As of current, the digital advertising world is heavily focused and has been dependent on the use of third-party cookies that will soon depreciate. Ad tech companies have built their software around third-party cookies to allow brands to distribute personalized experiences and messages to their audiences. Third-party cookies have been used as determining signals to pinpoint, discover, target, and measure user activity across domains and devices. Amidst the rapid increase in new tech devices and digital environments, it's become significantly more challenging for brands to understand and sufficiently interact with customers to provide the best experience. With the dawn of a cookieless future on the horizon, marketers should learn to utilize first-party identifiers to continue to target and measure ad performance efficiently and adequately accurately.
First-party user IDs were created to provide identification capabilities and enable campaign strategies without ever leveraging the use of third-party cookies. In a recent report by Advertiser Perceptions, 58% of advertisers reported they began to expand their first-party data collection capabilities, and 85% were in the beginning stages of discussing this issue with their ad tech and marketing partners to address this obstacle. However, it's essential to recognize that not all first-party IDs serve the same use or are created similarly.
How you interact and build your customers' profiles can significantly affect the marketing strategy, influence data privacy, and strengthen customer relationships. Currently, we are being presented with new identity resolution methods that have emerged to aid brands in accommodating users across domains and services. One that's getting attention in the tech realm is Hashed Emails. Now, we must ask whether this first-party method is scalable, and if so, is it the future of digital?
Hashed Emails
The digital industry has claimed that hashed emails will soon be the future of marketing. Apple and now Google's announcement of removing third-party cookies by the end of 2023 leaves companies looking for alternative strategies to identify and understand their customers and potential prospects. Could this be the time for hashed emails to rise to the occasion? The Hashed emails methodology uses Deterministic Matching to leverage first-party data that customers have provided to bring together device-level data to individual customer profiles with 100% confidence.
Hashed emails as a method are superior to third-party cookies because it uses Identity Resolution capabilities to track users' engagement by logging into a website, social media, or any other type of platform in real-time. All while keeping the user's experience anonymous and secure, protecting the individual's privacy. Cross-browser and device user reconciliation only occur when a typical PII has been shared (e.g., when a user has logged on two different domains or two other devices), focusing on accuracy over scale.
The challenge, however, with Hashed Emails and Deterministic Matching is that the data is hard to come by. On average, the amount of logged-in users is low, limiting the available deterministic signals, therefore, the scale that a solely deterministic solution can offer. Many ad tech systems often can't match identities because an individual isn’t logged in to an email address or any other piece of deterministic data, making identities unavailable. Some digital websites in the US are reporting ID coverage to be as low as single-digit percentages. In addition, if a user's email is made visible to a company in client-side requests without lawful privacy procedures established, it can inevitably be discovered and used by malicious actors. We're left wondering if this is our only option to combat and address targeting adequately without making any extreme changes.
The Future of Digital
Another rising option to consider as 3P cookies begin to vanish is Probabilistic Predictions based on Deep Behavioral Data methods. For a long time, Deep Behavioral Data has been overlooked for its ability to pull data generated by, or in response to, a customer’s engagement in a digital environment.
The need for Deep Behavioral Data is upon us. Through the innovations of technology, we’re now able to identify the underlying tech of probabilistic predictions allowing for a much more extensive range of your user base. This method will enable companies to generate maximum data from visitors in a scalable way. Deep Behavioral Data differs from other probabilistic predictions, having the ability to achieve accuracy based on the in-depth collection of data gathered from numerous events on one page. This process poses to be accurate in providing both scale and accuracy as well as protecting the privacy of users. As third-party data begins dissipating, we must ask ourselves how we will transition forward? Could Deep Behavioral Data be the answer to our question?
One of the first companies to pioneer the way for Deep Behavioral Data, is Kahoona. Our disruptive 1P data activation platform brings audience data to life in a way that’s accurate and scalable without compromising user privacy or performance, using probabilistic predictions. By doing so, increase user monetization and optimize ROI while protecting your assets from financial risks. While waiting for the depreciation of the third-party cookies can seem dreadful, analyzing user interactions to accurately subdivide your user audience and offer real-time personalization, on the other hand, is triumphant.
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Hashed Emails and Probabilistic Predictions - Say Goodbye to 3P Cookies
8/23/2022
As of current, the digital advertising world is heavily focused and has been dependent on the use of third-party cookies that will soon depreciate. Ad tech companies have built their software around third-party cookies to allow brands to distribute personalized experiences and messages to their audiences. Third-party cookies have been used as determining signals to pinpoint, discover, target, and measure user activity across domains and devices. Amidst the rapid increase in new tech devices and digital environments, it's become significantly more challenging for brands to understand and sufficiently interact with customers to provide the best experience. With the dawn of a cookieless future on the horizon, marketers should learn to utilize first-party identifiers to continue to target and measure ad performance efficiently and adequately accurately.
First-party user IDs were created to provide identification capabilities and enable campaign strategies without ever leveraging the use of third-party cookies. In a recent report by Advertiser Perceptions, 58% of advertisers reported they began to expand their first-party data collection capabilities, and 85% were in the beginning stages of discussing this issue with their ad tech and marketing partners to address this obstacle. However, it's essential to recognize that not all first-party IDs serve the same use or are created similarly.
How you interact and build your customers' profiles can significantly affect the marketing strategy, influence data privacy, and strengthen customer relationships. Currently, we are being presented with new identity resolution methods that have emerged to aid brands in accommodating users across domains and services. One that's getting attention in the tech realm is Hashed Emails. Now, we must ask whether this first-party method is scalable, and if so, is it the future of digital?
Hashed Emails
The digital industry has claimed that hashed emails will soon be the future of marketing. Apple and now Google's announcement of removing third-party cookies by the end of 2023 leaves companies looking for alternative strategies to identify and understand their customers and potential prospects. Could this be the time for hashed emails to rise to the occasion? The Hashed emails methodology uses Deterministic Matching to leverage first-party data that customers have provided to bring together device-level data to individual customer profiles with 100% confidence.
Hashed emails as a method are superior to third-party cookies because it uses Identity Resolution capabilities to track users' engagement by logging into a website, social media, or any other type of platform in real-time. All while keeping the user's experience anonymous and secure, protecting the individual's privacy. Cross-browser and device user reconciliation only occur when a typical PII has been shared (e.g., when a user has logged on two different domains or two other devices), focusing on accuracy over scale.
The challenge, however, with Hashed Emails and Deterministic Matching is that the data is hard to come by. On average, the amount of logged-in users is low, limiting the available deterministic signals, therefore, the scale that a solely deterministic solution can offer. Many ad tech systems often can't match identities because an individual isn’t logged in to an email address or any other piece of deterministic data, making identities unavailable. Some digital websites in the US are reporting ID coverage to be as low as single-digit percentages. In addition, if a user's email is made visible to a company in client-side requests without lawful privacy procedures established, it can inevitably be discovered and used by malicious actors. We're left wondering if this is our only option to combat and address targeting adequately without making any extreme changes.
The Future of Digital
Another rising option to consider as 3P cookies begin to vanish is Probabilistic Predictions based on Deep Behavioral Data methods. For a long time, Deep Behavioral Data has been overlooked for its ability to pull data generated by, or in response to, a customer’s engagement in a digital environment.
The need for Deep Behavioral Data is upon us. Through the innovations of technology, we’re now able to identify the underlying tech of probabilistic predictions allowing for a much more extensive range of your user base. This method will enable companies to generate maximum data from visitors in a scalable way. Deep Behavioral Data differs from other probabilistic predictions, having the ability to achieve accuracy based on the in-depth collection of data gathered from numerous events on one page. This process poses to be accurate in providing both scale and accuracy as well as protecting the privacy of users. As third-party data begins dissipating, we must ask ourselves how we will transition forward? Could Deep Behavioral Data be the answer to our question?
One of the first companies to pioneer the way for Deep Behavioral Data, is Kahoona. Our disruptive 1P data activation platform brings audience data to life in a way that’s accurate and scalable without compromising user privacy or performance, using probabilistic predictions. By doing so, increase user monetization and optimize ROI while protecting your assets from financial risks. While waiting for the depreciation of the third-party cookies can seem dreadful, analyzing user interactions to accurately subdivide your user audience and offer real-time personalization, on the other hand, is triumphant.
