The "Digital Payments 2025 Dataset" is a synthetic dataset representing digital payment transactions across various payment applications in India for the year 2025. It captures monthly transaction data for multiple payment apps, including banks, UPI platforms, and mobile payment services, reflecting the growing adoption of digital payments in India. The dataset was created as part of a college project to simulate realistic transaction patterns for research, education, and analysis in data science, economics, and fintech studies. It includes metrics such as customer transaction counts and values, total transaction counts and values, and temporal data (month and year). The data is synthetic, generated using Python libraries to mimic real-world digital payment trends, and is suitable for academic research, teaching, and exploratory data analysis.

The population share with mobile internet access in India was forecast to continuously increase between 2024 and 2029 by in total 25 percentage points. After the fifteenth consecutive increasing year, the mobile internet penetration is estimated to reach 73.62 percent and therefore a new peak in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Bangladesh and Sri Lanka.

E-wallet is a technology that needs to be installed in the smartphone and allows customers to store money and do online transactions directly from the wallet whereas QR code works through a few banking apps, store apps to integrate debit/credit card details [1]. This research aims to present a data article on the factors influencing on E-wallet behavioral intention in a transitional economy. Data were obtained from 311 respondents in Da Nang city, Vietnam. First, the collected data was processed by SPSS software for descriptive statistical analysis. Then, Exploratory Factors Analysis and Regression analysis were used to determine the relationship between the research factors and E-wallet behavioral intention.

This dataset contains a collection of user reviews and ratings for the Livin' by Mandiri mobile application. Livin' by Mandiri is a digital financial service platform developed by Bank Mandiri, one of Indonesia's largest banks, offering features like payments, money transfers, and financial management on mobile devices for both Android and iOS users. The data was collected by scraping reviews from the Google Play Store, providing insights into user feedback and app performance.

Columns

The dataset is provided in a CSV file and includes the following columns: * date: The date when the user review was submitted, in datetime format. * review: The textual content of the user's review. * rating: The user's rating, on a scale of 1 to 5. * thumbs_up: The total number of 'thumbs up' or likes given by other users to that particular review. * version: The version of the app when the user submitted the review.

Distribution

The dataset is structured as a CSV file. It contains approximately 155,192 records, representing reviews submitted between 30 September 2021 and 24 December 2022.

Review counts show significant peaks at certain times: * 12/29/2021 - 01/07/2022: 16,439 reviews * 02/21/2022 - 03/02/2022: 11,191 reviews * 05/22/2022 - 05/31/2022: 10,247 reviews * 07/06/2022 - 07/15/2022: 10,477 reviews * 07/15/2022 - 07/24/2022: 11,011 reviews

Rating Distribution: * 4.92 - 5.00 (5-star equivalent): 86,215 reviews * 1.00 - 1.08 (1-star equivalent): 39,183 reviews * 3.96 - 4.04 (4-star equivalent): 10,951 reviews * 3.00 - 3.08 (3-star equivalent): 9,464 reviews * 1.96 - 2.04 (2-star equivalent): 9,379 reviews

Thumbs Up Distribution: * 0.00 - 47.94: 154,810 reviews (majority of reviews received low 'thumbs up' counts) * Higher counts are present but significantly less frequent, with a maximum of 2,397 thumbs up for a single review.

App Version Distribution: * 1.0.2: 28% of reviews * [null]: 24% of reviews (indicating no version information available for these reviews) * Other: 48% of reviews across various versions.

Usage

This dataset is ideal for: * Exploratory Data Analysis (EDA) to understand trends in user feedback. * Sentiment Analysis to gauge overall user satisfaction and identify emotional tones in reviews. * App performance monitoring and identifying areas for improvement based on user comments and ratings. * Market research into digital banking service perception in Indonesia. * Academic research on financial technology adoption and mobile app user behaviour.

Coverage

• Geographic Scope: The reviews are primarily from users in Indonesia, given that Bank Mandiri is a major Indonesian bank.
• Time Range: The data spans from 30 September 2021 to 24 December 2022.
• Demographic Scope: The dataset reflects feedback from users of the Livin' by Mandiri mobile app on Android and iOS devices.

License

CC-BY-NC

Who Can Use It

This dataset is beneficial for: * Data Analysts and Scientists: For performing EDA, sentiment analysis, and building predictive models related to user satisfaction. * App Developers and Product Managers: To understand user pain points, identify popular features, and guide future app updates. * Researchers: Studying digital finance, user experience, and mobile app ecosystems in emerging markets like Indonesia. * Business Intelligence Professionals: To inform strategic decisions based on customer feedback and market sentiment.

Dataset Name Suggestions

• Livin' by Mandiri App Reviews
• Indonesian Mobile Banking User Feedback
• Bank Mandiri Digital App Ratings
• Fintech App Reviews Indonesia
• Livin' App User Sentiment Data

Attributes

Original Data Source: Livin' by Mandiri App Reviews

Cryptocurrency payments are forecast to grow at a CAGR of nearly 17 percent between 2023 and 2030, although the market is relatively small. The forecast is according to a market estimate made in early 2023, based on various conditions and sources available at that time. It should be noted, however, that cryptocurrency used for payments is predicted to be a far smaller market than the predicted transaction value of CBDC, or the forecast market size of instant payments. Indeed, research from early 2023 across 40 countries suggested that the market share of cryptocurrency in e-commerce transaction was "less than one percent" in all survey countries, with predictions being this would not change in the future.

A dataset consisting of 751,500 English app reviews of 12 online shopping apps. The dataset was scraped from the internet using a python script. This ShoppingAppReviews dataset contains app reviews of the 12 most popular online shopping android apps: Alibaba, Aliexpress, Amazon, Daraz, eBay, Flipcart, Lazada, Meesho, Myntra, Shein, Snapdeal and Walmart. Each review entry contains many metadata like review score, thumbsupcount, review posting time, reply content etc. The dataset is organized in a zip file, under which there are 12 json files and 12 csv files for 12 online shopping apps. This dataset can be used to obtain valuable information about customers' feedback regarding their user experience of these financially important apps.

In today's digital landscape, data transparency and compliance are paramount. Organizations across industries are striving to maintain trust and adhere to regulations governing data privacy and security. To support these efforts, we present our comprehensive Ads.txt and App-Ads.txt dataset.

Key Benefits of Our Dataset:

• Coverage: Our dataset offers a comprehensive view of the Ads.txt and App-Ads.txt files, providing valuable information about publishers, advertisers, and the relationships between them. You gain a holistic understanding of the digital advertising ecosystem.
• Multiple Data Formats: We understand that flexibility is essential. Our dataset is available in multiple formats, including .CSV, .JSON, and more. Choose the format that best suits your data processing needs.
• Global Scope: Whether your business operates in a single country or spans multiple continents, our dataset is tailored to meet your needs. It provides data from various countries, allowing you to analyze regional trends and compliance.
  • Top-Quality Data: Quality matters. Our dataset is meticulously curated and continuously updated to deliver the most accurate and reliable information. Trust in the integrity of your data for critical decision-making.
  • Seamless Integration: We've designed our dataset to seamlessly integrate with your existing systems and workflows. No disruptions—just enhanced compliance and efficiency.

The Power of Ads.txt & App-Ads.txt: Ads.txt (Authorized Digital Sellers) and App-Ads.txt (Authorized Sellers for Apps) are industry standards developed by the Interactive Advertising Bureau (IAB) to increase transparency and combat ad fraud. These files specify which companies are authorized to sell digital advertising inventory on a publisher's website or app. Understanding and maintaining these files is essential for data compliance and the prevention of unauthorized ad sales.

How Can You Benefit? - Data Compliance: Ensure that your organization adheres to industry standards and regulations by monitoring Ads.txt and App-Ads.txt files effectively. - Ad Fraud Prevention: Identify unauthorized sellers and take action to prevent ad fraud, ultimately protecting your revenue and brand reputation. - Strategic Insights: Leverage the data in these files to gain insights into your competitors, partners, and the broader digital advertising landscape. - Enhanced Decision-Making: Make data-driven decisions with confidence, armed with accurate and up-to-date information about your advertising partners. - Global Reach: If your operations span the globe, our dataset provides insights into the Ads.txt and App-Ads.txt files of publishers worldwide.

Multiple Data Formats for Your Convenience: - CSV (Comma-Separated Values): A widely used format for easy data manipulation and analysis in spreadsheets and databases. - JSON (JavaScript Object Notation): Ideal for structured data and compatibility with web applications and APIs. - Other Formats: We understand that different organizations have different preferences and requirements. Please inquire about additional format options tailored to your needs.

Data That You Can Trust:

We take data quality seriously. Our team of experts curates and updates the dataset regularly to ensure that you receive the most accurate and reliable information available. Your confidence in the data is our top priority.

Seamless Integration:

Integrate our Ads.txt and App-Ads.txt dataset effortlessly into your existing systems and processes. Our goal is to enhance your compliance efforts without causing disruptions to your workflow.

In Conclusion:

Transparency and compliance are non-negotiable in today's data-driven world. Our Ads.txt and App-Ads.txt dataset empowers you with the knowledge and tools to navigate the complexities of the digital advertising ecosystem while ensuring data compliance and integrity. Whether you're a Data Protection Officer, a data compliance professional, or a business leader, our dataset is your trusted resource for maintaining data transparency and safeguarding your organization's reputation and revenue.

Get Started Today:

Don't miss out on the opportunity to unlock the power of data transparency and compliance. Contact us today to learn more about our Ads.txt and App-Ads.txt dataset, available in multiple formats and tailored to your specific needs. Join the ranks of organizations worldwide that trust our dataset for a compliant and transparent future.

Context

This dataset explores the relationship between digital behavior and mental well-being among 100,000 individuals. It records how much time people spend on screens, use of social media (including TikTok), and how these habits may influence their sleep, stress, and mood levels.

It includes six numerical features, all clean and ready for analysis, making it ideal for machine learning tasks like regression or classification. The data enables researchers and analysts to investigate how modern digital lifestyles may impact mental health indicators in measurable ways.

Dataset Applications

• Quantify how screen‑time, TikTok use, or multi‑platform engagement statistically relate to stress, sleep loss, and mood.
• Train regression or classification models that forecast stress level or mood score from real‑time digital‑usage metrics.
• Feed user‑specific data into recommender systems that suggest screen‑time caps or bedtime routines to improve mental health.
• Provide evidence for guidelines on youth screen‑time limits and platform moderation based on observed stress‑sleep trade‑offs.
• Serve as a teaching dataset for EDA, feature engineering, and model evaluation in data‑science or psychology curricula.
• Evaluate app interventions (e.g., screen‑time nudges) by comparing predicted versus actual post‑intervention stress or mood shifts.
• Cluster individuals into digital‑behavior personas (e.g., “heavy late‑night scrollers”) to tailor mental‑health resources.
• Generate synthetic time‑series scenarios (what‑if reductions in TikTok hours) to estimate downstream impacts on sleep and stress.
• Use engineered features (ratio of TikTok hours to total screen‑time, etc.) in broader wellbeing models that include diet or exercise data.
• Assess whether mental‑health prediction models remain accurate and unbiased across different screen‑time or platform‑use segments. # Column Descriptions
• screen_time_hours – Daily total screen usage in hours across all devices.
• social_media_platforms_used – Number of different social media platforms used per day.
• hours_on_TikTok – Time spent on TikTok daily, in hours.
• sleep_hours – Average number of sleep hours per night.
• stress_level – Stress intensity reported on a scale from 1 (low) to 10 (high).
• mood_score – Self-rated mood on a scale from 2 (poor) to 10 (excell # Inspiration This dataset was inspired by growing concerns about how screen time and social media affect mental health. It enables analysis of the links between digital habits, stress, sleep, and mood—encouraging data-driven solutions for healthier online behavior and emotional well-being. # Ethically Mined Data: This dataset has been ethically mined and synthetically generated without collecting any personally identifiable information. All values are artificial but statistically realistic, allowing safe use in academic, research, and public health projects while fully respecting user privacy and data ethics.

In financial year 2024, almost 164 billion digital payments were recorded across India. This was a significant increase compared to the previous three years.  Variety of digital payments  The total value of digital payments included large-scale interbank payments, such as Real Time Gross Settlement (RTGS) or National Electronic Funds Transfer (NEFT), as well as payments used by individuals, such as credit and debit cards. India’s mobile payment system, Unified Payments Interface (UPI), recorded strong gains, both in numbers and in value, since 2015. Thereby, it comes as no surprise that international key players, such as Google Pay or Amazon Pay, entered the market. Nevertheless, the most used app in 2021 was domestic app PhonePe.  COVID-19 effects  Since the beginning of the COVID-19 pandemic in India the number of digital payment transactions continued to grow. This was also true for the various methods of credit and debit transfers, including mobile payments through UPI. Nevertheless, the value of card payments and of large value credit transfers, such as RTGS, decreased considerably in financial year 2021.

The Measurable AI UberEats E-Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Taiwan, Japan, Australia) - Americas (United States, Mexico, Chile) - EMEA (United Kingdom, France, Italy, United Arab Emirates, AE, South Africa)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the UberEats food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

Drive for “digital financial inclusion” has been a key policy factor in India for the last decade. Payments Bank is one of the recent most important strategy factors to fulfill this objective. This paper briefly discusses the structure and functions of payments banks as conceptualized and implemented. The primary goal of payments bank was enhancing the range of digital financial inclusion by leveraging the user friendly app-based financial service platform to provide effective services in a more accessible form for low-income households and small business firms. This study attempts to understand the expectations of the policy makers regarding the role of these new financial entities and their opportunities and challenges. In the last few years after their introduction, payments banks have not been able to perform satisfactorily in the profitability front probably due to their huge initial infrastructural costs. However, it is expected that in the years ahead, payments banks are going to play a larger role in achieving financial inclusion in India. In this paper, we have discussed the performance of the payments banks in the last few years and the opportunities and challenges they are likely to face in the future years.

The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points). All of the Digital City Map (DCM) datasets are featured on the Streets App All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

This dataset contains a collection of user reviews and ratings for the Grab mobile application, extracted from the Google Store [1]. It offers valuable insights into public perception and user feedback regarding Grab's services, which include ride-hailing, food delivery, and digital payments across various Southeast Asian countries [1]. The data can be instrumental for analysing sentiment, identifying trends, and pinpointing user pain points over time [1].

Columns

• index: A numerical index for each review [2].
• review_text: The actual text content of the user review [2].
• review_rating: The rating given by the user, ranging from 1 to 5 stars [2].
• author_id: A unique identifier for the author of the review [2].
• author_name: The name of the review's author, which can include generic names like "A Google user" [2, 3].
• author_app_version: The version of the Grab application at the time the review was submitted [2].
• review_datetime_utc: The date and time (in UTC) when the review was published [2].
• review_likes: The number of likes a particular review received [2].

Distribution

The dataset is typically provided in CSV format [4]. It contains approximately 441,546 records or rows [3]. Specific file size information is not available in the provided materials.

Usage

This dataset is ideal for a variety of analytical tasks, including: * Extracting sentiments and trends: Understand the overall sentiment of users towards the Grab app and how it changes over time [1]. * Identifying app version performance: Determine which versions of the application received the most positive or negative feedback [1]. * Topic modelling: Discover common themes and pain points mentioned by users in their reviews [1]. * Product improvement: Inform product development and marketing strategies by leveraging user feedback. * Market research: Gain insights into user experiences in the ride-hailing and food delivery sectors.

Coverage

The dataset covers app reviews from the Google Store, pertaining to Grab's operations globally, specifically in countries like Singapore, Malaysia, Cambodia, Indonesia, Myanmar, the Philippines, Thailand, and Vietnam [1, 5]. The time range of the reviews spans from 31 May 2013 to 9 November 2023 [6]. Demographic scope is not explicitly detailed but represents a broad user base from regions where Grab operates.

License

CCO

Who Can Use It

• Data Scientists and Machine Learning Engineers: For building sentiment analysis models, topic models, or predictive analytics on user feedback.
• Product Managers: To understand user satisfaction, identify feature requests, and prioritise product improvements based on direct feedback.
• Marketing and PR Teams: To monitor brand perception, identify common complaints or praises, and tailor communication strategies.
• Business Analysts: For market research, competitive analysis, and understanding customer behaviour trends.
• Researchers: For academic studies on consumer behaviour, app usage, or natural language processing applications.

Dataset Name Suggestions

• Grab App User Reviews
• Google Play Grab Feedback
• Grab Application Ratings
• Grab Mobility & Delivery Reviews
• Southeast Asia Super-App Reviews

Attributes

Original Data Source: 🛵🥝 Grab App Reviews from Google Store

This dataset is having data of customers who buys clothes online. The store offers in-store style and clothing advice sessions. Customers come in to the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want.

The company is trying to decide whether to focus their efforts on their mobile app experience or their website.

This dataset contains customer reviews and ratings for the Shopee mobile application from the Google Play Store. Shopee Pte. Ltd. is a Singaporean multinational technology company specialising in e-commerce, operating as a subsidiary of Sea Limited. Launched in 2015 in Singapore, Shopee has since expanded globally and, as of 2021, is recognised as the largest e-commerce platform in Southeast Asia, attracting 343 million monthly visitors. It facilitates online purchasing and selling for consumers and sellers across East Asia and Latin America. This dataset is designed to offer a clear understanding of public perception and sentiment towards the Shopee app over an extended period.

Columns

• Index: A unique identifier for each review.
• review_text: The full text of the user's review.
• review_rating: The rating given by the user, on a scale of 1 to 5.
• author_id: A unique identifier for the author of the review.
• author_name: The display name of the review's author.
• author_app_version: The version of the Shopee application used by the author at the time of the review.
• review_datetime_utc: The date and time (in UTC) when the review was posted.
• review_likes: The number of likes received by the review.

Distribution

The dataset is typically provided in a CSV file format and is structured as tabular data. It contains approximately 782,000 records. Specific file size details are not available.

Usage

This dataset is invaluable for gaining insight into public opinion regarding the Shopee app over time. It can be used for various analytical purposes, including: * Extracting sentiments and identifying evolving trends in user feedback. * Determining which versions of the app elicited the most positive or negative feedback. * Applying topic modelling techniques to pinpoint specific pain points or common issues reported by users, and many more analytical applications.

Coverage

The dataset primarily covers app reviews from the Google Play Store for the Shopee application. While Shopee operates globally across Southeast Asia, East Asia, and Latin America, the dataset title suggests a focus on reviews from Singaporean users. The time range for the reviews spans from 22 June 2015 to 13 November 2023. The data reflects feedback from mobile app users who submitted reviews during this period.

License

CC-BY-SA

Who Can Use It

This dataset is suitable for a wide range of users, including: * Data Analysts and Market Researchers to understand consumer behaviour and sentiment. * Product Managers and App Developers for identifying user needs, improving app features, and addressing pain points. * Businesses and E-commerce Platforms seeking competitive analysis or insights into customer satisfaction in the online retail sector. * Academics and Students for research in natural language processing (NLP), sentiment analysis, and consumer studies.

Dataset Name Suggestions

• Shopee Google Play App Reviews
• Singapore Shopee App User Feedback
• Shopee Mobile App Ratings Dataset
• Google Play Shopee Review Analysis
• Shopee E-commerce App Reviews

Attributes

Original Data Source: 🇸🇬 Shopee App Reviews from Google Store

The Measurable AI Temu & Fast Fashion E-Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan, Thailand, Malaysia, Vietnam, Indonesia, Singapore, Hong Kong, Phillippines) - EMEA (Spain, United Arab Emirates, Saudi, Qatar) - Latin America (Brazil, Mexico, Columbia, Argentina)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more - Email ID (can work out user overlap with peers and loyalty)

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

The Measurable AI Rappi alternative Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our alternative data to produce actionable consumer insights for use cases such as: - User overlap between players - Market share analysis - User behavioral traits (e.g. retention rates, spending patterns) - Average order values - Promotional strategies used by the key players - Items ordered (SKU level data) Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - LATAM (Brazil, Mexico, Argentina, Colombia, Chile)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more - MAIDs

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the Rappi food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact michelle@measurable.ai for a data dictionary and to find out our volume in each country.

The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points). All of the Digital City Map (DCM) datasets are featured on the Streets App All previously released versions of this data are available at BYTES of the BIG APPLE- Archive Updates for this dataset, along with other multilayered maps on NYC Open Data, are temporarily paused while they are moved to a new mapping format. Please visit https://www.nyc.gov/site/planning/data-maps/open-data/dwn-digital-city-map.page to utilize this data in the meantime.

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.

Using Factori Consumer Data graph you can solve use cases like:

Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

Lookalike Modeling

Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

TagX Web Browsing Clickstream Data: Unveiling Digital Behavior Across North America and EU Unique Insights into Online User Behavior TagX Web Browsing clickstream Data offers an unparalleled window into the digital lives of 1 million users across North America and the European Union. This comprehensive dataset stands out in the market due to its breadth, depth, and stringent compliance with data protection regulations. What Makes Our Data Unique?

Extensive Geographic Coverage: Spanning two major markets, our data provides a holistic view of web browsing patterns in developed economies. Large User Base: With 300K active users, our dataset offers statistically significant insights across various demographics and user segments. GDPR and CCPA Compliance: We prioritize user privacy and data protection, ensuring that our data collection and processing methods adhere to the strictest regulatory standards. Real-time Updates: Our clickstream data is continuously refreshed, providing up-to-the-minute insights into evolving online trends and user behaviors. Granular Data Points: We capture a wide array of metrics, including time spent on websites, click patterns, search queries, and user journey flows.

Data Sourcing: Ethical and Transparent Our web browsing clickstream data is sourced through a network of partnered websites and applications. Users explicitly opt-in to data collection, ensuring transparency and consent. We employ advanced anonymization techniques to protect individual privacy while maintaining the integrity and value of the aggregated data. Key aspects of our data sourcing process include:

Voluntary user participation through clear opt-in mechanisms Regular audits of data collection methods to ensure ongoing compliance Collaboration with privacy experts to implement best practices in data anonymization Continuous monitoring of regulatory landscapes to adapt our processes as needed

Primary Use Cases and Verticals TagX Web Browsing clickstream Data serves a multitude of industries and use cases, including but not limited to:

Digital Marketing and Advertising:

Audience segmentation and targeting Campaign performance optimization Competitor analysis and benchmarking

E-commerce and Retail:

Customer journey mapping Product recommendation enhancements Cart abandonment analysis

Media and Entertainment:

Content consumption trends Audience engagement metrics Cross-platform user behavior analysis

Financial Services:

Risk assessment based on online behavior Fraud detection through anomaly identification Investment trend analysis

Technology and Software:

User experience optimization Feature adoption tracking Competitive intelligence

Market Research and Consulting:

Consumer behavior studies Industry trend analysis Digital transformation strategies

Integration with Broader Data Offering TagX Web Browsing clickstream Data is a cornerstone of our comprehensive digital intelligence suite. It seamlessly integrates with our other data products to provide a 360-degree view of online user behavior:

Social Media Engagement Data: Combine clickstream insights with social media interactions for a holistic understanding of digital footprints. Mobile App Usage Data: Cross-reference web browsing patterns with mobile app usage to map the complete digital journey. Purchase Intent Signals: Enrich clickstream data with purchase intent indicators to power predictive analytics and targeted marketing efforts. Demographic Overlays: Enhance web browsing data with demographic information for more precise audience segmentation and targeting.

By leveraging these complementary datasets, businesses can unlock deeper insights and drive more impactful strategies across their digital initiatives. Data Quality and Scale We pride ourselves on delivering high-quality, reliable data at scale:

Rigorous Data Cleaning: Advanced algorithms filter out bot traffic, VPNs, and other non-human interactions. Regular Quality Checks: Our data science team conducts ongoing audits to ensure data accuracy and consistency. Scalable Infrastructure: Our robust data processing pipeline can handle billions of daily events, ensuring comprehensive coverage. Historical Data Availability: Access up to 24 months of historical data for trend analysis and longitudinal studies. Customizable Data Feeds: Tailor the data delivery to your specific needs, from raw clickstream events to aggregated insights.

Empowering Data-Driven Decision Making In today's digital-first world, understanding online user behavior is crucial for businesses across all sectors. TagX Web Browsing clickstream Data empowers organizations to make informed decisions, optimize their digital strategies, and stay ahead of the competition. Whether you're a marketer looking to refine your targeting, a product manager seeking to enhance user experience, or a researcher exploring digital trends, our cli...