Diminishing returns in advertising occur when each additional dollar of spend produces a smaller revenue return. As brands scale a channel without re-testing incrementality, efficiency erodes silently, leaving millions trapped in low-return media placements. Without ongoing measurement, even a high-performing channel can quietly become a budget drain. This video walks through a real case where a diminishing returns analysis identified an 80% budget cut that changed revenue by just 1%
Why Diminishing Returns Make Single Incrementality Tests Dangerous
A single incrementality test only captures performance at one spend level, so treating that result as fixed while a channel keeps scaling can hide the exact point where returns start shrinking. John Wallace, CEO of LiftLab, walks through a cautionary case where an advertiser scaled a channel nearly eight-fold based on one incrementality test, which had shown a 70-cent return for every dollar spent. Assuming that 70-cent return would hold steady, they kept increasing spend, effectively assuming linear performance in a channel that could not supply an unlimited amount of quality inventory.
When LiftLab ran a diminishing returns analysis on the account, the forecast showed the advertiser could cut roughly 80% of spend on that channel with only a 1% change in revenue. This case illustrates a core principle of media budget optimization: performance metrics shift as spend scales or the ad ecosystem changes, so treating a single test as permanent truth can cost a business millions in wasted ad spend.
In this video, you’ll learn:
Why relying on a single, outdated incrementality test can lead to massive overspending as you scale
How to identify the point of diminishing returns in your ad spend to optimize profitability
Why performance metrics should never be treated as static figures in a changing ad ecosystem
Why cutting ad spend does not always result in a proportional decline in total revenue
TIME STAMPFull Video Transcript: Diminishing Returns Case Study
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In this transcript, LiftLab CEO John Wallace walks through a real advertiser case study, tracing how one channel scaled past the point of efficient returns and how a diminishing returns analysis brought it back to profitability.
[00:00 – 00:35] The Initial Insight and Successful Scaling
Here’s a story about an advertiser who initially did things correctly. They had the insight to run an incrementality test in-platform, which revealed that for every dollar spent on ads, they could claim credit for 70 cents in revenue. With this 70-cent-on-the-dollar factor, they proceeded with confidence to increase their spend dramatically, nearly eight-fold.
[00:36 – 01:10] Ignoring the Law of Diminishing Returns
Unfortunately, they did not revisit that initial incrementality test as they scaled. They continued to assume they would maintain that 70-cent return even at significantly higher spend levels, effectively ignoring the law of diminishing returns. They assumed their spend would scale linearly, implying they would continue to find a similar quantity of quality placements and targetable consumers, which was not the case.
[01:11 – 01:38] The Shift to Data-Driven Optimization
By the time we got involved, they had grown this channel to their second-largest, which is the only media plan I’ve seen where an OTT platform was the second-largest channel. Using LiftLab’s diminishing returns experiment, our forecast suggested they could cut about 80% of their spend with only a 1% change in revenue, saving them tens of millions of dollars.
[01:39 – 02:13] Results and Strategic Takeaways
They proceeded with a 50% cut in spend, and after corroborating the results, they confirmed there was no impact on revenue, as that portion of the budget was spent on low-efficiency media. They then cut another 30%, totaling an 80% reduction, and now have a happy channel that they can fund profitably. The answer wasn’t to exit the channel, but to find the point where the profit from an extra dollar of investment outweighs the cost.
[02:14] Closing Recommendation
My recommendation to marketers is that if you aren’t going to run diminishing returns experiments, you at least owe it to yourself to run far more incrementality tests. You need to be able to run enough of them to understand how your returns are impacted when you make large changes in spend or when the ad ecosystem changes, rather than relying on static results from a prior experiment.
Key Lessons: Cutting Ad Spend by 80% Through Diminishing Returns Analysis
Don’t assume linear scaling: Increasing your budget eight-fold does not guarantee an eight-fold increase in returns; efficiency often drops as you exhaust high-quality inventory.
Continuous Testing: If you cannot run ongoing diminishing returns experiments, you must increase the frequency of your standard incrementality tests to stay informed.
Efficiency over Volume: Sometimes cutting a significant portion of your budget (e.g., 80%) is necessary to shed low-efficiency media and reclaim a healthy, profitable channel.
Strategic Adaptation: The goal is to find the “sweet spot” where each additional dollar of investment produces a profit that outweighs the cost of the media.
