Bid shading and predictive pricing algorithms are becoming increasingly important for DSPs that optimize this process.

Bid shading is an advanced strategy that DSPs use to optimize bids in first-price auctions, where the highest bidder gets the ad placement and pays the exact amount of their bid. Using sophisticated algorithms, bid shading predicts fair market prices to enable bidding just above expected market prices, ensuring advertisers don’t overpay for ad placements while securing premium inventory.

This approach offers several important benefits. First and foremost, when implemented correctly, it results in cost savings for advertisers, which improves overall campaign performance. The savings achieved can be reinvested in additional impressions or higher quality placements, leading to more effective advertising results.

Adjust 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-looking strategies advertisers can consider to effectively navigate this transition.

The first strategy is to use the availability and type of ad IDs as key signals for bid shading decisions. DSPs are increasingly differentiating between highly accurate, specific ad IDs and less specific or anonymous traffic. Analytics show that unrecognized inventory is often priced lower than recognized inventory across all performance bands, so a tailored approach is recommended with assertive bid shading for non-ID-bound inventory and a cautious strategy for recognized inventory.

Another strategy that is becoming increasingly important for advertisers is to rely on contextual features to drive relevance. DSPs use 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 improve the pricing model’s ability to understand and accurately navigate the competitive bidding landscape.

The final strategy is to develop alternative methods. DSPs are exploring innovative methods such as the use of cohorts and the numerous proposed cookie-free APIs. Effective bidding in new 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 compute requirements.

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

When advertisers want 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 clients and as a way to add value. To demonstrate this expertise, they can ask about algorithm complexity, transparency and ability to optimize for cost efficiency.

A DSP that leverages advanced AI techniques and unique insights from shopping and entertainment signals to optimize bid shading and win rate models would be extremely valuable. By using algorithms that bid based on the predicted value of an impression—bids are priced precisely to create a surplus between the advertiser’s bid and the winning price—DSPs can help advertisers extract maximum value from third-party offerings.

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

Once advertisers have identified their clear goals and KPIs, they should communicate these to their DSP to develop tailored bid shading strategies that align with their goals. And by maintaining open communication and participating in regular check-ins and feedback sessions, advertisers ensure that bid shading algorithms are optimized for their needs and market conditions.

As programmatic advertising continues to move away from deterministic bid requests and ad IDs, using ML models to learn bid price landscapes remains a key strategy for advertisers to maintain strong ROAS. During this transition, it will be critical for advertisers to leverage a strong DSP that continues to develop innovative and refined bid shading techniques to ensure the delivery and performance advertisers expect.

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