Envestnet®| Yodlee®'s Consumer 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

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

During a consumer survey conducted in the United States in the first quarter of 2021, it was found that the majority of respondents, namely 56 percent, were willing to give out their gender in exchange for receiving personalized ads or offers from companies. Nearly 21 percent of survey participants expressed the will to share their household income for the same purpose.

Listing of all purchase orders and contracts issued to procure goods and/or services within City-Parish. In the City-Parish, a PO/Contract is made up of two components: a header and one or many detail items that comprise the overarching PO/Contract. The header contains information that pertains to the entire PO/Contract. This includes, but is not limited to, the total amount of the PO/Contract, the department requesting the purchase and the vendor providing the goods or services. The detail item(s) contain information that is specific to the individual item ordered or service procured through the PO/Contract. The item/service description, item/service quantity and the cost of the item is located within the PO/Contract details. There may be one or many detail items on an individual PO/Contract. For example, a Purchase Order for a computer equipment may include three items: the computer, the monitor and the base software package. Both header information and detail item information are included in this dataset in order to provide a comprehensive view of the PO/Contract data. The Record Type field indicates whether the record is a header record (H) or detail item record (D). In the computer purchase example from above, the system would display 4 records – one header record and 3 detail item records. It should be noted header information will be duplicated on all detail items. No detail item information will be displayed on the header record. ***In October of 2017, the City-Parish switched to a new system used to track PO/Contracts. This data contains all PO/Contracts entered in or after October 2017. For prior year data, please see the Legacy Purchase Order dataset https://data.brla.gov/Government/Legacy-Purchase-Orders/54bn-2sqf

In today’s rapidly evolving digital landscape, understanding consumer behavior has never been more crucial for businesses seeking to thrive. Our Consumer Behavior Data database serves as an essential tool, offering a wealth of comprehensive insights into the current trends and preferences of online consumers across the United States. This robust database is meticulously designed to provide a detailed and nuanced view of consumer activities, preferences, and attitudes, making it an invaluable asset for marketers, researchers, and business strategists.

Extensive Coverage of Consumer Data Our database is packed with thousands of indexes that cover a broad spectrum of consumer-related information. This extensive coverage ensures that users can delve deeply into various facets of consumer behavior, gaining a holistic understanding of what drives online purchasing decisions and how consumers interact with products and brands. The database includes:

Product Consumption: Detailed records of what products consumers are buying, how frequently they purchase these items, and the spending patterns associated with these products. This data allows businesses to identify popular products, emerging trends, and seasonal variations in consumer purchasing behavior. Lifestyle Preferences: Insights into the lifestyles of consumers, including their hobbies, interests, and activities. Understanding lifestyle preferences helps businesses tailor their marketing strategies to resonate with the values and passions of their target audiences. For example, a company selling fitness equipment can use this data to identify consumers who prioritize health and wellness.

Product Ownership: Information on the types of products that consumers already own. This data is crucial for businesses looking to introduce complementary products or upgrades. For instance, a tech company could use product ownership data to target consumers who already own older versions of their gadgets, offering them incentives to upgrade to the latest models.

Attitudes and Beliefs: Insights into consumer attitudes, opinions, and beliefs about various products, brands, and market trends. This qualitative data is vital for understanding the emotional and psychological drivers behind consumer behavior. It helps businesses craft compelling narratives and brand messages that align with the values and beliefs of their target audience.

The State Contract and Procurement Registration System (SCPRS) was established in 2003, as a centralized database of information on State contracts and purchases over $5000. eSCPRS represents the data captured in the State's eProcurement (eP) system, Bidsync, as of March 16, 2009. The data provided is an extract from that system for fiscal years 2012-2013, 2013-2014, and 2014-2015

Data Limitations:
Some purchase orders have multiple UNSPSC numbers, however only first was used to identify the purchase order. Multiple UNSPSC numbers were included to provide additional data for a DGS special event however this affects the formatting of the file. The source system Bidsync is being deprecated and these issues will be resolved in the future as state systems transition to Fi$cal.

Data Collection Methodology:

The data collection process starts with a data file from eSCPRS that is scrubbed and standardized prior to being uploaded into a SQL Server database. There are four primary tables. The Supplier, Department and United Nations Standard Products and Services Code (UNSPSC) tables are reference tables. The Supplier and Department tables are updated and mapped to the appropriate numbering schema and naming conventions. The UNSPSC table is used to categorize line item information and requires no further manipulation. The Purchase Order table contains raw data that requires conversion to the correct data format and mapping to the corresponding data fields. A stacking method is applied to the table to eliminate blanks where needed. Extraneous characters are removed from fields. The four tables are joined together and queries are executed to update the final Purchase Order Dataset table. Once the scrubbing and standardization process is complete the data is then uploaded into the SQL Server database.

Secondary/Related Resources:

• State Contract Manual (SCM) vol. 2 http://www.dgs.ca.gov/pd/Resources/publications/SCM2.aspx
• State Contract Manual (SCM) vol. 3 http://www.dgs.ca.gov/pd/Resources/publications/SCM3.aspx
• Buying Green http://www.dgs.ca.gov/buyinggreen/Home.aspx
• United Nations Standard Products and Services Code, http://www.unspsc.org/

Use our Best Buy products to collect ratings, prices, and descriptions about products from an e-commerce online web. You can purchase either the entire dataset or a customized subset, depending on your requirements. The Best Buy Products Dataset stands as a comprehensive resource for businesses, researchers, and analysts aiming to navigate the vast array of products offered by Best Buy, a leading retailer in consumer electronics and technology. Tailored to provide a deep understanding of Best Buy's e-commerce ecosystem, this dataset facilitates market analysis, pricing optimization, customer behavior comprehension, and competitor assessment. At its core, the dataset encompasses essential attributes such as product ID, title, descriptions, ratings, reviews, pricing details, and seller information. These fundamental data elements empower users to glean insights into product performance, customer sentiment, and seller credibility, thereby facilitating informed decision-making processes. Whether you're a retailer looking to enhance your product portfolio, a researcher investigating trends in consumer electronics, or an analyst seeking to refine e-commerce strategies, the Best Buy Products Dataset offers a valuable resource for uncovering opportunities and driving success in the ever-evolving landscape of retail.

United States CSI: Home Buying Conditions data was reported at 140.000 1966=100 in May 2018. This records a decrease from the previous number of 144.000 1966=100 for Apr 2018. United States CSI: Home Buying Conditions data is updated monthly, averaging 149.000 1966=100 from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 182.000 1966=100 in Dec 1998 and a record low of 37.000 1966=100 in Nov 1981. United States CSI: Home Buying Conditions data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house?

Context

The Customer Shopping Preferences Dataset offers valuable insights into consumer behavior and purchasing patterns. Understanding customer preferences and trends is critical for businesses to tailor their products, marketing strategies, and overall customer experience. This dataset captures a wide range of customer attributes including age, gender, purchase history, preferred payment methods, frequency of purchases, and more. Analyzing this data can help businesses make informed decisions, optimize product offerings, and enhance customer satisfaction. The dataset stands as a valuable resource for businesses aiming to align their strategies with customer needs and preferences. It's important to note that this dataset is a Synthetic Dataset Created for Beginners to learn more about Data Analysis and Machine Learning.

Content

This dataset encompasses various features related to customer shopping preferences, gathering essential information for businesses seeking to enhance their understanding of their customer base. The features include customer age, gender, purchase amount, preferred payment methods, frequency of purchases, and feedback ratings. Additionally, data on the type of items purchased, shopping frequency, preferred shopping seasons, and interactions with promotional offers is included. With a collection of 3900 records, this dataset serves as a foundation for businesses looking to apply data-driven insights for better decision-making and customer-centric strategies.

Dataset Glossary (Column-wise)

• Customer ID - Unique identifier for each customer
• Age - Age of the customer
• Gender - Gender of the customer (Male/Female)
• Item Purchased - The item purchased by the customer
• Category - Category of the item purchased
• Purchase Amount (USD) - The amount of the purchase in USD
• Location - Location where the purchase was made
• Size - Size of the purchased item
• Color - Color of the purchased item
• Season - Season during which the purchase was made
• Review Rating - Rating given by the customer for the purchased item
• Subscription Status - Indicates if the customer has a subscription (Yes/No)
• Shipping Type - Type of shipping chosen by the customer
• Discount Applied - Indicates if a discount was applied to the purchase (Yes/No)
• Promo Code Used - Indicates if a promo code was used for the purchase (Yes/No)
• Previous Purchases - The total count of transactions concluded by the customer at the store, excluding the ongoing transaction
• Payment Method - Customer's most preferred payment method
• Frequency of Purchases - Frequency at which the customer makes purchases (e.g., Weekly, Fortnightly, Monthly)

Structure of the Dataset

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Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic customer shopping experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world customer shopping behavior. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation in the context of consumer preferences and retail scenarios.

Cover Photo by: Freepik

Thumbnail by: Clothing icons created by Flat Icons - Flaticon

Context

You get many visitors to your website every day, but you know only a small percentage of them are likely to buy from you, while most will perhaps not even return. Right now you may be spending money to re-market to everyone, but perhaps we could use machine learning to identify the most valuable prospects?

Content

This data set represents a day's worth of visit to a fictional website. Each row represents a unique customer, identified by their unique UserID. The columns represent feature of the users visit (such as the device they were using) and things the user did on the website in that day. These features will be different for every website, but in this data a few of the features we consider are: - basket_add_detail: Did the customer add a product to their shopping basket from the product detail page? - sign_in: Did the customer sign in to the website? - saw_homepage: Did the customer visit the website's homepage? - returning_user: Is this visitor new, or returning?

In this data set we also have a feature showing whether the customer placed an order (ordered), which is what we predict on.

United States CSI: Home Buying Conditions: Bad Time to Buy data was reported at 29.000 % in May 2018. This records an increase from the previous number of 27.000 % for Apr 2018. United States CSI: Home Buying Conditions: Bad Time to Buy data is updated monthly, averaging 24.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 79.000 % in Nov 1981 and a record low of 7.000 % in Dec 1998. United States CSI: Home Buying Conditions: Bad Time to Buy data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house?

United States CSI: Home Buying Conditions: Bad Time: Can't Afford data was reported at 7.000 % in May 2018. This records an increase from the previous number of 5.000 % for Apr 2018. United States CSI: Home Buying Conditions: Bad Time: Can't Afford data is updated monthly, averaging 8.000 % from Feb 1978 (Median) to May 2018, with 467 observations. The data reached an all-time high of 19.000 % in Aug 2011 and a record low of 1.000 % in Jul 1999. United States CSI: Home Buying Conditions: Bad Time: Can't Afford data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house? Responses to the query 'Why do you say so?'

customer_id: Unique ID of the customer. age: The age of the customer. annual_income: The customer's annual income (in USD). purchase_amount: The total amount of purchases made by the customer (in USD). purchase_frequency: Frequency of customer purchases (number of times per year). region: The region where the customer lives (North, South, East, West). loyalty_score: Customer's loyalty score (a value between 0-100).

This dataset includes information on customer profiles and their purchasing behaviors. The data features columns for user ID, age, annual income, purchase amount, loyalty score (categorized into classes), region, and purchase frequency. It is intended for analyzing customer segmentation and loyalty trends, and can be used for various machine learning and data analysis tasks related to customer behavior and market research.

Explanation: These data are imaginary data. It was created entirely for the purpose of improving users, it has nothing to do with reality.

The emergence of new media and the shift away from traditional media toward digital services has particularly prompted a change in media buying strategies. Since almost all companies are undergoing a digital transformation, media buying agencies must specialize in online advertising to adapt to the changing media landscape. Data-driven insights and programmatic advertising have propelled the industry forward. With rising consumer spending and corporate profit, businesses increasingly pour more resources into advertising to capture larger market shares. Media buying agencies have been riding this wave, capitalizing on the surging demand. Media Buying Agencies revenue has increased at a CAGR of 3.3% to a total of $13.8 billion in 2025, including an estimated 1.9% in the current year, while profit reaches 6.5%. The industry has witnessed rapid transformation driven by digital innovation and shifting consumer behaviors. Advertisers have gravitated toward digital platforms, spurred by the drastic transition from traditional media. This shift resulted in digital spending overtaking traditional media investments, with giants like Facebook, Google, and Amazon capturing significant market shares. The emergence of programmatic ad buying and data analytics has revolutionized how agencies target audiences, allowing for more precise and efficient campaigns. Amid this evolution, consolidation among major players like Omnicom and WPP has heightened competition, pushing smaller firms toward niche markets or out of the industry altogether. These dynamics have underscored the importance of adapting to technological advancements and economic changes to remain competitive. Over the next five years, businesses are poised to increase their advertising budgets to capitalize on rising consumer activity, providing significant opportunities for media buying agencies. The phase-out of third-party cookies and increasing emphasis on first-party data will drive agencies to focus on privacy-compliant strategies, while AI-driven programmatic advertising will continue to transform the industry. Agencies will expand services, offering integrated, multi-channel strategies and leveraging influencer marketing to tap into niche markets. The expansion of digital platforms has given access to niche markets that were harder to reach in the past. Companies increasingly turn to media buying agencies to seek integrated marketing solutions that harness cross-platform potential, driving revenue growth. Nonetheless, the proliferation of digital ad space, declining prices and waning demand for traditional advertising will limit industry growth. Overall, industry revenue is poised to hike at a CAGR of 1.8% to $15.1 billion in 2030.

United States CSI: Home Buying Conditions: Relative: Time data was reported at 3.000 % in May 2018. This records a decrease from the previous number of 8.000 % for Apr 2018. United States CSI: Home Buying Conditions: Relative: Time data is updated monthly, averaging -6.000 % from Feb 1978 (Median) to May 2018, with 467 observations. The data reached an all-time high of 13.000 % in Feb 2000 and a record low of -27.000 % in Jul 1982. United States CSI: Home Buying Conditions: Relative: Time data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house? Responses to the query 'Why do you say so?'

CSI: Vehicle Buying Conditions: Bad Time: Can't Afford data was reported at 4.000 % in May 2018. This records an increase from the previous number of 3.000 % for Apr 2018. CSI: Vehicle Buying Conditions: Bad Time: Can't Afford data is updated monthly, averaging 5.000 % from Feb 1978 (Median) to May 2018, with 484 observations. The data reached an all-time high of 22.000 % in Oct 2011 and a record low of 0.000 % in Feb 2000. CSI: Vehicle Buying Conditions: Bad Time: Can't Afford data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H034: Consumer Sentiment Index: Vehicle Buying Conditions. Response to the query: 'Why do you say so? The question was: Speaking of the automobile market -- do you think the next 12 months or so will be a good time or a bad time to buy a car?

United States CSI: Home Buying Conditions: Good Time: Times Good data was reported at 14.000 % in May 2018. This records a decrease from the previous number of 15.000 % for Apr 2018. United States CSI: Home Buying Conditions: Good Time: Times Good data is updated monthly, averaging 4.000 % from Feb 1978 (Median) to May 2018, with 467 observations. The data reached an all-time high of 17.000 % in Nov 2017 and a record low of 0.000 % in Jan 2011. United States CSI: Home Buying Conditions: Good Time: Times Good data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house? Responses to the query 'Why do you say so?'

Sample purchasing data containing information on suppliers, the products they provide, and the projects those products are used for. Data created or adapted from publicly available sources.

We collect, validate, model, and segment raw data signals from over 900+ sources globally to deliver thousands of mobile audience segments. We then combine that data with other public and private data sources to derive interests, intent, and behavioral attributes. Our proprietary algorithms then clean, enrich, unify and aggregate these data sets for use in our products. We have categorized our audience data into consumable categories such as interest, demographics, behavior, geography, etc. Audience Data Categories:Below mentioned data categories include consumer behavioral data and consumer profiles (available for the US and Australia) divided into various data categories. Brand Shoppers:Methodology: This category has been created based on the high intent of users in terms of their visits to Brand outlets in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Place Category Visitors:Methodology: This category has been created based on the high intent of users visiting specific places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Demographics:This category has been created based on deterministic data that we receive from apps based on the declared gender and age data. Marital Status, Education, Party affiliation, and State residency are available in the US. Geo-Behavioural:This category has been created based on the high intent of users in terms of the frequency of their visits to specific granular places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Interests:This segment is created based on users' interest in a specific subject while browsing the internet when the visited website category is clearly focused on a specific subject such as cars, cooking, traveling, etc. We use a deterministic model to assign a proper profile and time that information is valid. The recency of data can range from 14 to 30 days, depending on the topic. Intent:Factori receives data from many partners to deliver high-quality pieces of information about users’ shopping intent. We collect data from sources connected to the eCommerce sector and we also receive data connected to online transactions from affiliate networks to deliver the most accurate segments with purchase intentions, such as laptops, mobile phones, or cars. The recency of data can range from 7 to 14 days depending on the product category. Events:This category was created based on the high interest of users in terms of content related to specific global events - sports, culture, and gaming. Among the event segments, we also distinguish categories related to the interest in certain lifestyle choices and behaviors. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. App Usage:Mobile category is a branch of the taxonomy that is dedicated only to the data that is based on mobile advertising IDs. It is based on the categorization of the mobile apps that the user has installed on the device. Auto Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of automobile and other automotive attributes via a survey or registration. These audiences are currently available in the USA. Motorcycle Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of motorcycle and other motorcycle-based attributes via a survey or registration. These audiences are currently available for the USA. Household:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on users' declaring their marital status, parental status, and the overall number of children via a survey or registration. These audiences are currently available in the USA. Financial:Consumer Profiles - Available for the US and Australia this audience has been created based on their behavior in different financial services like property ownership, mortgage, investing behavior, and wealth and declaring their estimated net worth via a survey or registration. Purchase/ Spending Behavior:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on their behavior in different spending behaviors in different business verticals available in the USA. Clusters:Consumer Profiles - Available for the US and AustraliaClusters are groups of consumers who exhibit similar demographic, lifestyle, and media consumption characteristics, empowering marketers to understand the unique attributes that comprise their most profitable consumer segments. Armed with this rich data, data scientists can drive analytics and modeling to power their brand’s unique marketing initiatives. B2B Audiences;Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring their employee credentials, designations, and companies they work in, further specifying business verticals, revenue breakdowns, and headquarters locations. Customizable Audiences Data Segment:Brands can choose the appropriate pre-made audience segments or ask our data experts about creating a custom segment that is precisely tailored to your brief in order to reach their target customers and boost the campaign's effectiveness. Location Query Granularity:Minimum area: HEX 8Maximum area: QuadKey 17/City

The Abt-Buy dataset for entity resolution derives from the online retailers Abt.com and Buy.com. The dataset contains 1081 entities from abt.com and 1092 entities from buy.com as well as a gold standard (perfect mapping) with 1097 matching record pairs between the two data sources. The common attributes between the two data sources are: product name, product description and product price.

The dataset was initially published in the repository of the Database Group of the University of Leipzig: https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolution

To enable the reproducibility of the results and the comparability of the performance of different matchers on the Abt-Buy matching task, the dataset was split into fixed train, validation and test sets. The fixed splits are provided in the CompERBench repository:

http://data.dws.informatik.uni-mannheim.de/benchmarkmatchingtasks/index.html