Company Domain to IP Address Linkage Database | Company IP Data | Monthly Feed | 3M+ Domains to IP Connections

Elevate your business strategy with our premier "Company Domain to IP Address Linkage Database," a meticulously crafted dataset that connects over 3 million company domains directly to their corresponding IP addresses. Updated monthly, this essential resource is designed for organizations seeking to enhance their cybersecurity measures, refine digital marketing strategies, and streamline B2B communications with unparalleled accuracy and efficiency.

Optional Add-On: MAID to Business Domains Dataset Enhance your dataset with our optional add-on, the "MAID to Business Domains Dataset." This valuable extension provides a linkage between Mobile Advertising IDs (MAIDs) and company domains, offering a deeper layer of insight for targeted marketing and precise audience engagement strategies. Ideal for businesses looking to leverage mobile data for enhanced targeting and analytics.

Key Features:

Comprehensive Coverage: Gain access to a vast database of over 3 million company domains linked to their respective IP addresses, covering a wide spectrum of industries and global regions. Premium Data Quality: Enjoy the highest standards of data integrity with our dataset, meticulously maintained and regularly refreshed to ensure the most accurate and up-to-date information. Seamless Integration: Effortlessly integrate our database into your existing tech stack, enhancing your cybersecurity, CRM, and marketing automation tools with rich, actionable data.

Benefits:

Enhanced Digital Marketing: Use precise domain-to-IP mappings to refine your digital marketing efforts, ensuring content and ads reach the right corporate audiences, significantly boosting B2B engagement. Robust Cybersecurity: Strengthen your cybersecurity framework by utilizing detailed domain and IP information for improved network monitoring and threat detection. Strategic Insights: Draw on our extensive dataset for market analysis, competitor tracking, and customer behavior insights, informing strategic decisions across your organization. Efficient B2B Communication: Identify and engage corporate networks more effectively, optimizing B2B outreach and operational efficiency. Applications:

Targeted B2B Engagement: Equip your marketing and sales teams with detailed data, allowing for highly targeted content delivery and advertising to specific corporate networks. Advanced Security Measures: Leverage the dataset for comprehensive network monitoring and access management, enhancing your cybersecurity posture. Informed Strategic Planning: Utilize deep insights from domain and IP linkages for market analysis and business intelligence, driving forward-looking strategies. Operational Excellence: Streamline the identification and engagement process with corporate networks, improving efficiency and response rates in B2B communication. The "Company Domain to IP Address Linkage Database" is a foundational tool for any business aiming to excel in the digital age, offering critical data for improving digital marketing, cybersecurity, and strategic planning. Available exclusively on Datarade.ai, this database stands at the forefront of digital innovation.

Consider enhancing your strategic toolkit further with our "MAID to Business Domains Dataset" add-on, a powerful resource for businesses focused on maximizing the impact of mobile marketing and engagement.

This competition involves advertisement data provided by BuzzCity Pte. Ltd. BuzzCity is a global mobile advertising network that has millions of consumers around the world on mobile phones and devices. In Q1 2012, over 45 billion ad banners were delivered across the BuzzCity network consisting of more than 10,000 publisher sites which reach an average of over 300 million unique users per month. The number of smartphones active on the network has also grown significantly. Smartphones now account for more than 32% phones that are served advertisements across the BuzzCity network. The "raw" data used in this competition has two types: publisher database and click database, both provided in CSV format. The publisher database records the publisher's (aka partner's) profile and comprises several fields:

publisherid - Unique identifier of a publisher. Bankaccount - Bank account associated with a publisher (may be empty) address - Mailing address of a publisher (obfuscated; may be empty) status - Label of a publisher, which can be the following: "OK" - Publishers whom BuzzCity deems as having healthy traffic (or those who slipped their detection mechanisms) "Observation" - Publishers who may have just started their traffic or their traffic statistics deviates from system wide average. BuzzCity does not have any conclusive stand with these publishers yet "Fraud" - Publishers who are deemed as fraudulent with clear proof. Buzzcity suspends their accounts and their earnings will not be paid

On the other hand, the click database records the click traffics and has several fields:

id - Unique identifier of a particular click numericip - Public IP address of a clicker/visitor deviceua - Phone model used by a clicker/visitor publisherid - Unique identifier of a publisher adscampaignid - Unique identifier of a given advertisement campaign usercountry - Country from which the surfer is clicktime - Timestamp of a given click (in YYYY-MM-DD format) publisherchannel - Publisher's channel type, which can be the following: ad - Adult sites co - Community es - Entertainment and lifestyle gd - Glamour and dating in - Information mc - Mobile content pp - Premium portal se - Search, portal, services referredurl - URL where the ad banners were clicked (obfuscated; may be empty). More details about the HTTP Referer protocol can be found in this article. Related Publication: R. J. Oentaryo, E.-P. Lim, M. Finegold, D. Lo, F.-D. Zhu, C. Phua, E.-Y. Cheu, G.-E. Yap, K. Sim, M. N. Nguyen, K. Perera, B. Neupane, M. Faisal, Z.-Y. Aung, W. L. Woon, W. Chen, D. Patel, and D. Berrar. (2014). Detecting click fraud in online advertising: A data mining approach, Journal of Machine Learning Research, 15, 99-140.

Parameters list and corresponding values.