Shopper Demographics Analytics Market Outlook

As per our latest research, the global shopper demographics analytics market size in 2024 is valued at USD 5.3 billion, with a robust CAGR of 14.7% projected through the forecast period. By 2033, the market is expected to reach USD 17.2 billion, reflecting the accelerating adoption of advanced analytics solutions in retail and related sectors. The primary growth driver is the increasing need for retailers and brands to understand and predict consumer behavior in an era characterized by omnichannel shopping and intense competition.

The growth of the shopper demographics analytics market is significantly propelled by the retail sector’s digital transformation. Retailers are increasingly leveraging analytics to gain granular insights into customer demographics, preferences, and purchasing behavior. The integration of artificial intelligence (AI) and machine learning (ML) into analytics platforms has enabled businesses to process vast amounts of data in real time, offering actionable insights that drive personalized marketing and operational efficiency. As consumer expectations for tailored experiences continue to rise, retailers are investing heavily in shopper analytics to enhance customer engagement, improve inventory management, and optimize store layouts, further fueling market expansion.

Another key growth factor is the proliferation of e-commerce and the corresponding surge in online data generation. E-commerce platforms are uniquely positioned to collect detailed demographic and behavioral data, which can be analyzed to segment customers, predict purchasing trends, and personalize marketing campaigns. The adoption of cloud-based analytics solutions has further democratized access to advanced analytics, allowing even small and medium-sized enterprises (SMEs) to harness the power of shopper demographics analytics. Moreover, the integration of analytics with customer relationship management (CRM) and point-of-sale (POS) systems has streamlined data collection and analysis, enabling businesses to respond swiftly to changing consumer preferences.

The increasing focus on omnichannel retail strategies is also driving demand for shopper demographics analytics. Retailers are striving to provide a seamless shopping experience across physical stores, online platforms, and mobile applications. Analytics solutions help bridge the gap between different channels by offering a unified view of customer behavior, enabling businesses to deliver consistent and personalized experiences. The rise of smart stores and the deployment of Internet of Things (IoT) devices have further enriched the data ecosystem, providing real-time insights into foot traffic, dwell times, and purchase patterns. These advancements are expected to sustain the market’s high growth trajectory over the coming years.

From a regional perspective, North America currently dominates the shopper demographics analytics market, owing to the presence of major technology providers and early adoption by leading retailers. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, expanding retail infrastructure, and increasing digital adoption among consumers. Europe also holds a significant market share, supported by strong regulatory frameworks and a mature retail sector. The Middle East & Africa and Latin America are emerging as promising markets, as retailers in these regions invest in analytics to stay competitive and cater to evolving consumer demands. These regional dynamics underscore the global relevance and growth potential of shopper demographics analytics.

Component Analysis

The shopper demographics analytics market by component is bifurcated into software and services, with software solutions representing the larger share in 2024. The software segment encompasses a wide range of analytics platforms, including proprietary and open-source solutions designed to collect, process, and visualize demographic data. These platforms leverage advanced technologies such as AI, ML, and big data analytics to deliver actionable insights in real time. The growing adoption of cloud-based analytics software has further accelerated market growth, enabling retailers to scale their analytics capabilities without significant upfront investment in IT infrastructure. The continuous evolution of analytics software, with features such as predictive modeling, data v

This dataset encompasses deterministic consumer demographics, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Included are age, gender, ethnicity, location, employment, education, income, pet ownership, having kids/children, relationship status, military status, number of people in household, car ownership vs lease, small business owner, spanish TV viewership as a proxy for acculturation, and having kids under 18 in the home.

According to a survey carried out in 2024 in the United States, some ** percent of baby boomers were shopping for groceries once a week. Among millennials, the share of those shopping weekly for groceries was lower, at ** percent. On the other hand, ** percent of millennials were shopping for groceries daily, while baby boomers were only ******percent. Find this and more survey data in our Consumer Insights tool. Filter by countless demographics, drill down to your own, hand-tailored target audience, and compare results across countries worldwide.

In 2025, consumers in the United States were surveyed about their regular food and everyday products shopping destinations. Among those who shopped at discount stores, ** percent of both male and female respondents reported doing so. Find this and more survey data in our Consumer Insights tool. Filter by countless demographics, drill down to your own, hand-tailored target audience, and compare results across countries worldwide.

Welcome to the Retail Sales and Customer Demographics Dataset! 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 behavior.

****Dataset Overview:**

This dataset is a snapshot of a fictional retail landscape, capturing essential attributes that drive retail operations and customer interactions. It includes key details such as Transaction ID, Date, Customer ID, Gender, Age, Product Category, Quantity, Price per Unit, and Total Amount. These attributes enable a multifaceted exploration of sales trends, demographic influences, and purchasing behaviors.

Why Explore This Dataset?

• Realistic Representation: Though synthetic, the dataset mirrors real-world retail scenarios, allowing you to practice analysis within a familiar context.
• Diverse Insights: From demographic insights to product preferences, the dataset offers a broad spectrum of factors to investigate.
• Hypothesis Generation: As you perform EDA, you'll have the chance to formulate hypotheses that can guide further analysis and experimentation.
• Applied Learning: Uncover actionable insights that retailers could use to enhance their strategies and customer experiences.

Questions to Explore:

• How does customer age and gender influence their purchasing behavior?
• Are there discernible patterns in sales across different time periods?
• Which product categories hold the highest appeal among customers?
• What are the relationships between age, spending, and product preferences?
• How do customers adapt their shopping habits during seasonal trends?
• Are there distinct purchasing behaviors based on the number of items bought per transaction?
• What insights can be gleaned from the distribution of product prices within each category?

Your EDA Journey:

Prepare to immerse yourself in a world of data-driven exploration. Through data visualization, statistical analysis, and correlation examination, you'll uncover the nuances that define retail operations and customer dynamics. EDA isn't just about numbers—it's about storytelling with data and extracting meaningful insights that can influence strategic decisions.

Embrace the Retail Sales and Customer Demographics Dataset as your canvas for discovery. As you traverse the landscape of this synthetic retail environment, you'll refine your analytical skills, pose intriguing questions, and contribute to the ever-evolving narrative of the retail industry. Happy exploring!

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
   ...

Context This dataset is of retail data collected from various retailers across the United States. The dataset is designed to mimic the complexity and challenges of real-world retail data, making it ideal for research and training purposes in the field of predictive analytics. The dataset covers a wide range of aspects, from customer demographics to product details, store information, and sales data.

Content The dataset contains 20,000 rows and 19 columns, each row representing a unique purchase made by a customer. The columns in the dataset are as follows:

CustomerID: A unique identifier for each customer. Age: The age of the customer. Gender: The gender of the customer. AnnualIncome: The annual income of the customer in USD. SpendingScore: A score (out of 100) that indicates the customer's spending behavior. ProductCategory: The category of the product that the customer bought. ProductPrice: The price of the product that the customer bought in USD. PurchaseDate: The date when the customer bought the product. StoreID: The ID of the store where the purchase was made. StoreLocation: The location of the store. PaymentMethod: The payment method used by the customer. DiscountApplied: Whether a discount was applied to the purchase (True or False). DiscountPercent: The percentage of discount applied to the purchase. ProductCost: The cost of the product to the retailer in USD. Profit: The profit made by the retailer on the sale in USD. FootTraffic: The number of people that visited the store on the day of the purchase. InventoryLevel: The inventory level of the product at the time of the purchase. MarketingExpenditure: The amount of money spent on marketing the product in USD. CompetitorPrice: The price of the same product at a competitor's store in USD.

More than **** of consumers belonging to Generation Z bought something on social media platforms, according to a survey in 2024. Almost a ***** of overall consumers bought on social media platforms. The consumer experience In a 2023 survey, Facebook and Instagram were the social media platforms offering the best shopping experience. To gain deeper insights into the elements constituting a satisfactory social commerce shopping journey from the user's viewpoint, key factors shaping consumers' heightened engagement with social commerce included, but were not limited to, deals and discounts, seamless purchasing processes, exclusive offers, and increased availability of customer reviews. Social shopping destinations Facebook is the leading social commerce platform globally, except among Gen Z, who favor Instagram and TikTok. However, the types of social media accounts that shoppers followed and purchased from varied by age group. Gen Z and Millennials predominantly bought from brand accounts, with Gen Z also showing a preference for social media influencers. Conversely, Gen X and Boomers preferred purchasing from trusted retailer accounts.

In 2021, ** percent of social shoppers in Indonesia were full-time worker. Social shopping or social commerce is a form of e-commerce that describes buying and selling items through social media platforms. Social commerce practices have gained popularity in Indonesia in recent years.

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 targeting requirements and receive custom pricing for your marketing objectives.

CE Vision USA is the premier data set tracking consumer spend on credit and debit cards. Private investors and corporate clients use CE Vision retail commerce data for competitor analysis, market share, cross-shopping, demographics, and market share data by industry and channel.

This dataset displays demographic information for all Boulder Parks and Recreation members and visitors. The dataset includes customer age, gender, resident status, location (city, state, and zipcode), entry date, and membership package type(s).

Please note that due to the nature of open-ended data entry for many customer detail fields, some customer data (e.g. city) will need to be cleaned and normalized before analysis.

Description: This dataset includes detailed demographic and behavioral information about restaurant consumers. It is designed to provide insights into consumer profiles, preferences, and habits, which can be useful for improving customer experience and developing targeted marketing strategies.

Features:

Consumer_ID: A unique identifier assigned to each consumer in the dataset. City: The city where the consumer resides. State: The state or province where the consumer is located. Country: The country where the consumer lives. Latitude: The geographical latitude of the consumer’s location. Longitude: The geographical longitude of the consumer’s location. Smoker: Indicates whether the consumer is a smoker (e.g., Yes/No). Drink_Level: The consumer’s level of alcohol consumption (e.g., None, Light, Moderate, Heavy). Transportation_Method: The mode of transportation the consumer uses to travel to the restaurant (e.g., Car, Public Transit, Walking). Marital_Status: The consumer’s marital status (e.g., Single, Married, Divorced, Widowed). Usage:

Consumer Profiling: Understand the demographics and habits of different consumer segments to tailor marketing strategies and restaurant offerings. Location Analysis: Analyze consumer location data to identify key markets and optimize restaurant placement or delivery areas. Behavioral Insights: Study smoking and drinking habits to adjust menu options and enhance customer experience. Transportation Trends: Assess how consumers travel to the restaurant to improve accessibility and convenience. Source: The data is collected from restaurant surveys, customer profiles, and demographic studies.

Notes:

Ensure that personal data is handled securely and in compliance with privacy regulations. Regular updates may be necessary to reflect changes in consumer behavior and demographics.

E-commerce Customer Behavior Dataset

Overview

This synthetic dataset contains information about customer behavior in an e-commerce platform. It includes various attributes such as customer demographics, purchase history, and customer satisfaction.

Columns

• customer_id: Unique identifier for each customer.
• name: Full name of the customer.
• email: Email address of the customer.
• age: Age of the customer.
• gender: Gender of the customer (Male/Female).
• annual_income: Annual income of the customer.
• total_purchases: Total number of purchases made by the customer.
• avg_purchase_value: Average value of purchases made by the customer.
• days_since_last_purchase: Number of days since the last purchase.
• customer_satisfaction: Customer satisfaction rating (1-5).
• churn: Whether the customer has churned (1) or not (0).

Potential Uses

• Customer segmentation
• Churn prediction
• Customer lifetime value prediction
• Market basket analysis

License

This dataset is released under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.