Reactive vs. Predictive: The Publisher’s Guide to Price Floor Optimization with Opti Digital

Look, as a publisher, you've probably had about a million conversations about price floors. For example, whether they're worth the trouble, how to test them, and if they're too risky to roll out at scale. It makes sense, of course. Ad tech tools always come with big promises, but often publishers are left wondering if they'll ever see meaningful returns, let alone quickly.

That's why I sat down for a candid conversation with Sébastien Moutte, CSO & Co-founder at Opti Digital, who are tackling price floor optimization in a way that might just flip how you think about incremental revenue.

We explored key practical issues, including:

• Is it really worth partnering with an ad tech provider for price floor optimization?
• How realistic are A/B tests for dynamic flooring, and when do you just have to roll it out widely?
• And should your price floor strategy be reactive, predictive, or both?

This conversation is about making smarter decisions on when and how to test and scale, and how to effectively balance predictive and reactive strategies. Ultimately, it's about bringing clarity to a complex topic, so you can confidently unlock incremental revenue opportunities.

ROB: Publishers are under constant pressure to bring in revenue. But is the juice really worth the squeeze to work with an ad tech partner for price floor optimization?

SÉBASTIEN: It depends largely on the underlying revenue model. In our case, we operate on a performance-based approach, taking a share only of the incremental revenue generated through our solution.

For example, in a recent year-end review with one of our clients, our technology drove an additional €1 million in revenue. Even with an average revenue share of 20%, the benefit to the publisher remained significant. What’s more, because this revenue uplift is generated without increasing ad pressure or making major changes to the website, it flows directly to EBITDA.

That said, this model is most effective at scale. For smaller publishers, a 10 to 20% uplift on limited volume may not translate into a materially impactful outcome. Once revenue share and integration effort are factored in, the benefit can become marginal.

ROB: Everyone loves a good A/B test, but is running an A/B test for dynamic flooring actually realistic?

SÉBASTIEN: In theory, yes. However, in practice, testing on a limited portion of inventory can be complex and often yields inconclusive results. For example, if a publisher applies a dynamic floor pricing strategy to only 50% of their inventory while the other half has no price floors and is priced lower, ad exchanges, such as DV360 or Google Ad Exchange, can detect this through their algorithms. With a global view of the auctioned inventory, they will identify paths to similar impressions at lower prices and shift spend accordingly.

In our case, we conducted such a test and found that applying dynamic floors to a small subset of inventory did not generate strong results. However, when the strategy was implemented across 90% of the same site’s inventory, it performed significantly better.

ROB: Publishers tend to be cautious about sweeping changes, and rightly so. So, when it comes to implementing dynamic flooring across most of your inventory, isn't that just too big a risk?

SÉBASTIEN: Given the complexity and revenue implications involved, it's entirely reasonable (and even prudent) for publishers to be cautious when testing new solutions. From a technical standpoint, however, it’s perfectly fine for publishers to begin by applying the solution to a smaller portion of their inventory.

This approach allows any potential issues to be identified early without putting overall revenue at risk. However, if the implementation doesn’t scale to at least 90% of the inventory, the expected results are unlikely to materialize. Again, buyers and ad exchange algorithms naturally seek the lowest-priced paths to comparable inventory, which significantly limits the effectiveness of a partial rollout. To truly understand the impact a dynamic flooring solution can have, publishers need to reach that 90% threshold.

It’s also important to note that the model requires some time to learn. Typically, publishers should allow around one month for the system to adjust and begin delivering its full potential. In our case, we also provide backfill technology to monetize unsold impressions, which can further enhance revenue uplift when activated.

While no ad tech provider can guarantee fixed or uniform results, given the variability in market dynamics and contextual factors, publishers should consistently see positive outcomes. The value of a dynamic flooring solution lies in its automated, adaptive, and granular approach, which continuously unlocks incremental value compared to static, one-size-fits-all pricing strategies.

ROB: When it comes to price floors, everyone wants to know, should you be reactive or predictive? Or maybe the real question is, why not both?

SÉBASTIEN: Effective price floor models need to combine both predictive and reactive elements. Predictively, floors are set based on grouped traffic dimensions and user value cohorts, categorizing users by low, medium, or high value according to some specific info like cookies or user IDs.

Regarding the reactive component, it operates at the individual user level rather than as part of a broader group. For example, if we set a floor price of €1 on a page and observe strong bids at that level, we can incrementally increase the floor price. Conversely, if bids fall below expectations, we lower the floor. This approach makes the floor price unique and adaptive for each request within its specific context, enabling real-time optimization.

Clarity Over Guesswork, Every Time

Managing price floors is a complex balancing act. One that directly impacts publishers' bottom lines, operational efficiency, and strategic planning. As publishers, you don't have room for uncertainty. You need clear strategies, straightforward tests, and practical ways to scale effectively.

If you're evaluating your approach right now, here’s what you should be asking yourself:

• Do I fully understand how my revenue model and scale impact the effectiveness of a dynamic flooring solution?
• Am I realistically testing dynamic flooring in a way that can lead to actionable results?
• Can I confidently scale a dynamic flooring strategy across the majority of my inventory without undue risk?
• Is my price floor strategy effectively balancing predictive and reactive approaches to adapt in real-time?

These questions matter because price floors can’t just be set and forgotten. Getting this right means the difference between incremental revenue and leaving money on the table. Time to make sure you're on the right side of that equation.

This is content created in paid partnership with Opti Digital. We only feature partners who we believe bring real value to the publisher community.