Sponsored By MediaGo * January 27, 2025 *
Peter Jinfeng Pan is the head of MediaGo.
In digital marketing, advertisers should evaluate each paid impression by asking themselves the following five questions: Is it a real user or not? Will this user see the advertisement? What type of content does the user like? What is the user’s intent? What value can this user add?
The marketing funnel is built around these five questions. Advertisers should ignore impressions that are negative or uncertain. Otherwise, they can justify their investment. While ad placement used to rely on broad demographic data in the past, today’s privacy-conscious landscape makes it more difficult to answer these questions.
The solution to this challenge is to shift the focus away from user data and towards optimizing ad experience through deep learning.
Adopting a user-centric approach refocuses the ad placement to media, context, creativity and products – which serve inherently as signals instead of demographic data – making them key variables for optimizing advertising effectiveness. Deep learning models can intelligently infer relationships by training them on a wide range of signals.
Deep learning (DL) models are able to answer the five questions above at every impression auction. Records show that DL is a transformative tool, enabling advertisers to navigate complexity through the analysis of vast datasets and identifying intricate pattern. This capability ensures unmatched efficiency and precision, even in a world increasingly shaped by privacy priorities.
From AI to machine-learning to deep learning
Today’s advertisers are faced with a plethora of media opportunities, driven by users who have diverse interests and intentions. It is like searching for the perfect match in a sea of screws and nuts.
When faced with this ocean challenge, however, traditional AI is only able to divide the ocean into different regions and rely on humans to extract features and identify possible matches within each area.
DL uses deep neural networks that are trained on billions and billions of datapoints, which surpasses traditional AI in terms of computational ability. It can, for instance, find the best match across an entire ocean in just milliseconds. This offers unparalleled speed and accuracy in advertising.
Contextual Targeting with Deep Learning leads to privacy-compliant precision targeting
DL’s core strength lies in its ability of processing and extracting meaningful information from vast datasets and diverse datasets. This makes it a powerful tool for data analysis and decision making.
In a world with limited user data, contextual targeting has become an alternative that is privacy-compliant. While the amount of data processed through contextual targeting is not as large as traditional targeting, its real-time performance is crucial.
DL’s multi-layer networks can handle complex user data, such as dwell time, engagement patterns, and contextual information. This allows for the completion of ad matching and bidding in milliseconds.
Deep-learning-powered predictive bidding increases campaign performance
Balancing budget pacing with high-quality ads has been a challenge for the advertising industry for a long time. The traditional bidding methods, which rely on simple model, fail to address the trade-off. DL revolutionizes the predictive bidding process by analyzing large datasets in real time to uncover complex patterns between user interaction data.
Advertisers can accurately assess ad performance, user attention, and intent while dynamically adjusting bids, allocating budget to ads that are more likely to convert. Predictive bidding powered by DL leads to improved campaign performance, increasing conversion rates, and reducing CPA. This results in a better balance between budget pacing, and ad-quality.
DL overcomes limitations of the traditional model which relies on basic demographic and behavior data. DL models reveal subtle patterns and similarities that are missed by traditional methods. This allows advertisers to target highly relevant audiences who are similar to their best customers. DL transforms lookalike models into a powerful growth tool by leveraging deep insights about data relationships.
By analyzing media data, Deep Learning accurately identifies invalid traffic and assesses its value. This protects advertisers’ budgets and ensures brand safety by directing their spending towards genuine, high-valued traffic.
DL improves creative optimization through a deeper analysis of ad elements, such as images and videos. This allows for better data-driven creative optimizing. It identifies subtle patterns within creative content and uncovers what resonates with specific audiences.
Deep learning applications in real-world increase ROAS, campaign volumes and CVR.
MediaGo’s advanced models demonstrate some of the significant improvements DL brings to advertising campaign performance. These include improved user journey predictions, increased traffic quality and optimized bidding strategy. By accurately measuring traffic value, invalid traffic can be reduced to less that 10% of the average industry. By leveraging media and historical information for real-time insights, the average viewable exposure rate increases by 20 percent. CTR and CVR increase by 15 and 40 percent respectively. DL can dynamically adjust bids based upon real-time data. This results in an average 35% increase of ROAS. The combined impact of these models is evident in real world results. MediaGo’s DL model was used by a global digital agency to achieve an 111% increase in campaign volumes while maintaining a stable ROAS. Another agency saw a similar increase in campaign volume. CVR increases by 170% with an 8.8% increase in ROAS.
The deep learning revolution is rewriting the DNA of advertising for a new structure
The deep learning revolution is rebuilding the foundation of advertising. Unlike the old spray and pray paradigm, DL introduces systems that can decode hyper-granular audiences.
DL’s ability to atomize advertising operations is the true revolution: Collapsing campaigns into dynamic strategies that reconfigures creatives, bidding parameter and channel allocations all in real-time — while ensuring strict compliance with privacy.
The DL models discussed here are a great example of this transformation in action. These models map cross channel user journeys and provide precise context-aware matching, effectively collapsing the strategy development and implementation into an AI-driven continuum. Deep learning is the future of advertising.
Sponsored by MediaGo
https://digiday.com/?p=570432
