Envestnet®| Yodlee®'s Online Purchase Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

CE Scanner US provides financial services investors with point-of-sale transaction data. Proprietary M&A attribution and volume equivalency offer rollup views to ticker and brand level with comparative detailed category/subcategory views into retail sales, volumes, distribution, and trends.

All available on the tool. 24 hours a day/7 days a week/ 365 days a year.

NATIONAL USA DATA: 251 million individuals, 170 million households, over 1000 targeting variables and filters available including income, children, home type, investments, vehicle, life stage, and more. Full postal address on the full file. Email/phone/IP.

app.trakdatainc.com

According to Cognitive Market Research, the global Data Broker Services market size is USD 268154.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 8.00% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 107261.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 80446.26 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 61675.47 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.0% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 13407.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.4% from 2024 to 2031.
Middle East and Africa held the major market ofaround 2% of the global revenue with a market size of USD 5363.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.7% from 2024 to 2031.
The Subscription Paid held the highest Data Broker Services market revenue share in 2024.

Market Dynamics of Data Broker Services Market

Key Drivers of Data Broker Services Market

Increasing Demand for Personalized Marketing Solutions to boost the demand globally

The Data Broker Services Market is being driven by the increasing demand for personalized marketing solutions. Companies across various industries are leveraging data broker services to access valuable consumer insights and enhance their marketing strategies. Data brokers offer a wide range of data sets, including demographic, behavioral, and transactional data, which can be used to create targeted marketing campaigns. By utilizing data broker services, companies can tailor their marketing messages to specific consumer segments, leading to higher engagement and conversion rates. This trend is expected to continue driving the growth of the Data Broker Services Market as businesses increasingly prioritize personalized marketing approaches to remain competitive in the digital age.

Growing Focus on Data Monetization to Propel Market Growth

Another key driver of the Data Broker Services Market is the growing focus on data monetization. Organizations are realizing the value of their data assets and are looking for ways to monetize them. Data broker services enable companies to sell their data to third parties, such as marketers, researchers, and other businesses, generating additional revenue streams. This trend is particularly prevalent in industries with large amounts of consumer data, such as retail, finance, and healthcare. By monetizing their data, companies can unlock new revenue opportunities and offset the costs associated with data collection and management. As the demand for data-driven insights continues to grow, the Data Broker Services Market is expected to expand, driven by the increasing number of organizations looking to capitalize on their data assets.

Restraint Factors Of Data Broker Services Market

Regulatory Challenges and Data Privacy Concerns to Limit the Sales

One of the key restraints in the Data Broker Services Market is the increasing regulatory challenges and data privacy concerns. With the implementation of regulations such as the GDPR in Europe and the CCPA in California, data brokers are facing stricter requirements for data collection, processing, and sharing. Compliance with these regulations requires significant resources and can limit the ability of data brokers to collect and monetize data. Additionally, concerns about data privacy and security among consumers are leading to greater scrutiny of data broker practices, further complicating the operating environment for these companies. As regulatory pressures continue to increase, data brokers may face challenges in expanding their operations and maintaining profitability.

Opportunity for the Data Broker Services Market

The Data Broker Service Market is poised to benefit significantly from the integration of blockchain technology.

By leveraging blockchain's decentralized and immutable nature, data brokers can ensure tamper-proof data exchange, enable secure data sharing, and provide auditable trails. This can increase trust and confidence in data exchange, driving growth in the data broker...

The awareness among worldwide consumers about companies selling their personal data to third parties has grown in recent years. As of July 2022, three in four consumers in selected countries worldwide said they knew that companies sell personal information. In comparison, in 2020, this share was a little over 60 percent.

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Here's the schema of Consumer Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_description
company_sic_code4 company_sic_code4_description
company_sic_code6
company_sic_code6_description
company_sic_code8
company_sic_code8_description company_parent_company
company_parent_company_location company_public_private company_subsidiary_company company_residential_business_code company_revenue_at_side_code company_revenue_range
company_revenue company_sales_volume
company_small_business company_stock_ticker company_year_founded company_minorityowned
company_female_owned_or_operated company_franchise_code company_dma company_dma_name
company_hq_address
company_hq_city company_hq_duns company_hq_state
company_hq_zip5 company_hq_zip4 co...

Consumer Intelligence: Comprehensive demographic, lifestyle & purchase data from 140M+ consumers across 8,000+ brands. Deterministic transaction-based modeling delivers 70% ROAS increase vs traditional targeting through 150+ behavioral segments.

CE Transact Signal USA is the premier merchant attributable alternative data set tracking consumer spend on credit and debit cards, available as a panelized aggregated feed.

Alesco's Consumer Database contains demographic information on almost every household in the nation. Nowhere else will you find more complete and accurate information on U.S. households, individuals by name and age, lifestyle interests, hobbies, purchase behavior and ethnicity along with detailed financial-related data including mortgage, wealth and credit attributes. Alesco provides hundreds of selection options to help you target your customers more precisely.

We build the database utilizing hundreds of sources including public records, directories, county recorder and tax assessor files, US Census data, surveys, and purchase transactions. The file is built at both the individual and household levels to provide multiple targeting options. We continuously utilize USPS processing routines to give you the most complete and up-to-date addresses.

Flexible pricing available to meet all your business needs. Data is available on a transactional basis or for yearly licensing with unlimited use cases for marketing and analytics.

Retail Sales of Consumer Goods: Year to Date: Zhejiang data was reported at 929.300 RMB bn in Mar 2025. This records an increase from the previous number of 603.900 RMB bn for Feb 2025. Retail Sales of Consumer Goods: Year to Date: Zhejiang data is updated monthly, averaging 660.297 RMB bn from Jan 2003 (Median) to Mar 2025, with 233 observations. The data reached an all-time high of 3,390.000 RMB bn in Dec 2024 and a record low of 26.993 RMB bn in Jan 2003. Retail Sales of Consumer Goods: Year to Date: Zhejiang data remains active status in CEIC and is reported by Zhejiang Bureau of Statistics. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HA: Retail Sales of Consumer Goods: Provincial and Municipal Statistical Bureau.

Track share of shelf, predict revenue surprises, and drill down into brand and category level performance by household demography across thousands of brands and hundreds of manufacturers. Scanner Consumer Packaged Goods (CPG) data is sourced from thousands of retail stores and millions of underlying US households across grocery and drugstore chains. Available exclusively to investors.

Noticeable shift to data-driven advertising and marketing is driving demand for customer data platform (CDP) services. The global customer data platform market is placed at US$ 2.6 billion in 2024 and has been projected to expand at a CAGR of 13% to reach a valuation of US$ 8.7 billion by 2034-end.

Report Attributes	Details
Customer Data Platform Market Size (2024E)	US$ 2.6 Billion
Forecasted Market Value (2034F)	US$ 8.7 Billion
Global Market Growth Rate (2024 to 2034)	13% CAGR
North America Market Share (2034E)	24.3% CAGR
Market Share of Retail Segment (2034F)	21%
Japan Market Growth Rate (2024 to 2034)	13.5%
Key Companies Profiled	NGDATA Inc. ActionIQ Tealium Adobe AgileOne SAP SE Ascent360, Inc. Salesforce BlueConic Oracle Corporation Lytics Inc. mParticle Inc.

Country-wise Insights

Attribute	United States
Market Value (2024E)	US$ 300 Million
Growth Rate (2024 to 2034)	13.5% CAGR
Projected Value (2034F)	US$ 1.06 Billion

Attribute	China
Market Value (2024E)	US$ 300 Million
Growth Rate (2024 to 2034)	13% CAGR
Projected Value (2034F)	US$ 1.02 Billion

Category-wise Insights

Attribute	Analytics
Segment Value (2024E)	US$ 1.3 Billion
Growth Rate (2024 to 2034)	12.3% CAGR
Projected Value (2034F)	US$ 4.2 Billion

Attribute	Retail
Segment Value (2024E)	US$ 600 Million
Growth Rate (2024 to 2034)	12% CAGR
Projected Value (2034F)	US$ 1.8 Billion

Retail Sales of Consumer Goods: Shanxi data was reported at 818.050 RMB bn in 2024. This records an increase from the previous number of 798.180 RMB bn for 2023. Retail Sales of Consumer Goods: Shanxi data is updated yearly, averaging 306.360 RMB bn from Dec 1995 (Median) to 2024, with 30 observations. The data reached an all-time high of 818.050 RMB bn in 2024 and a record low of 37.595 RMB bn in 1995. Retail Sales of Consumer Goods: Shanxi data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HA: Retail Sales of Consumer Goods: Province.

The "Sales Transaction Details" dataset presents a detailed account of individual sales transactions, encompassing essential information such as order dates, customer details, product categories, and financial metrics. This dataset serves as a valuable resource for businesses seeking to analyse and optimize their sales processes. By exploring the nuances of each transaction, stakeholders can gain insights into customer preferences, product performance, and overall sales dynamics. From the total sales amount and quantity of products ordered to the profitability of each transaction, this dataset empowers decision-makers with the information needed to enhance customer satisfaction and refine business strategies. Whether assessing the impact of specific products or understanding regional variations in sales, the dataset offers a comprehensive view of the intricacies within the sales pipeline.

Table: Sales Transaction Details

Column Name	Description
Order Date	The date when the order was placed.
Customer Name	The name of the customer placing the order.
State	The state in which the customer resides.
Category	The broad category to which the product belongs.
Sub-Category	A more specific sub-category of the product.
Product Name	The name of the product in the order.
Sales	The total sales amount for the order.
Quantity	The quantity of products ordered.
Profit	The profit earned from the order.

• This Sales Transaction Details dataset provides a granular view of individual sales transactions, including key details such as order date, customer information, product categories, and financial metrics. Analysing this dataset allows for a deeper understanding of customer preferences, product performance, and overall sales dynamics.

Snapshot img

Customer Analytics Applications Market Size 2024-2028

The customer analytics applications market size is estimated to grow by USD 16.73 billion at a CAGR of 17.58% between 2023 and 2028. The growth of the market depends on several factors, including the increasing number of social media users, the growing need for improved customer satisfaction, and an increase in the adoption of customer analytics by SMEs. Customer analytics application refers to a software or system that analyzes customer data such as behavioral, demographic, and personal information to gain insights into their behavior, preferences, and needs. It uses various techniques such as data mining, predictive modeling, and statistical analysis to gather information and make informed decisions in marketing, sales, product development, and overall customer management. The goal of a customer analytics application is to enhance customer understanding and improve business strategies by allowing companies to make data-driven decisions and provide personalized experiences to their customers.

What will be the Size of the Market During the Forecast Period?

To learn more about this report, View Report Sample

Market Dynamics

In the evolving internet retail landscape, businesses are increasingly adopting innovative cloud deployment modes to enhance their operational efficiency. Customer Data Platforms (CDPs) like Neustar and Clarity Insight are pivotal in integrating and analyzing customer data to drive personalized experiences and strategic decisions. These platforms leverage cloud deployment modes to offer scalable solutions that support internet retail operations and enhance customer engagement. Data platforms are instrumental in collecting and processing vast amounts of data, providing valuable insights for trailblazers in the industry. By utilizing advanced cloud deployment modes, companies can efficiently manage their data infrastructure and improve their online retail strategies. Integrating Neustar and Clarity Insight into their systems enables businesses to stay ahead of the competition by offering tailored experiences and optimizing their internet retail performance through scalable solutions.

Key Market Driver

An increase in the adoption of customer analytics by SMEs is notably driving market growth. Expanding the efficiency and performance of business operations is critical to achieving the desired set of goals of an organization. Businesses with a customer-centric approach deal with massive amounts of customer data, which is stored, managed, and processed in real-time. SMEs generate numerous forms of customer data related to customer demographics and sales, marketing campaigns, websites, and conversations. Consequently, these businesses must scrutinize all this customer-related data to achieve a competitive edge in the market. SMEs are majorly using these as they enable better forecasting, resource management, and streamlining of data under one platform, lower operational costs, improve decision-making, and expand sales.

In addition, the increase in customer data, along with the companies' need to automate customer data processing, is leading to the increased adoption by SMEs. Hence, customer analytics is being executed across SMEs for better management of their business operations via a centralized management system with enhanced collaboration, productivity, simplified compliance, and risk management. Such factors are the significant driving factors driving the growth of the global market during the forecast period.

Major Market Trends

Advancements in technology are an emerging trend shaping the market growth. AI and ML technologies have revolutionized the way businesses understand and analyze customer data, allowing them to make more informed decisions and deliver customized experiences. Also, AI and ML have played a critical role in fake detection and prevention in the customer analytics market. Algorithms can identify unusual activities that may indicate fraud by analyzing transactional data and behavioral patterns. This allows businesses to secure themselves and their customers from potential financial losses.

Additionally, AI and ML have enhanced customer segmentation capabilities. Businesses can group customers based on their similarities by using clustering algorithms, allowing them to create targeted marketing campaigns for specific segments. This enables enterprises to personalize their messages and offers, resulting in higher customer engagement and conversion rates. These factors are anticipated to fuel the market growth and trends during the forecast period.

Significant Market Restrain

Data integration issues are a significant challenge hindering market growth. To analyze customer data generated from various types of systems, enterprises use these. The expansion in the use of smart devices and Internet penetration is creating huge amounts of dat

China Retail Sales of Consumer Goods: YoY: Year to Date: Above Designated Size Enterprise data was reported at 5.700 % in Mar 2025. This records an increase from the previous number of 4.300 % for Feb 2025. China Retail Sales of Consumer Goods: YoY: Year to Date: Above Designated Size Enterprise data is updated monthly, averaging 7.800 % from Feb 2011 (Median) to Mar 2025, with 156 observations. The data reached an all-time high of 43.900 % in Feb 2021 and a record low of -23.400 % in Feb 2020. China Retail Sales of Consumer Goods: YoY: Year to Date: Above Designated Size Enterprise data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under Global Database’s China – Table CN.HA: Retail Sales of Consumer Goods: Above Designated Size Enterprise: by Commodity . [COVID-19-IMPACT]

Retail Sales in the United States increased 0.50 percent in July of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This fully synthetic dataset simulates digital sales transactions, marketing channels, and customer feedback for an online store environment. It was designed to support hands-on exploration of sales analytics, marketing attribution, customer segmentation, and business intelligence workflows.

Highlights:

~3,000 transactions with realistic structure while preserving privacy.

Detailed features including product categories, pricing, quantities, calculated revenue.

Customer demographics (country, gender), marketing channels, sales platforms.

Customer feedback captured as Net Promoter Scores (NPS).

Ideal for practicing data cleaning, exploratory data analysis, dashboard design, and predictive modeling in marketing and e-commerce contexts.

File Information

File format: CSV

Total rows: 3,000

Columns: 15

Encoding: UTF-8

Delimiter: Comma (,)

Columns Overview

date: Date of purchase

first_name: Customer names

gender: Gender (Male/Female/Other)

product_name: Name of the digital product

category: Product category (e.g., Course, Template, Webinar)

price: Unit price

quantity: Number of units purchased

revenue: Total revenue = price × quantity

customer_country: Country of the customer

platform: Sales platform (e.g., ClickFunnels, Kajabi)

marketing_channel: Marketing source (e.g., Email, Organic Search)

nps_score: Customer Net Promoter Score (0–10)

The global consumer data storage devices market is projected to experience significant growth over the forecast period, 2025-2033. The market is anticipated to reach a value of XX million by 2033, expanding at a CAGR of XX% during the forecast period. The growth can be attributed to factors such as the increasing adoption of digital devices, the proliferation of data-intensive applications, and the growing need for data storage security. The market is segmented based on type and application. By type, the market is divided into hard disk drives (HDDs) and solid-state drives (SSDs). HDDs are traditional storage devices that use spinning disks to store data, while SSDs are newer devices that use flash memory to store data. SSDs are faster and more durable than HDDs, but they are also more expensive. The market is also segmented based on application, with online sales and offline sales being the two major segments. The online sales segment is expected to grow at a faster rate than the offline sales segment due to the increasing popularity of e-commerce. The major players in the consumer data storage devices market include IBM, Dell, HPE, Huawei, Western Digital, Seagate Technology, Lenovo, Toshiba, NetApp, Pure Storage, Kingston Technology, ADATA, Micron Technology, CMC Magnetics (Verbatim), RITEK Group, and Sony.

DES is publishing Statewide Contract (Master Contract) spend as data becomes available. The spend is reported by vendors and is reported by contract and customer. Includes OMWBE, Vet and Small Business status as well.