How bid shading drives cost efficiency for advertisers in a world without ad IDs

Bid shading and predictive pricing algorithms are becoming crucial for DSPs optimizing this process.

Bid shading is an advanced strategy DSPs use to optimize bidding in first-price auctions where the highest bidder wins the ad placement and pays the exact amount of their bid. Using sophisticated algorithms, bid shading predicts market-clearing prices to allow bids just above the expected market rates, ensuring advertisers do not overpay for ad placements while securing premium inventory.

This approach offers several key benefits. Primarily, it drives cost efficiencies for advertisers when executed correctly, which enhances overall campaign performance. The achieved savings can be reinvested into additional impressions or higher-quality placements, leading to more effective advertising outcomes.

Adapting bid shading without ad IDs

Currently, bidders rely heavily on ad IDs to determine the value of impressions and make informed bids. However, as the digital advertising industry evolves, DSPs must adapt their bid pricing strategies to maintain efficiency and performance. There are three forward-thinking strategies advertisers can consider to navigate this transition effectively.

The first strategy is to leverage the availability and type of ad IDs as crucial signals for bid shading decisions. DSPs are increasing their differentiation between high-fidelity, specific ad IDs and less specific or anonymous traffic. Analysis shows that unrecognized inventory often clears for lower prices across all performance bands compared to recognized inventory. Therefore, a tailored approach with assertive bid shading for non-ID-tied inventory and a cautious strategy for recognized inventory is recommended.

Another strategy for advertisers that’s becoming essential, is to rely on contextual features to drive relevance. DSPs leverage advanced AI technologies such as convolutional neural networks and large language models to analyze visual and textual content. These technologies provide deeper contextual insights and enhance the pricing model’s ability to understand and precisely navigate the competitive bidding landscape.

The final strategy is developing alternative methods. DSPs are exploring innovative methods like using cohorts and the multiple proposed non-cookie APIs. Effective bidding in emerging auction types requires implementing knowledge distillation techniques to reduce the size of deep learning models and developing hybrid models that combine deep learning with parametric approaches. These strategies ensure efficient bidding while minimizing memory and computing power requirements.

A DSP with a proven track record can help future-proof advertising strategies 

As advertisers seek to implement these strategies, choosing a DSP with a proven track record in bid shading is at the top of their list. Some DSPs offer bid shading as a standard service at no additional cost as a commitment to their customers and value delivery. To establish this expertise, they can inquire about algorithm sophistication, transparency and ability to optimize for cost efficiency. 

A DSP that harnesses advanced AI techniques and unique insights from shopping and entertainment signals to optimize bid shading and win rate models would be highly valuable. By employing algorithms that bid based on the predicted value of an impression — accurately pricing bids to create a surplus between the advertiser’s bid and the winning price — DSPs can help advertisers derive maximum value from third-party supply.

For example, with the latest generation of AI-powered enhancements to Amazon DSP over the last year, including bid shading, advertisers are seeing a 14% improvement in ROAS for third-party inventory. This results in 36–40% cost efficiencies, delivering 1.6 times more impressions than campaigns without bid shading.

Once advertisers identify their clear objectives and KPIs, they should communicate those to their DSP for tailored bid shading strategies that match their goals. And, by maintaining open communication and engaging in regular check-ins and feedback sessions, advertisers will ensure that bid shading algorithms are optimized for their needs and market conditions.

As programmatic advertising evolves away from deterministic bid requests and ad IDs, using ML models to learn bid price landscapes will remain a pivotal strategy for advertisers to maintain strong ROAS. As this transition unfolds, it’ll be crucial for advertisers to leverage a strong DSP that will continue innovating and refining bid shading techniques to ensure the delivery and performance advertisers expect.

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