Yield optimization: A systemic, not static, approach

It’s easy to think that launching new yield optimization features will automatically make a commerce media program more money. In theory, they do– we wouldn’t build them otherwise.

The problem is, many conversations treat these features like a switch: as long as I raise price floors, enable quality scoring, and launch bid automation, incremental spend should follow. Once the code is live, the feature will immediately begin creating value.

In reality, commerce media networks are more like living organisms than on/off buttons. There is always a delicate balance between the three-sided ecosystem (commerce companies, advertisers, and consumers) that means each new feature requires a thoughtful rollout in order to drive meaningful results.

Optimization and growth can happen simply because a feature exists; however, when teams continuously analyze marketplace behavior, test hypotheses, measure outcomes, and refine their approach over time, this is when we start to see a real difference.

In this article, we’ll discuss our approach to yield optimization and how a systemic model can drive incremental revenue over time.

What yield optimization actually means

Traditionally, there are three main growth levers for any commerce media network.

• Supply: A network can add formats such as sponsored brands, display, or video, or increase the number of available placements. This is especially valuable when fill rates are high, and demand is strong enough to support more inventory.
• Demand: A network can add direct advertisers, long-tail advertisers, agencies, or programmatic demand. This can happen through managed service, self-service, sales activity, or supply-side platform integrations.
• Yield optimization: A network can make better decisions with the inventory and demand it already has.

Yield optimization makes every auction, bid, impression, inventory slot, and dollar of existing advertiser budget work harder.

We can affect yield optimization by doing things like adjusting price floors, improving relevancy, changing pacing logic, updating auction rules, or automating bids toward an advertiser’s desired outcome.

Done well, these changes can make hundreds of thousands of dollars of difference. They also compound, meaning each improvement strengthens the marketplace and creates better inputs for the next decision.

Over time, the program becomes more efficient, more competitive, and better able to identify where incremental revenue is available.

Every optimization starts with understanding the marketplace

It’s easy to assume that if you make one change, a predictable result will follow. Raise the minimum bid, and revenue should increase. Improve quality scoring, and click-through rate should improve. Turn on outcome automation, and advertisers should spend more efficiently.

But every action has a trickle-down effect across the marketplace.

A higher floor may increase the value of winning bids, but it may also lower fill in segments where competition is limited. A new quality score may improve relevancy, but it may interact with an existing publisher ranking model in unexpected ways. An automated bidding strategy may improve advertiser outcomes, but only if there is enough auction volume and conversion data to make informed decisions.

Before enabling an optimization, teams need to understand the conditions around it.

• Is demand actually constrained?
• Is there meaningful unspent advertiser budget?
• Is marketplace ROAS leaving room for additional monetization?
• Which auction segments consistently overperform?
• Where are advertisers already competing aggressively?
• Where is competition limited?
• Which advertiser cohorts are most sensitive to price changes?
• Which marketplace health metrics should never be sacrificed?

These questions determine where an optimization belongs, how it should be configured, and how aggressively it should be introduced.

This means as we approach yield optimization, before enabling the feature flag, we start with business analysis.

Features are only as strong as their configuration

Consider price floors.

At a basic level, a price floor raises the minimum bid required to compete for a specific piece of inventory. That sounds straightforward: a network increases the minimum bid, winning bids become more valuable, and revenue rises.

In practice, raising floors across an entire marketplace without understanding advertiser behavior can create the opposite result. Advertisers may stop entering certain auctions, fill rates may fall, and efficient campaigns may lose access to inventory. Spend may shift toward other placements or remain unused.

Recently, we increased price floors for one of our networks. The work involved much more than changing a configuration.

1. First, we identified where advertisers had excess budget and were consistently reaching performance goals. We reviewed acceptable ROAS ranges and separated advertisers into cohorts based on their behavior, performance, and sensitivity to price.
2. We then introduced one controlled change instead of applying the same increase everywhere.
3. Once the test was live, we monitored revenue, advertiser performance, fill, win rate, auction participation, and unspent budget. We expanded the change only where the data supported it, and we adjusted the strategy as new marketplace behavior emerged.

The analysis, testing, monitoring, and decision-making around the feature, not just the feature itself, created the result.

This is true across nearly every yield capability. The same quality score, bidding model, or pacing system can produce very different outcomes depending on where it is enabled, which signals it uses, and how closely the rollout is monitored.

Every optimization creates new decisions

Increasingly, we find yield optimization tests expose new opportunities. This means the output of an optimization can go beyond just new revenue and begin to ask a new set of questions.

• What changed in advertiser behavior?
• Which segments responded differently?
• Did the optimization create more competition or simply redistribute existing spend?
• Did it improve network revenue while maintaining advertiser performance?
• What should be tested next?

Optimization is iterative because the marketplace does not remain still. Advertisers adjust their bids and budgets, consumer demand changes, inventory expands, and seasonality changes auction dynamics.

A strong optimization process must account for that movement.

Testing and refinement: Because not every yield optimization option belongs everywhere

One of the most important principles in yield optimization is that every marketplace reaches maturity differently, and not every optimization technique belongs everywhere. A capability can be powerful in one network and have limited value in another.

Rather than turning on several optimization options, we’re focused on enabling the right capability, in the right part of the marketplace, at the right time.

This is where the analysis and the ability to test and integrate each one effectively matter. Our operating system includes a few consistent practices.

• Hypothesis: Teams begin with a definition of what they expect to happen and why.
• Measurement: They establish success criteria before launching the test. Revenue alone is rarely enough: depending on the optimization, success may also depend on ROAS, fill, click-through rate, conversion, unspent budget, advertiser retention, or shopper experience.
• Strategy: Teams isolate changes whenever possible. If pricing, ranking, and bidding logic all change at once, it becomes difficult to understand which action created the outcome.
• Monitoring: Before scaling, they ensure frequent proactive monitoring. A positive result in one category, placement, or advertiser cohort does not always translate across the entire marketplace.
• Guardrails: They roll back when necessary. A test that does not work is still useful when the team understands why.
• Documentation: Learnings should improve the next experiment rather than remain with the people who happened to run the first one.

The network does not need every decision to be correct on the first attempt; rather, it needs the ability to learn faster and apply those learnings more consistently.

Measurement is part of optimization

Measurement is sometimes treated as the final step. A feature launches, the team waits for results, and reporting determines whether it worked.

But for yield optimization, measurement has to begin before launch. Teams need a clear baseline with defined metrics of potential changes, what should remain stable, and the time period required to separate actual impact from normal marketplace variation.

This is difficult because attribution is not always obvious.

• Revenue may increase after a floor change, but the network also needs to understand whether that revenue came from genuinely higher auction value, a change in advertiser mix, seasonality, or a shift in inventory volume.
• Click-through rate may improve after launching a new quality score, but the network still needs to determine whether conversion and advertiser returns improved with it.
• Spend may rise under automated bidding, but the program needs to understand whether the increase is incremental or whether the automation simply moved budget between campaigns.

In some cases, a clean control group is available. In others, teams may need to use matched cohorts, holdouts, pre- and post-period analysis, or modeled estimates.

None of these methods are perfect in every situation. The goal is to define the strongest practical methodology before the change goes live.

Monitoring also needs to exist before launch. If a team cannot see auction participation, fill, pacing, advertiser performance, and revenue at the required level of detail, it cannot confidently evaluate the optimization.

Optimization requires product and program expertise

Yield optimization sits at the intersection of product, data science, operations, and commercial strategy.

A product team may understand how a price floor or ranking model functions. A program team may understand advertiser behavior, seasonal demand, category dynamics, and the operational realities of the network. A data science team may understand which signals are reliable and how much evidence is required before making a decision.

The strongest results come when these perspectives work together. This is why execution quality matters as much as feature availability. Two networks can have access to the same capability and produce very different outcomes based on how they analyze, test, and operate it.

Koddi’s approach combines commerce-first decisioning, real-time reporting, configurable auction logic, and dedicated program support. The platform makes optimization possible, while the process helps networks apply those capabilities based on their specific marketplace conditions.

As a result, the output compounds over time. Though individually, each improvement may look incremental, collectively, they create a stronger marketplace.