Analyzing Customer Spending Habits to Improve Sales Performance

A Cross-Country Study

By Vineet Bahl [source]

About this dataset

This Sales Data dataset offers a unique insight into the spending habits of customers from various countries across the globe. With detailed information on customer age, gender, product category, quantity, unit cost and price, as well as revenue generated through sales of products listed in this dataset, you can explore and discover patterns in consumer behavior. Analyze shifts in consumer trends with qualitative data like customer age and gender to know what drives customers’ decisions when shopping online or offline. Compare different markets to analyze pricing strategies for new product launches or promotional campaigns. Also with this dataset you can gain valuable insights about the changes in consumer demand for specific products over time – find out which Products had better margin or however see how different promotions impacted overall sales performance from different categories and sub-categories! Analyzing consumer behavior is key to success when it comes to commerce business models so this Sales Data offers powerful ways into understanding your customer base better!

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How to use the dataset

This dataset presents a great opportunity to actively analyze customer spending habits on products and services to improve sales performance. The data contains information about the date of purchase, year, month, customer age, gender, country, state and product category. Further analysis can reveal insights into different customer segments based on their demographic characteristics such as age and gender as well as location (country & state).

The dataset also includes 3 additional columns at the end: quantity purchased in each transaction, unit cost and unit price for each product or service purchased which can be used to determine if customers are purchasing items in bulk or buying more expensive items than usual. Likewise any discrepancies between the unit cost & price can help establish whether discounts were applied during those transactions which could potentially point towards loyalty or reward programs being put in place for returning customers. Lastly the final column shows total revenue generated from those purchases which we can use to identify any patterns whereby certain groups of customers show higher purchasing power than others based on their spends (unit cost & quantity combination) over various periods/months/years of sales interactions with them.

In summary this dataset allows us to explore numerous dimensions related to ascertaining superior sales performance by studying how its various attributes play out together when it comes down to driving profitability through improved customer acquisition strategies as well increasing purchase rates from existing ones minus any discounts available in-between!

Research Ideas

• Analyzing customer demographics by countries and states to better target future marketing campaigns.

• Tracking changes in customers’ spending habits over time for different product categories.

• Identifying which product categories have the highest average revenue per sale to help prioritize resources for those products or services

Acknowledgements

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

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: SalesForCourse_quizz_table.csv | Column name | Description | |:---------------------|:--------------------------------------------------| | Date | Date of the sale. (Date) | | Year | Year of the sale. (Integer) | | Month | Month of the sale. (Integer) | | Customer Age | Age of the c...

Demographical Shopping Purchases Data

Analyzing customer purchasing patterns and preferences

By Joseph Nowicki [source]

About this dataset

This dataset contains demographic information about customers who have made purchases in a store, including their name, IP address, region, age, items purchased, and total amount spent. Furthermore, this data can provide insights into customer shopping behaviour for the store in question - from their geographical information to the types of products they purchase. With detailed demographic data like this at hand it is possible to make strategic decisions regarding target customers as well as developing specific marketing campaigns or promotions tailored to meet their needs and interests. By gaining deeper understanding of customer habits through this dataset we unlock more possibilities for businesses seeking higher engagement levels with shoppers

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How to use the dataset

This dataset includes information such as customer's names, IP address, age, items purchased and amount spent. This data can be used to uncover patterns in spending behavior of shoppers from different areas or regions across demographics like age group or gender.

Research Ideas

• Analyze customer shopping trends based on age and region to maximize targetted advertising.
• Analyze the correlation between customer spending habits based on store versus online behavior.
• Use IP addresses to track geographical trends in items purchased from a particular online store to identify new markets for targeted expansion

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: Demographic_Data_Orig.csv | Column name | Description | |:---------------|:------------------------------------------------------------------------------------------------| | full.name | The full name of the customer. (String) | | ip.address | The IP address of the customer. (String) | | region | The region of residence of the customer. (String) | | in.store | A boolean value indicating whether the customer made the purchase in-store or online. (Boolean) | | age | The age of the customer. (Integer) | | items | The number of items purchased by the customer. (Integer) | | amount | The total amount spent by the customer. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Joseph Nowicki.

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...

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.

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.

Demographics Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used to compare demographics breakdown (age and income excluded in this free sample view) for one company vs. a competitor for a set period of time (Ex: How do demographics like wealth, ethnicity, children in the household, homeowner status, and political affiliation differ for Walmart vs. Target shopper?).

Inquire about a CE subscription to perform more complex, near real-time demographics analysis functions on public tickers and private brands like: • Analyze a demographic, like age or income, within a state for a company in 2023 • Compare all of a company’s demographics to all of that company’s competitors through most recent history

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Demographics Analysis

Problem A global retailer wants to understand company performance by age group.

Solution Consumer Edge transaction data can be used to analyze shopper transactions by age group to understand: • Overall sales growth by age group over time • Percentage sales growth by age group over time • Sales by age group vs. competitors

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key demographic drivers of growth for company-wide reporting • Reduce investment in underperforming age groups, both online and offline • Determine retention by age group to refine campaign strategy • Understand how different age groups are performing compared to key competitors

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period ...

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...

Description This dataset provides synthetic data that simulates customer demographics, financial attributes, and behavioral patterns for customer retention analysis. It is designed for machine learning, data analysis, and business insights.

The dataset includes a variety of features such as customer age, gender, income, spending score, credit score, and more. It also includes a binary target variable (Target) that indicates customer retention:

• 0: Customer not retained.

• 1: Customer retained . With 30,000 rows and 12 columns, this dataset is ideal for:

• Predictive modeling to identify factors influencing customer retention.

• Exploratory Data Analysis (EDA) to understand customer behavior.

• Feature engineering and visualization projects.

Columns * Customer_ID: Unique identifier for each customer. * Age: Age of the customer (18–70 years). * Gender: Gender of the customer (Male/Female). * Annual_Income: Annual income in USD ($20,000–$150,000). * Spending_Score: A score based on customer spending habits (1–100). * Region: Geographical region (North, South, East, West). * Marital_Status: Marital status (Single, Married, Divorced, Widowed). * Num_of_Children: Number of children in the household (0–4). * Employment_Status: Employment status (Employed, Unemployed, Student, Retired). * Credit_Score: Credit score (300–850). * Online_Shopping_Frequency: Monthly frequency of online shopping (0–20). * Target: Binary variable for customer retention (0 = Not Retained, 1 = Retained).

Key Features

• Realistic and diverse feature set for business analysis.
• Imbalanced target variable to simulate real-world retention scenarios.
• Fully synthetic data, ensuring privacy and usability for public projects.

Access high-fidelity consumer data powered by our proprietary modeling technology that provides the most comprehensive consumer intelligence, accurate targeting, first-party data enrichment, and personalization at scale. Our deterministic dataset, anchored in the purchasing habits of over 140 million U.S. consumers, delivers superior targeting performance with proven 70% increase in ROAS.

Core Data Assets Transactional Data Foundation: Real purchasing behavior from over 140 million U.S. consumers with 8.5 billion behavioral signals across 250 million adults. Seven years of daily credit card and debit card purchase data aggregated from all major credit cards sourced from more than 300 national banks, capturing $2+ trillion in annual discretionary spending.

Consumer Demographics & Lifestyle: Comprehensive profiles including age, income, household composition, geographic distribution, education, employment, and lifestyle indicators. Our proprietary taxonomy organizes consumer spending across 8,000+ brands and 2,500+ merchants, from major retailers to emerging direct-to-consumer brands.

Behavioral Segmentation: 150+ custom consumer communities including demographic groups (Gen Z, Millennials, Gen X), lifestyle segments (Health & Fitness Enthusiasts, Tech Early Adopters, Luxury Shoppers), and behavioral categories (Deal Seekers, Brand Loyalists, Premium Service Users, Streaming Subscribers). Purchase Intelligence: Deep insights into consumer spending patterns across entertainment, fitness, fashion, technology, travel, dining, and retail categories. Our models identify cross-category purchasing behaviors, seasonal trends, and brand switching patterns to optimize targeting strategies. Advanced Modeling Technology

Our proprietary consumer intelligence engine combines deterministic transaction-based data with Smart Audience Engineering that transforms first-party signals from anonymized website traffic, behavioral indicators, and CRM enrichment into precision-modeled segments. Unlike traditional data providers who sell static lists, our AI-powered predictive modeling continuously learns and optimizes for unprecedented precision and superior conversion outcomes.

Performance Advantages: Audiences built on user-level transactional data deliver 70% increase in ROAS compared to traditional targeting methods. Weekly-optimized audiences with performance narratives eliminate wasted ad spend by 20-30%, while our deterministic AI models analyze hundreds of attributes and conversion-validated signals to identify prospects with genuine purchase intent, not just lookalike behaviors.

Data Trust is a leading market research firm that specializes in providing valuable insights on consumer behavior, preferences, and trends. With a long history of delivering high-quality data, Data Trust is known for its meticulous approach to data collection and analysis. The company's extensive portfolio includes data on demographics, consumer spending habits, and product preferences, making it an invaluable resource for businesses seeking to gain a deeper understanding of their target audience.

Data Trust's data offerings are diverse and include data on over 1000 industries, with a focus on emerging markets and niche segments. The company's team of experts works closely with clients to identify specific data requirements, ensuring that the data provided meets their exact needs. With a commitment to data authenticity and accuracy, Data Trust has established itself as a trusted partner in the world of market research.

This AI-Driven Consumer Behavior Dataset captures key aspects of online shopping behavior, including purchase decisions, browsing activity, customer reviews, and demographic details. The dataset is designed for research in consumer behavior analysis, AI-driven recommendation systems, and digital marketing optimization.

Key Features: ✔ Consumer Purchase Data – Tracks product purchases, prices, discounts, and payment methods. ✔ Clickstream Data – Includes browsing behavior, pages visited, session duration, and cart abandonment. ✔ Customer Reviews & Sentiments – Provides ratings, textual reviews, and sentiment analysis scores. ✔ Demographic Information – Includes age, gender, location, and income levels. ✔ Target Column (purchase_decision) – Indicates whether a customer completed a purchase (1) or not (0).

Retail Customer Segmentation

Uncovering Interesting Patterns in Purchase Behavior

By Abhishek Sharma [source]

About this dataset

This dataset contains customer purchase data from a retail store, offering insight into customers' shopping habits. Compiling transactions across multiple invoices, this dataset offers an opportunity to analyze and measure customer behavior in order to understand buying patterns and devise strategies to drive increased sales. From individual items purchased to total spend by country of origin, this comprehensive dataset allows for detailed segmentation analysis of how customers shop, where they spend their money and which products are most popular. With this information businesses can tailor their services more precisely and adjust their prices accordingly for maximum benefit. Dive in now and uncover valuable insights about your customers!

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How to use the dataset

This dataset is a great resource for customer segmentation analysis. Customer segmentation is the process of dividing customers into different subgroups based on characteristics such as age, gender, income, and purchasing behavior. By understanding these characteristics and customer behaviors, businesses can make more informed decisions on how to best target their marketing efforts to reach the right people with the right message.

Research Ideas

• Estimating customer lifetime value by taking into account the frequency of purchases, unit price and country of origin.
• Analyzing customer purchase patterns to identify which items are popular with different customers segments and tailor product/marketing strategies accordingly.
• Using machine learning algorithms to cluster customers based on their purchasing behavior and preferences to effectively target marketing efforts

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: customer_segmentation.csv | Column name | Description | |:----------------|:---------------------------------------------| | InvoiceNo | Unique identifier for each invoice. (String) | | StockCode | Unique identifier for each product. (String) | | Description | Description of the product. (String) | | Quantity | Number of items purchased. (Integer) | | InvoiceDate | Date of the invoice. (Date) | | UnitPrice | Price of each item. (Float) | | Country | Country of origin of the purchase. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Abhishek Sharma.

This dataset contains 150,000 retail interaction records representing customer journeys in both e-commerce and in-store environments. It captures detailed behavioral, demographic, and product-related information to support research in product sales history, customer demographics, purchase patterns, personalized shopping experiences, customer behavior analysis, and predictive modeling.

Each row corresponds to a unique customer–product interaction, including session details, browsing or purchasing behavior, and applied discounts. The purchase column serves as the binary target variable (1 = purchased, 0 = not purchased), making the dataset suitable for various classification and recommendation tasks.

Key Features

Size: 150,000 rows × 19 columns

Target Column: purchase (binary: 1 = purchased, 0 = not purchased)

Data Types:

Categorical: User ID, product ID, interaction type, device type, product category, brand, location, gender

Numerical: Price, discount, age, loyalty score, previous purchase count, average purchase value

Temporal: Timestamp (to study trends and patterns)

Text: Search keywords

Behavioral Data: Interaction type (view, click, add to cart, purchase), purchase history statistics

Product Metadata: Category, brand, price, discount percentage

User Demographics: Age, gender, loyalty score

Applications:

Retail personalization

Purchase prediction

Customer segmentation

Behavioral pattern analysis

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific ta...

This dataset contains anonymized credit card transaction records, enriched with behavioral cluster assignments and key transaction attributes such as merchant category, transaction type, and customer demographics. Designed for segmentation and marketing analytics, it enables organizations to identify spending patterns, target customer segments, and optimize marketing strategies.

Product Placement Market Outlook

According to our latest research, the global product placement market size reached USD 26.2 billion in 2024, reflecting the growing integration of branded content across multiple entertainment and media platforms. The market is experiencing a robust expansion, with a compound annual growth rate (CAGR) of 12.3% projected from 2025 to 2033. By the end of 2033, the product placement market is forecasted to attain a value of USD 74.2 billion. This remarkable growth is primarily propelled by the increasing shift of advertising budgets towards non-intrusive marketing strategies and the proliferation of digital streaming services, which have created new opportunities for seamless brand integration in content.

One of the predominant growth drivers for the product placement market is the evolving media consumption habits of global audiences. As viewers migrate from traditional television to digital streaming platforms, advertisers have been compelled to seek innovative methods to engage consumers without disrupting their viewing experience. Product placement, which subtly integrates brands into the storyline of films, television shows, and digital content, has proven highly effective in capturing audience attention while maintaining the narrative flow. The rise of binge-watching culture and the increasing popularity of original content on over-the-top (OTT) platforms have further amplified the demand for strategic brand integrations, creating a fertile environment for market expansion.

Another significant factor fueling market growth is the demonstrable return on investment (ROI) that product placement offers compared to conventional advertising. Unlike traditional commercials that viewers can easily skip or ignore, product placements are embedded within the content, ensuring higher visibility and brand recall. This approach not only enhances brand authenticity but also allows companies to target specific demographics with precision. For instance, automotive brands frequently place their latest models in blockbuster films, while tech companies leverage digital placements in web series and video games. The measurable impact on consumer behavior, coupled with the ability to track engagement through advanced analytics, has made product placement an indispensable component of modern marketing strategies.

Furthermore, the integration of advanced technologies such as artificial intelligence (AI), augmented reality (AR), and data analytics is revolutionizing the product placement landscape. AI-powered solutions enable content creators and marketers to identify optimal placement opportunities based on audience preferences and viewing patterns. AR and virtual product placements are gaining traction, especially in digital and social media content, allowing brands to reach younger, tech-savvy audiences in immersive ways. These technological advancements are not only enhancing the effectiveness of product placements but also enabling real-time customization and localization, thus broadening the marketÂ’s reach and appeal across diverse regions and consumer segments.

From a regional perspective, North America continues to dominate the product placement market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The presence of major film and television production hubs in the United States, along with the rapid adoption of digital platforms, has solidified North AmericaÂ’s leadership. However, the Asia Pacific region is witnessing the fastest growth, driven by the burgeoning entertainment industry in countries such as India, China, and South Korea. The increasing localization of content and the rising penetration of OTT services are expected to further accelerate market growth in this region over the forecast period.

Brand Activation Agencies play a crucial role in the evolving landscape of product placement. These agencies specialize in creating immersive brand experiences that seamlessly integrate into the content consumers engage with daily. By leveraging their expertise in strategic planning and creative execution, brand activation agencies ensure that product placements are not only visible but also resonate with the target audience. They work closely with content creators to develop narratives that naturally incorporate brands, enhancing authenti

Podcast Advertising Market Outlook

According to our latest research, the global podcast advertising market size reached USD 2.6 billion in 2024, reflecting the rapid adoption of digital audio platforms among consumers and brands alike. The market is projected to grow at a robust CAGR of 13.8% from 2025 to 2033, reaching an estimated USD 8.2 billion by 2033. This impressive growth trajectory is primarily driven by the increasing popularity of on-demand audio content, deeper audience engagement, and the ability of podcast advertising to deliver targeted, measurable results for brands across various industry verticals.

One of the most significant growth factors for the podcast advertising market is the shift in consumer behavior towards digital and mobile-first media consumption. As more listeners turn to podcasts for news, entertainment, education, and niche interests, advertisers are capitalizing on the highly engaged and loyal podcast audience. The intimate nature of the medium, where hosts often build strong relationships with their listeners, enhances the effectiveness of advertising messages. This high engagement rate, coupled with the ability to target ads based on listener demographics, interests, and listening habits, is driving substantial brand investment in podcast advertising. Moreover, the proliferation of smart devices and streaming platforms has made podcasts more accessible, further expanding the listener base and, subsequently, the potential reach for advertisers.

Another key driver is the evolution of ad technology within the podcasting ecosystem. The introduction of dynamic ad insertion, programmatic buying, and advanced analytics has revolutionized the way brands approach podcast advertising. Dynamic ad insertion allows for real-time ad placement, ensuring that advertisements are timely, relevant, and tailored to specific audience segments. Programmatic tools enable advertisers to optimize campaign performance and maximize ROI, while analytics platforms provide detailed insights into listener behavior, ad impressions, and conversions. These technological advancements have made podcast advertising more measurable and accountable, attracting a broader spectrum of advertisers, including those from traditionally conservative sectors such as finance and healthcare.

The diversification of industry verticals utilizing podcast advertising is also fueling market growth. Industries such as retail & e-commerce, media & entertainment, BFSI, healthcare, automotive, and technology are increasingly recognizing the value of podcast ads in reaching niche audiences and driving brand awareness or direct response campaigns. Brands are leveraging a variety of ad formats and placements to align with their marketing objectives, whether it is building long-term brand equity or driving immediate sales. As a result, the podcast advertising market is witnessing an influx of both large enterprises and smaller businesses, each seeking to capitalize on the unique advantages offered by the medium.

Understanding the dynamics of Podcast Audience Research is crucial for advertisers aiming to maximize the impact of their campaigns. By delving into audience demographics, listening habits, and preferences, brands can tailor their messages to resonate more effectively with listeners. This research not only helps in crafting compelling content but also in selecting the right podcasts and ad formats that align with the target audience's interests. As the podcasting landscape becomes increasingly competitive, leveraging audience insights can be a game-changer for advertisers seeking to enhance engagement and conversion rates. The ability to analyze and interpret audience data is becoming a vital skill for marketers in the podcast advertising space.

Regionally, North America continues to dominate the podcast advertising market, accounting for the largest share of global revenues in 2024. The United States, in particular, is home to a highly mature podcasting ecosystem with a vast array of content creators, platforms, and listeners. Europe is also experiencing significant growth, driven by increasing adoption of podcasts in markets such as the UK, Germany, and France. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by rising smartphone penetration and the localization of podcast content. Latin America and the

Connected TV Advertising Market Outlook

According to our latest research, the Connected TV Advertising market size reached USD 19.8 billion in 2024, reflecting the rapid adoption of digital streaming and the proliferation of smart devices worldwide. The market is experiencing robust momentum, registering a CAGR of 13.1% from 2025 to 2033. By the end of the forecast period, the Connected TV Advertising market is projected to attain a value of USD 52.4 billion by 2033. This impressive growth is driven by the convergence of television and digital advertising, shifting consumer viewing habits, and the increasing sophistication of ad targeting technologies.

One of the primary growth factors for the Connected TV Advertising market is the accelerated cord-cutting trend, as consumers move away from traditional cable and satellite TV subscriptions in favor of streaming services. This shift is creating a vast new audience for advertisers, who are eager to reach viewers on platforms such as Netflix, Hulu, Amazon Prime Video, and YouTube TV. The ability to deliver highly targeted, interactive, and measurable ads through Connected TV (CTV) platforms is transforming the advertising landscape, making it more efficient and effective than ever before. Advertisers are leveraging data-driven insights to optimize campaigns in real time, resulting in higher engagement rates and improved return on investment.

Another significant driver is the technological advancement in CTV devices and advertising platforms. The integration of artificial intelligence, machine learning, and advanced analytics is enabling precise audience segmentation and personalized ad delivery. These technologies empower brands to tailor their messages based on viewer preferences, demographics, and viewing behavior, leading to a more relevant and engaging advertising experience. Moreover, the rise of programmatic advertising in the CTV ecosystem is streamlining the buying and selling of ad inventory, reducing manual processes, and increasing transparency for both advertisers and publishers. This evolution is attracting a diverse range of advertisers, from global brands to local businesses, further fueling market growth.

Additionally, the expansion of high-speed internet infrastructure and the growing availability of affordable smart TVs and streaming devices are broadening the reach of CTV advertising. Emerging markets, particularly in Asia Pacific and Latin America, are witnessing a surge in CTV adoption as internet penetration rates climb and consumer incomes rise. This democratization of access is enabling advertisers to tap into previously underserved audiences, unlocking new revenue streams and driving global market expansion. The seamless integration of CTV ads across multiple devices and platforms is also enhancing campaign effectiveness, as advertisers can now deliver consistent messaging across the consumer journey.

Digital Video Advertising is becoming an integral part of the Connected TV ecosystem, offering advertisers a dynamic and engaging way to connect with audiences. As consumers increasingly turn to streaming platforms for their entertainment needs, digital video ads provide a seamless way to integrate brand messages into the viewing experience. These ads leverage high-quality visuals and sound to capture viewer attention, making them a powerful tool for storytelling and brand building. With the ability to target specific demographics and measure performance in real-time, digital video advertising is helping brands achieve greater reach and impact in the competitive media landscape.

From a regional perspective, North America continues to dominate the Connected TV Advertising market, accounting for the largest share of global revenues. The regionÂ’s mature digital ecosystem, high household adoption of smart TVs, and strong presence of leading streaming platforms make it a prime market for CTV advertising innovation. Europe follows closely, benefiting from widespread broadband connectivity and a rapidly evolving regulatory landscape that supports digital advertising growth. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by a burgeoning middle class, increasing digital literacy, and aggressive investments in streaming infrastructure. Latin America and the Middle East & Africa are also showing promis

Budget Binder with Envelopes Market Outlook

According to our latest research, the global Budget Binder with Envelopes market size reached USD 1.18 billion in 2024, with a robust compound annual growth rate (CAGR) of 7.6% projected through the forecast period. By 2033, the market is expected to attain a value of USD 2.29 billion, driven by the growing emphasis on personal finance management, rising adoption of cash envelope budgeting systems, and an increasing preference for physical tools that promote financial discipline. This growth is underpinned by evolving consumer behavior, technological integration in traditional budgeting tools, and the proliferation of e-commerce platforms facilitating global reach and accessibility.

One of the primary growth drivers for the Budget Binder with Envelopes market is the rising awareness surrounding personal finance management. In recent years, individuals have become increasingly conscious of the need to track and control their spending, particularly in the wake of economic uncertainties and inflationary pressures. The envelope budgeting method, which involves allocating cash to specific spending categories, has gained traction as a simple yet effective way to curb overspending. Budget binders with envelopes cater to this need by providing a tangible, organized, and visually appealing solution for individuals who prefer hands-on methods over digital alternatives. Furthermore, the popularity of financial literacy campaigns and social media influencers advocating for envelope budgeting has contributed to heightened demand for these products, especially among millennials and Gen Z consumers.

Technological advancements and design innovation have also played a significant role in propelling market growth. Manufacturers are increasingly focusing on enhancing the functionality and aesthetics of budget binders, offering features such as RFID protection, customizable inserts, and integration with mobile budgeting apps. The use of premium materials, personalized designs, and eco-friendly options has expanded the market’s appeal to a broader demographic, including environmentally conscious consumers and gift buyers. Additionally, the availability of budget binders in various formats—ranging from compact, travel-friendly versions to larger, comprehensive kits—has diversified the product offering and enabled manufacturers to target niche segments such as students, small business owners, and educators.

Another critical growth factor is the expansion of distribution channels, particularly the surge in online retail. E-commerce platforms have democratized access to budget binders with envelopes, allowing consumers worldwide to purchase products from a wide array of brands and suppliers. The convenience of online shopping, combined with detailed product information, customer reviews, and competitive pricing, has significantly boosted sales volumes. Moreover, the presence of budget binders in specialty stores, supermarkets, and hypermarkets ensures that consumers who prefer in-person shopping experiences can easily access these products. Strategic partnerships between manufacturers and retailers, as well as targeted marketing campaigns, have further strengthened market penetration and brand visibility.

In the realm of personal finance management, the use of a Coupon Organizer Accordion is gaining popularity among budget-conscious individuals. This tool allows users to efficiently categorize and store coupons, making it easier to track savings and reduce overall spending. The accordion-style design provides ample space for organizing coupons by category or expiration date, enhancing the user's ability to quickly access and utilize them during shopping trips. As consumers continue to seek ways to maximize their purchasing power, the integration of coupon organizers with budget binders offers a comprehensive approach to financial management. This synergy not only aids in budgeting but also encourages smarter shopping habits, contributing to long-term financial well-being.

From a regional perspective, North America continues to dominate the Budget Binder with Envelopes market, accounting for a significant share of global revenue in 2024. The regionÂ’s strong focus on personal finance education, coupled with a mature retail infrastructure and high disposable inc