Anna VintsevskaNot all traffic that leaves SSPs contributes to revenue. Many SSPs underestimate the damage unproductive requests can cause to infrastructure and long-term revenue potential. Across the entire chain, additional charges to the QPS bill and DSP rejections result in weakened performance, wasted infrastructure resources, and shrinking margins.
Working closely with SSPs, we notice them wasting money on non-monetizable queries. Too often, these costs are overlooked until they grow into significant revenue leakage. ML-powered tools can turn things around, reshaping SSPs’ approach to optimization.
More traffic doesn't always mean more value
In the past, many sellers could rely on consistent growth by focusing solely on volume. More requests mean more bidding opportunities, which means more potential revenue. However, as the programmatic landscape shifts focus from quantity to quality, the opposite can be true. Requests that don’t meet buyer demand are wasted:
Request duplication. The same impression can be sent multiple times due to parallel connections or misconfigured integrations. Each duplicate request forces DSP to process and discard identical information, lowering QPS limits and reducing engagement with SSP. Instead of focusing resources on quality signals, SSP servers are busy managing huge volumes of traffic without any potential.
Irrelevant traffic. The requests are instantly discarded since they have no match with DSP’s active campaigns. Compared to targeted traffic that is aligned to campaign demand, it generates no income and harms the SSP-DSP relationships.
Poor frequency and pacing rules. Requests may be clustered too tightly, overwhelming DSP systems without increasing win probability.
Lack of geo, device, or content alignment. When requests don’t match the demand for buyers’ campaign requirements, DSP rejects traffic. Not only do these requests waste bandwidth and processing power, but they also make DSPs lose trust in the quality of SSP’s traffic.
An SSP might believe it is scaling traffic, but what’s really happening is an inflation of requests with declining yield.
Where SSPs silently lose money
Unoptimized traffic doesn’t just fail to generate revenue. Instead of driving monetization, you’re just running up your QPS bill, turning a core resource into an unnecessary expense, affecting long-term sustainability.
Server load and infrastructure costs
Imagine an SSP sending 10 billion requests per day, but only 20% are monetizable. The remaining 8 billion requests still consume server resources, bandwidth, and database storage (all of which you have to pay for). Furthermore, it carries network traffic costs and partner fees, blocking access to higher-value bids that could have driven revenue.
DSP rejection
Once SSPs send low-value requests, DSPs start pushing back. DSPs automatically discard requests that don’t meet their criteria. For instance, requests missing required fields (like device ID) or requests from regions where the DSP has no demand. If DSPs see consistently poor traffic quality from an SSP, they may deprioritize the partner’s traffic in favor of a cleaner one.
For the SSP, this means fewer bids and lower win rates. The longer-term effect is reputational damage, which directly impacts revenue potential.
Margin loss
Even if overall revenue remains stable, the cost of servicing low-value traffic combined with missed opportunities for higher-paying impressions jeopardizes profitability. In practice, SSPs may appear to maintain revenue, but their margins suffer since resources are spent on requests that generate no return.
Smarter auctions start with smarter traffic
SSPs should not have to choose between scale and efficiency. With the right tools, they can achieve both, turning traffic management into a competitive advantage. We’ve developed ML-powered solutions that go beyond static business rules and dynamically adapt to demand behavior.
Instead of flooding DSPs with raw, unfiltered requests, traffic shaping applies smart decision-making at the SSP level. By analyzing historical and real-time performance, our models intelligently filter out incoming bid traffic, ensuring only the highest-quality impressions enter the auction. Dynamic traffic shaping directs traffic toward the most profitable routes, maximizing SSP revenue since it uses infrastructure more efficiently.
Through floor pricing and filtering based on domain, placement ID, or integration type, traffic shaping highlights only the impressions most likely to generate meaningful bids. This is especially important when DSPs operate under strict QPS limits.
Using ML algorithms, the Query Volume Optimizer calibrates request flow in real time. It learns DSP thresholds and adjusts request frequency accordingly, ensuring the “right-sized” volume of high-quality traffic and protecting relationships with DSPs.
One of our clients — a US-based monetization platform for publishers — faced monetization issues despite high traffic volumes. Due to low-quality requests, DSP partners were overwhelmed, and the performance was also suffering.
After migrating to the white-label SSP, a built-in ML-powered traffic-shaping mechanism dynamically filtered out underperforming, non-monetizable bid requests before they reached DSPs. Within 10 days, the client saw a 32% increase in bid rates and a 27% improvement in overall revenue efficiency.
By focusing on quality, the client recovered lost margin and built a more sustainable monetization model. They could finally see that their growth problem wasn’t a lack of traffic but a lack of optimization.
Wrapping up
Non-optimized SSP traffic may seem invisible, but its costs are real. SSPs can no longer afford to treat traffic optimization as an afterthought. Those who filter and shape traffic effectively will create stronger demand for their inventory.
Ad tech is now moving toward fewer but stronger players who invest in optimization to shape traffic more intelligently. The next stage of programmatic advertising will be defined by efficient, reliable traffic that is not only attractive to DSPs but also to publishers seeking trusted partners.
Combining ML-powered tools, the industry is in a strong position to cut through the bidstream mess and achieve greater efficiency and performance. By aligning requests to actual demand, both publishers and advertisers can safeguard their position in the supply chain, shifting from volume-driven to value-driven strategies.
