| Market Capitalization (Millions $) |
289 |
| Shares
Outstanding (Millions) |
90 |
| Employees |
301 |
| Revenues (TTM) (Millions $) |
173 |
| Net Income (TTM) (Millions $) |
-5 |
| Cash Flow (TTM) (Millions $) |
-6 |
| Capital Exp. (TTM) (Millions $) |
6 |
Adtheorent Holding Company Inc
Founded in 2012, we are a digital media platform which focuses on performance-first, privacy-forward methods to execute programmatic digital advertising campaigns, serving both advertising agency and brand customers. Without relying on individualized profiles or sensitive personal data for targeting, we utilize machine learning and advanced data analytics to make programmatic digital advertising more effective and efficient at scale, delivering measurable real-world value for advertisers. Our differentiated advertising capabilities and superior campaign performance, measured by customer-defined business metrics or Key Performance Indicators ('KPIs'), have helped fuel our customer adoption and year-after-year growth.
We use machine learning and advanced data science to organize, analyze and operationalize non-sensitive data to deliver real-world value for customers. Central to our ad-targeting and campaign optimization methods, we build custom machine learning models for each campaign using historic and real-time data to predict future consumer conversion actions for every digital ad impression. We have integrations with Ad Exchanges/Supply Side Platforms (SSPs), from which we are sent ad impression opportunities to evaluate and purchase. We predictively score all of these ad impression opportunities for the purpose of deciding which ad impressions will likely drive valuable conversions or engagement activity for our customers. Our predictive platform scores over one million digital ad impressions per second and 75 billion to 90 billion digital ad impressions per day, assigning a 'predictive score' to each. Each predictive score is determined by correlating non-personal data attributes associated with the particular impression with data corresponding to previously purchased impressions that yielded consumer conversion or engagement activity. Such non-individualized attributes include variables such as publisher, content and URL keywords, device make, device operating system and other device attributes, ad position, geographic data, weather, demographic signals, creative type and size, etc. The 'predictive scores' generated by our platform allow us and our advertising clients to determine which ad impressions are more likely or less likely to result in client-desired KPIs. Our machine learning models are customized for every campaign and our platform 'learns' over the course of each campaign as it processes more data related to data attributes and actual conversion experience. Based on these statistical probabilities or 'predictive scores,' our platform automatically determines bidding optimizations to drive conversions and advertiser ROI or 'return on ad spend' ('ROAS'), bidding on less than .001 of the evaluated impressions. Our use of machine learning and data science helps us to maximize efficiency and performance, enabling our customers to avoid wasted ad spend related to suboptimal impressions such as impressions that are predicted to be at a greater risk for fraud/invalid traffic or impressions with a higher likelihood of being unviewable, unmeasurable, and not brand safe, among other factors.
Our capabilities extend across the digital ecosystem to identify and engage digital actors with the highest likelihood of completing customer-desired actions, including online sales, other online actions, and real-world actions such as physical location visitation, in-store sales or vertical specific KPIs such as prescription fills/lift or submitted credit card applications. Our custom and highly impactful campaign executions encompass popular digital screens ' mobile, desktop, tablet, connected TV ('CTV') ' and all digital ad formats, including display, rich media, video, native and streaming audio. We actively manage our digital supply to provide advertisers with scale and reach, while minimizing redundant inventory, waste and other inefficiencies. Our CTV capability delivers scale and reach supplemented by innovative and industry recognized machine-learning optimizations towards real-world actions and value-added measurement services.
Our platform and machine learning-based targeting provide privacy advantages that are lacking from alternatives which rely on individual user profiles or cookies employing a 'one-to-one' approach to digital ad targeting. Our targeting approach is statistical, not individualized, and as a result we do not need to compile or maintain user profiles, and we do not rely on cookies or user profiles for targeting. Our solution-set is especially valuable to regulated customers, such as financial institutions and pharmaceutical companies, and other privacy-forward advertisers who desire efficient and effective digital ad-targeting without individualized or personal targeting data. We adhere to data usage protocols and model governance processes which help to ensure that each customer's data is safeguarded and used only for that customer's benefit, and we take a consultative and collaborative approach to data use best practices with all of our customers.
Company Address: 330 Hudson Street New York 10013 NY
Company Phone Number: 804-1359 Stock Exchange / Ticker: NASDAQ ADTH
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