card. The dataset is offered without any guarantees. Details about the data provider will be shared soon.

Success.ai’s Consumer Behavior Data for Consumer Goods & Electronics Industry Leaders in Asia, the US, and Europe offers a robust dataset designed to empower businesses with actionable insights into global consumer trends and professional profiles. Covering executives, product managers, marketers, and other professionals in the consumer goods and electronics sectors, this dataset includes verified contact information, professional histories, and geographic business data.

With access to over 700 million verified global profiles and firmographic data from leading companies, Success.ai ensures your outreach, market analysis, and strategic planning efforts are powered by accurate, continuously updated, and GDPR-compliant data. Backed by our Best Price Guarantee, this solution is ideal for businesses aiming to navigate and lead in these fast-paced industries.

Why Choose Success.ai’s Consumer Behavior Data?

1. Verified Contact Data for Precision Engagement

   • Access verified email addresses, phone numbers, and LinkedIn profiles of professionals in the consumer goods and electronics industries.
   • AI-driven validation ensures 99% accuracy, optimizing communication efficiency and minimizing data gaps.

2. Comprehensive Global Coverage

   • Includes profiles from key markets in Asia, the US, and Europe, covering regions such as China, India, Germany, and the United States.
   • Gain insights into region-specific consumer trends, product preferences, and purchasing behaviors.

3. Continuously Updated Datasets

   • Real-time updates capture career progressions, company expansions, market shifts, and consumer trend data.
   • Stay aligned with evolving market dynamics and seize emerging opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible use and legal compliance for all data-driven campaigns.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with industry leaders, marketers, and decision-makers in consumer goods and electronics industries worldwide.
• Consumer Trend Insights: Gain detailed insights into product preferences, purchasing patterns, and demographic influences.
• Business Locations: Access geographic data to identify regional markets, operational hubs, and emerging consumer bases.
• Professional Histories: Understand career trajectories, skills, and expertise of professionals driving innovation and strategy.

Key Features of the Dataset:

1. Decision-Maker Profiles in Consumer Goods and Electronics

   • Identify and engage with professionals responsible for product development, marketing strategy, and supply chain optimization.
   • Target individuals making decisions on consumer engagement, distribution, and market entry strategies.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (consumer electronics, FMCG, luxury goods), geographic location, or job function.
   • Tailor campaigns to align with specific industry trends, market demands, and regional preferences.

3. Consumer Trend Data and Insights

   • Access data on regional product preferences, spending behaviors, and purchasing influences across key global markets.
   • Leverage these insights to shape product development, marketing campaigns, and customer engagement strategies.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing and Demand Generation

   • Design campaigns tailored to consumer preferences, regional trends, and target demographics in the consumer goods and electronics industries.
   • Leverage verified contact data for multi-channel outreach, including email, social media, and direct marketing.

2. Market Research and Competitive Analysis

   • Analyze global consumer trends, spending patterns, and product preferences to refine your product portfolio and market positioning.
   • Benchmark against competitors to identify gaps, emerging needs, and growth opportunities in target regions.

3. Sales and Partnership Development

   • Build relationships with key decision-makers at companies specializing in consumer goods or electronics manufacturing and distribution.
   • Present innovative solutions, supply chain partnerships, or co-marketing opportunities to grow your market share.

4. Product Development and Innovation

   • Utilize consumer trend insights to inform product design, pricing strategies, and feature prioritization.
   • Develop offerings that align with regional preferences and purchasing behaviors to maximize market impact.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality consumer behavior data at competitive prices, ensuring maximum ROI for your outreach, research, and ma...

Customer Cohorts for Feature Releases Market Outlook

According to our latest research, the global market size for Customer Cohorts for Feature Releases reached USD 2.18 billion in 2024, demonstrating robust momentum driven by digital transformation initiatives across industries. The market is projected to expand at a CAGR of 13.7% from 2025 to 2033, reaching a forecasted value of USD 6.67 billion by 2033. This growth is underpinned by the increasing adoption of data-driven decision-making, the need for personalized user experiences, and the growing complexity of software product ecosystems. As organizations strive to deliver targeted features and maximize user engagement, the demand for sophisticated customer cohort analysis tools is surging, setting the stage for sustained market expansion.

One of the primary growth drivers for the Customer Cohorts for Feature Releases market is the accelerating shift towards personalized digital experiences. Enterprises are increasingly leveraging cohort analysis to segment their user base by behavioral, demographic, and technographic parameters, enabling them to roll out features tailored to specific user groups. This targeted approach not only enhances user satisfaction but also drives higher adoption rates for new features, reducing churn and increasing customer lifetime value. The proliferation of big data analytics and artificial intelligence has further amplified the capabilities of cohort analysis, allowing businesses to uncover deep insights into user behavior and preferences, thereby optimizing feature release strategies.

Another significant factor fueling market growth is the rising complexity of digital products and the need for agile product management. As software products evolve, organizations must continuously release new features to stay competitive. Customer cohort analysis empowers product teams to test, validate, and optimize features within specific user segments before broader deployment. This minimizes the risk of negative user experiences and ensures that resources are allocated efficiently. Additionally, the integration of cohort analysis with DevOps pipelines and continuous delivery frameworks is streamlining the feature release process, enabling faster and more reliable product updates.

The surge in regulatory compliance requirements and data privacy concerns is also shaping the market landscape. Organizations are under increasing pressure to ensure that feature releases comply with regional data protection laws, such as GDPR and CCPA. Cohort analysis tools equipped with robust privacy controls allow businesses to segment users based on consent and compliance status, mitigating legal risks and enhancing trust. Furthermore, the adoption of cloud-based cohort analysis solutions is facilitating seamless data integration and real-time analytics, empowering global teams to collaborate effectively and make informed decisions.

From a regional perspective, North America remains the dominant market for Customer Cohorts for Feature Releases, accounting for the largest revenue share in 2024. This leadership is attributed to the high concentration of technology-driven enterprises, advanced analytics infrastructure, and a strong culture of innovation. However, the Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, expanding e-commerce, and increasing investments in customer experience technologies. Europe is also experiencing steady adoption, particularly in sectors such as BFSI, healthcare, and retail, where regulatory compliance and user privacy are paramount. Latin America and the Middle East & Africa are emerging markets, gradually embracing cohort analysis as digital transformation initiatives gain traction.

Cohort Type Analysis

The cohort type segment, which includes behavioral, demographic, geographic, technographic, and other cohort classifications, plays a pivotal role in shaping the Customer Cohorts for Feature Releases market. Behavioral cohorts, in particular, have gained significant traction as organizations seek to understand user actions, engagement patterns, and feature adoption rates. By segmenting users based on in-app behaviors or transaction histories, companies can identify high-value users, track feature utilization, and tailor future releases to maximize impact. Behavioral cohort analysis is increasingly powered by machine learning algorithms, enabling predictive analytics and proactive feature optimization.

Success.ai’s Consumer Marketing Data for Food, Beverage & Consumer Goods Professionals Globally provides a comprehensive dataset tailored for businesses seeking to connect with decision-makers and marketing professionals in these dynamic industries. Covering roles such as brand managers, marketing strategists, and product developers, this dataset offers verified contact details, decision-maker insights, and actionable business data.

With access to over 700 million verified global profiles, Success.ai ensures your marketing, sales, and research efforts are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is essential for businesses aiming to lead in the food, beverage, and consumer goods sectors.

Why Choose Success.ai’s Consumer Marketing Data?

1. Verified Contact Data for Precision Targeting

   • Access verified work emails, phone numbers, and LinkedIn profiles of marketing professionals, brand leaders, and product strategists.
   • AI-driven validation ensures 99% accuracy, minimizing communication errors and maximizing outreach success.

2. Comprehensive Coverage Across Global Markets

   • Includes profiles of professionals from food and beverage companies, consumer goods manufacturers, and marketing agencies in key markets worldwide.
   • Gain insights into regional trends in product marketing, consumer engagement, and purchasing behaviors.

3. Continuously Updated Datasets

   • Real-time updates capture changes in professional roles, company strategies, and market trends.
   • Stay aligned with the fast-evolving consumer goods industry to identify emerging opportunities.

4. Ethical and Compliant

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

Data Highlights:

• 700M+ Verified Global Profiles: Connect with decision-makers, marketers, and product managers in the food, beverage, and consumer goods sectors worldwide.
• Leadership Insights: Gain detailed profiles of brand managers, marketing executives, and product developers shaping consumer trends.
• Contact Details: Access verified phone numbers and work emails for precision outreach.
• Industry Trends: Understand global marketing trends, regional consumer preferences, and market dynamics.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with key professionals managing brand strategies, product launches, and marketing campaigns in the food, beverage, and consumer goods industries.
   • Access data on career histories, certifications, and market expertise for targeted outreach.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (snack foods, beverages, household goods), geographic location, or job function.
   • Tailor campaigns to align with specific needs such as product placement, consumer engagement, or regional expansion.

3. Regional Trends and Consumer Insights

   • Leverage data on consumer preferences, product demand, and spending patterns in key markets.
   • Use these insights to refine product offerings, marketing strategies, and audience targeting.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Brand Outreach

   • Design targeted campaigns for food, beverage, and consumer goods products based on verified data and consumer insights.
   • Leverage multi-channel outreach, including email, phone, and digital advertising, to maximize engagement.

2. Product Development and Launch Strategies

   • Utilize consumer trend data to guide product development and market entry strategies.
   • Collaborate with brand managers and marketing professionals to align offerings with consumer preferences.

3. Sales and Partnership Development

   • Build relationships with distributors, retailers, and marketers in the consumer goods supply chain.
   • Present co-branding opportunities, joint marketing campaigns, or distribution strategies to decision-makers.

4. Market Research and Competitive Analysis

   • Analyze global trends in consumer goods marketing, product innovations, and purchasing behaviors to refine strategies.
   • Benchmark against competitors to identify growth opportunities, underserved markets, and high-demand products.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality consumer marketing data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified data into CRM systems, marketing platforms, or analytics tools via APIs or downloadable formats, streamlining workflows and enhancing productivity.

3. Data Acc...

1) Data Introduction • The Consumer Behavior and Shopping Habits Dataset is a tabular collection of customer demographics, purchase history, product preferences, shopping frequency, and online and offline purchasing behavior.

2) Data Utilization (1) Consumer Behavior and Shopping Habits Dataset has characteristics that: • Each row contains detailed consumer and transaction information such as customer ID, age, gender, purchased goods and categories, purchase amount, region, product attributes (size, color, season), review rating, subscription status, delivery method, discount/promotion usage, payment method, purchase frequency, etc. • Data is organized to cover a variety of variables and purchasing patterns to help segment customers, establish marketing strategies, analyze product preferences, and more. (2) Consumer Behavior and Shopping Habits Dataset can be used to: • Customer Segmentation and Target Marketing: You can analyze demographics and purchasing patterns to define different customer groups and use them to develop customized marketing strategies. • Product and service improvement: Based on purchase history, review ratings, discount/promotional responses, etc., it can be applied to product and service improvements such as identifying popular products, managing inventory, and analyzing promotion effects.

Dataset Description – Retail Sales & Customer Insights

This dataset simulates a retail business environment, capturing customer purchases across multiple stores and product categories. It is designed for practising data modelling, DAX calculations, and dashboard creation in Power BI. The dataset is realistic enough to demonstrate business insights such as sales performance, customer demographics, product profitability, and store comparisons.

Business Questions You Can Answer

• Which products and categories drive the most sales and profit?
• What is the trend of sales over time (monthly/quarterly/yearly)?
• Which stores or regions perform best?
• How do customer demographics (age, gender, city) influence sales?
• What is the average order value and customer lifetime value?
• How do discounts affect revenue?

Key Features

• 4 Tables with realistic business relationships (star schema).
• 5,000+ transactions for meaningful trend analysis.
• Rich enough to showcase calculated columns (Age, Tenure, Profit) and measures (Sales, Profit, AOV, YOY Growth).
• Suitable for building interactive dashboards in Power BI.

A global database of Census 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 census data with population trends for real estate, market research, audience targeting, and sales territory mapping.

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

Use cases for the Global Census Database (Consumer Demographic Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Real Estate Data Estimations

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Census data export methodology

Our consumer demographic data packages are offered in CSV format. All Demographic 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 demographic 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.

Dataset Descriptions This analysis involves three main datasets—Sales Data, Customer Data, and Shopping Mall Data—which provide information on transactions, customer demographics, and shopping mall characteristics. Each dataset contributes unique aspects that, when combined, offer valuable insights into sales patterns, customer behavior, and the impact of mall features on sales.

Sales Data: This dataset records transaction-level details for products sold across shopping malls. Key columns include:

invoice_no: Unique identifier for each transaction. customer_id: Identifier for the customer making the purchase. category: Product category (e.g., Clothing, Shoes). quantity: Quantity of each product purchased. invoice date: Date of transaction. price: Price of each product purchased. shopping_mall: Mall where the transaction took place. Purpose: Analyzing this dataset allows us to understand product sales across different malls and track how sales change over time or by category.

Customer Data: This dataset provides demographic details for each customer, including:

customer_id: Unique identifier for each customer. gender: Customer’s gender. age: Customer’s age. payment_method: Preferred payment method for transactions. Purpose: This dataset supports customer segmentation by demographics, such as age and gender, and helps identify spending patterns and payment preferences.

Shopping Mall Data: This dataset contains details of various shopping malls in California where the transactions occur. The columns include:

shopping_mall: Name of the mall. construction_year: Year the mall was established. area_sqm: Total area of the mall in square meters. location: City in California where the mall is located. stores_count: Number of stores within the mall. Purpose: This dataset provides context on mall attributes and enables analysis of how mall features—such as size, store count, and location—might influence customer traffic, sales, and purchasing behaviors.

Goal of Analysis The goal of analyzing this data is to uncover patterns and insights that can inform decisions for optimizing sales strategies, enhancing customer engagement, and understanding the effects of mall characteristics on customer behavior. By exploring connections among sales performance, customer demographics, and mall attributes, this analysis seeks to answer questions like:

Which mall characteristics (e.g., size, age, store count) are most strongly associated with higher sales volumes? How do customer demographics, such as age and gender, impact spending patterns across malls? What product categories are more popular in specific malls, and how does this vary with mall characteristics?

Expected Outcomes With this analysis, we aim to develop actionable insights into the sales dynamics in California's shopping malls, identify customer preferences by mall characteristics, and understand how mall attributes drive retail success. These insights can be valuable for mall operators, retailers, and marketing teams looking to improve customer experience, tailor product offerings, and maximize sales performance across different mall locations.

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.

Success.ai’s Consumer Sentiment Data offers businesses unparalleled insights into global audience attitudes, preferences, and emotional triggers. Sourced from continuous analysis of consumer behaviors, conversations, and feedback, this dataset includes psychographic profiles, interest data, and sentiment trends that help marketers, product teams, and strategists better understand their target customers. Whether you’re exploring a new market, refining your brand message, or enhancing product offerings, Success.ai ensures your consumer intelligence efforts are guided by timely, accurate, and context-rich data.

Why Choose Success.ai’s Consumer Sentiment Data?

1. Comprehensive Audience Insights

   • Access psychographic and interest-based profiles that reveal what motivates and influences your audience’s decisions.
   • Continuous updates ensure you stay aligned with shifting consumer sentiments, seasonal preferences, and emerging trends.

2. Global Reach Across Industries and Demographics

   • Includes insights from various markets, age groups, cultural backgrounds, and income levels.
   • Identify consumer attitudes in different regions, helping you tailor campaigns, products, and messaging to diverse audiences.

3. Continuously Updated Datasets

   • Real-time data analysis ensures that your consumer sentiment insights remain fresh, relevant, and actionable.
   • Adapt quickly to consumer feedback, market changes, and competitive pressures.

4. Ethical and Compliant

   • Adheres to global data privacy regulations, ensuring your usage of consumer sentiment data is both legal and respectful of personal boundaries.

Data Highlights:

• Psychographic Profiles: Understand lifestyle preferences, values, and interests that shape consumer choices.
• Sentiment Trends: Track evolving emotional responses to brands, products, and categories.
• Global Audience Insights: Evaluate consumer sentiments across multiple regions, languages, and cultural contexts.
• Continuous Updates: Receive current data that reflects the latest shifts in mood, opinion, and interest.

Key Features of the Dataset:

1. Granular Segmentation

   • Segment audiences by demographic, interest, buying behavior, and sentiment scores for targeted marketing efforts.
   • Focus on the attributes that matter most, from eco-conscious consumers to luxury shoppers or value seekers.

2. Contextual Sentiment Analysis

   • Go beyond basic positive/negative sentiment to understand nuanced emotional responses.
   • Identify triggers that inspire loyalty, dissatisfaction, trust, or skepticism.

3. AI-Driven Enrichment

   • Profiles enriched with actionable data provide deeper insights into consumer lifestyles, brand perceptions, and product affinities.
   • Leverage advanced analytics to develop personalized campaigns and product strategies.

Strategic Use Cases:

1. Marketing and Campaign Optimization

   • Craft campaigns that resonate emotionally by understanding what drives consumer engagement.
   • Adjust messaging, timing, and channels to align with evolving sentiment trends and seasonal shifts in consumer mood.

2. Product Development and Innovation

   • Identify unmet consumer needs and preferences before launching new products.
   • Refine features, packaging, and pricing strategies based on real-time consumer responses.

3. Brand Management and Positioning

   • Monitor brand perceptions to detect early signs of brand fatigue, trust erosion, or negative publicity.
   • Strengthen brand loyalty by addressing concerns, highlighting strengths, and adapting to changing market contexts.

4. Competitive Analysis and Market Entry

   • Benchmark consumer sentiment towards competitors, industry leaders, and emerging disruptors.
   • Assess market readiness and optimize entry strategies for new regions or segments.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access high-quality, verified data at competitive prices, ensuring efficient allocation of your marketing and research budgets.

2. Seamless Integration

   • Integrate enriched sentiment data into your analytics, CRM, or marketing platforms via APIs or downloadable formats.
   • Simplify data management and accelerate decision-making processes.

3. Data Accuracy with AI Validation

   • Benefit from AI-driven validation for reliable insights into consumer attitudes, leading to more confident data-driven strategies.

4. Customizable and Scalable Solutions

   • Tailor datasets to focus on specific segments, regions, or interests, and scale as your business grows and evolves.

APIs for Enhanced Functionality:

1. Data Enrichment API

   • Enhance your existing consumer records with psychographic and sentiment insights, deepening your understanding of audience motivations.

2. Lead Generation API

   • Identify audience segments receptive to your messaging, streamlini...

Success.ai’s User Profiles Data API provides on-demand access to a vast database of over 700 million verified consumer profiles worldwide. Designed to help you gain deeper insights into consumer behaviors, preferences, and interests, this API enables personalized interactions, improves retention strategies, and strengthens engagement efforts.

With continuously updated, AI-validated data, you can confidently evolve your marketing, product development, and customer experience initiatives to meet dynamic consumer needs. Backed by our Best Price Guarantee, Success.ai’s User Profiles Data API ensures you always have the intelligence required to build meaningful relationships with your audience and stay ahead in a competitive global marketplace.

Why Choose Success.ai’s User Profiles Data API?

1. Extensive Global Consumer Coverage

   • Access consumer profiles from diverse regions, demographics, and lifestyles, enabling truly global engagement strategies.
   • Expand into new markets with confidence, supported by comprehensive, real-time consumer insights.

2. AI-Validated Accuracy

   • Rely on 99% data accuracy through AI-driven validation, ensuring you base decisions on current and reliable consumer information.
   • Reduce guesswork and focus on reaching the right audience with the right message.

3. Continuous Real-Time Updates

   • Receive ongoing data refreshes that reflect evolving consumer interests, behaviors, and purchasing patterns.
   • Quickly adapt campaigns, products, and brand strategies to align with shifting consumer sentiments.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy standards, guaranteeing responsible, lawful use of consumer data in all applications.

Data Highlights:

• 700M+ Verified Consumer Profiles: Gain insights into a massive and diverse user base for more informed targeting.
• Behavioral and Demographic Indicators: Leverage comprehensive data points to segment audiences and refine content strategies.
• Real-Time Updates: Keep your CRM and marketing platforms enriched with fresh data, ensuring long-term relevancy.
• Best Price Guarantee: Achieve exceptional ROI on data-driven initiatives with top-tier insights at industry-leading prices.

Key Features of the User Profiles Data API:

1. On-Demand Data Enrichment

   • Seamlessly integrate the API into CRM systems or marketing automation tools to instantly enrich user records.
   • Eliminate data decay and manual imports, freeing up resources for strategy and execution.

2. Advanced Filtering and Segmentation

   • Query the API using criteria like geography, age, interests, or purchasing habits.
   • Deliver personalized offers and experiences that resonate with individual preferences, boosting loyalty and conversion rates.

3. Real-Time Validation and Reliability

   • AI-driven validation ensures your targeting is always supported by the most accurate, up-to-date information.
   • Make data-driven decisions with confidence, reducing wasted effort on irrelevant audiences.

4. Flexible Integration and Scalability

   • Adjust parameters as market conditions evolve, ensuring the data always aligns with current goals.
   • Scale your targeting efforts from local campaigns to global reach without compromising on data quality.

Strategic Use Cases:

1. Personalized Marketing Campaigns

   • Use demographic, behavioral, and interest data to craft highly relevant messaging and content.
   • Improve open rates, click-throughs, and conversions by consistently delivering value to users.

2. Customer Retention and Loyalty Programs

   • Identify patterns in user behavior to anticipate churn risks and reward loyalty.
   • Enhance engagement and long-term customer value by offering timely, personalized incentives.

3. Product Development and Innovation

   • Tailor product features and updates based on real-time user insights, ensuring alignment with consumer desires.
   • Accelerate product-market fit and maintain competitiveness by responding quickly to emerging trends.

4. Market Entry and Expansion

   • Understand consumer preferences in new regions or segments, guiding go-to-market strategies and messaging.
   • Reduce risks and increase success rates when entering unfamiliar territories backed by data-driven insights.

Why Choose Success.ai?

1. Best Price Guarantee

   • Secure top-quality consumer data at unmatched prices, maximizing ROI on outreach, product development, and retention strategies.

2. Seamless Integration

   • Incorporate the API into existing workflows with ease, boosting productivity and eliminating manual data maintenance.

3. Data Accuracy with AI Validation

   • Count on 99% accuracy to inform data-driven tactics, improve engagement, and elevate brand experiences.

4. Customizable and Scalable Solutions

   • Tailor datasets, filters, and se...