As the connected TV (CTV) / broadcaster video on demand (BVOD) industry in Australia increasingly embraces mediation, an unsung hero is emerging that plays a critical role in driving success for both buyers and publishers: traffic shaping.
Mediation platforms help publishers optimise ad inventory by connecting them with multiple demand sources to maximise fill rates and revenue potential through a streamlined bidding process. Once firmly in the domain of web publishers, mediation in CTV and BVOD means that publishers and broadcasters are no longer bound by working with just one demand partner.
As they start to explore working with multiple partners, what’s becoming apparent is that the real differentiator lies in how various Sell-Side Platforms (SSPs) manage traffic shaping through their machine learning algorithms.
What is Traffic Shaping and Why Does It Matter?
Traffic shaping is the process through which SSPs manage and prioritise ad requests to optimise performance for both buyers and publishers. This involves analysing the available ad inventory, understanding the demand from various Demand-Side Platforms (DSPs), and selecting which ad requests to prioritise based on factors like bid value, relevance, and audience targeting.
The key thing to understand is that not every impression makes it all the way through to the DSPs – all SSPs throttle the impressions they send through.
Let’s use the following example: Suppose an SSP receives 50 billion impressions across multiple publishers within a defined set of time and sends the corresponding bid requests to 100 DSPs without any traffic shaping. This would lead to an unmanageable 5 trillion potential bid requests and a massive waste of infrastructure, as ultimately, only one DSP can win each auction.
This scenario also places a huge burden on DSPs’ infrastructure – so they impose limits on the number of requests an SSP can send, these are known as queries per second (QPS) limits. This means SSPs need to decide which traffic to prioritise sending to each DSP. The goal is to send a given DSP only the most relevant or valuable impressions, to increase the likelihood of that DSP bidding and the impression being filled – resulting in higher win rates and improved inventory quality for buyers, and better yield for publishers.
However, what one SSP deems valuable or relevant to a DSP may not be what another SSP deems valuable or relevant.
This is where the effectiveness of an SSP’s algorithm comes into play…
The Role of SSP Algorithms
Manual optimisation can go only so far, as it usually revolves around broad, static constructs such as geography, platform, publisher, etc. It doesn’t do justice to the complexity and dynamic nature of real-time bidding. This is where traffic shaping powered by AI/machine learning, comes in.
Each SSP has its own algorithm for traffic shaping, which is influenced by a number of factors, such as the strength of its engineering teams and infrastructure, tenure in market, relationships with DSPs and addressability coverage. In the context of CTV, traffic shaping takes on additional complexity due to the fragmented nature of the ecosystem. Different devices, platforms, and viewing habits make it difficult to optimise ad delivery without highly sophisticated algorithms that understand the nuances of a given market.
PubMatic’s traffic shaping technology works at the impression level to send fewer, more strategic bid requests. PubMatic does this by leveraging machine learning to analyse and learn from previous bid request activity, which enables our platform to predict whether a given DSP would either not be interested in the impression or would be bidding too low to win in the auction, depending on the current mix of ad campaigns the DSP has.
Our AI-powered traffic shaping models make use of impression and bid-level features to generate traffic shaping recommendations and apply them in real-time. Machine learning enables our traffic-shaping models to continuously learn and adapt, evolving recommendations throughout the day as bid patterns change.
Some SSPs are more adept at understanding specific CTV landscapes than others and have algorithms that are better aligned with the needs of both buyers and publishers. This can lead to very different outcomes for both parties, even when comparing like-for-like deals.
A PubMatic Case Study
With its long-standing presence in the Australian market and leadership in AI and machine learning, PubMatic’s platform frequently outperforms rival SSPs to deliver superior CTV audience reach for buyers and improved monetisation for broadcasters.
This was recently put to the test by Foxtel on its Binge streaming platform. Using Publica’s mediation layer, Foxtel duplicated an existing CTV deal on PubMatic and saw an increased audience reach of 31% vs. the alternative SSP.
This is because the ad requests that PubMatic surfaced for the DSP were more relevant – they generated approximately 69% more responses (bid rate) and resulted in 205% more wins (win rate) than the other SSP – or to put it another way, PubMatic’s traffic shaping algorithm was 69% more effective at understanding what impressions and audience that DSP deemed valuable.
And here’s why this really matters… Increasing audience reach on the same pool of inventory leads to big benefits for both publishers/broadcasters and buyers. In this case, the buyer saw improved campaign efficiency – they were able to get their campaign delivered faster, while Foxtel was able to maximise the value of its inventory – by making a broader audience available to the buyer and having this campaign delivered more efficiently, Foxtel was able to allocate remaining inventory to other campaigns and in the longer run, accept more bookings – leading to more revenue generation.
Conclusion
As the CTV market continues to grow, the importance of traffic shaping will only increase. Buyers need to reach their target audiences in the most efficient manner possible, and publishers need to maximise their revenue opportunities. SSPs like PubMatic, with superior algorithms tailored to specific CTV landscapes, will play an increasingly pivotal role in ensuring both buyers and publishers achieve their goals. In the new world of CTV mediation, it’s not just about connecting buyers and sellers—it’s about using the right platform, with the right traffic shaping strategy.
With cutting edge machine learning engineering teams, control over our infrastructure, significant investments in addressability and decades long integrations with leading DSPs, PubMatic’s platform has been proven to perform for buyers and sellers time and time again.
To find out more about how you can leverage PubMatic’s platform to help your deals perform better, reach out to your PubMatic rep.
