Fill rate

What is Fill rate?

Fill rate is a publisher-side metric that measures the percentage of ad requests that result in an actual ad being served. It is calculated by dividing the number of ads served (impressions) by the total number of ad requests, then multiplying by 100. A high fill rate indicates strong inventory monetization, while a low fill rate signals missed revenue opportunities.

How it works

Fill rate is calculated using the following formula: Fill Rate (%) = (Ad Impressions Served / Ad Requests Made) × 100. For example, if a publisher sends 10,000 ad requests and 8,500 ads are served, the fill rate is 85%.

Why Fill Rate Falls Below 100%

A fill rate of 100% is rarely achieved and not always desirable. Several factors cause fill rate to drop below 100%.

Ad blockers intercept requests at the network level, targeting entire ad networks rather than individual ad calls. This allows the request to leave the app but prevents the ad from being served, reducing the counted fill rate.

Technical errors anywhere in the ad delivery chain, including SDK misconfigurations, timeout failures, or adtech stack issues, can cause requests to go unfilled.

Inventory quality mismatches occur when the ad content requested does not match available advertiser demand. Low-quality placements or audiences may receive fewer bids, leaving inventory unfilled.

Waterfall and mediation gaps arise when a publisher relies on a limited set of ad networks. If none of those networks have a suitable ad at that moment, the request goes unfilled.

Fill Rate vs. eCPM

Fill rate and effective cost per mille (eCPM) are closely related but distinct. A very high fill rate achieved by accepting low-value ads can reduce eCPM and overall revenue. Publishers must balance fill rate against ad quality to maximize total yield rather than optimizing either metric in isolation.

Why it matters

Fill rate directly determines how much of a publisher's available inventory is converted into actual revenue. Every unfilled ad request represents lost monetization potential. For apps relying on advertising as a primary revenue stream, consistent monitoring of fill rate is essential for identifying technical failures, network performance gaps, and placement inefficiencies before they compound into significant revenue loss. Fill rate also affects user experience indirectly. When ads fail to load in expected placements, layout inconsistencies can degrade the perceived quality of an app. On the demand side, advertisers and ad networks use publisher fill rate data as a signal of inventory reliability. Publishers with consistently low fill rates may receive fewer competitive bids, creating a cycle that further reduces both fill rate and eCPM.

How to improve Fill Rate

Improving fill rate requires addressing both technical reliability and demand diversity.

Integrate multiple ad networks. Relying on a single ad network creates a single point of failure. Publishers should work with several networks and compare their fill rate performance, pricing, and campaign quality across placements. Ad mediation platforms automate this process by routing each request to the network most likely to fill it at the highest value.

Implement in-app bidding. In-app bidding (header bidding for mobile) allows multiple demand sources to compete simultaneously for each impression rather than sequentially. This increases the probability that every request is filled and raises competitive pressure on pricing.

Optimize ad placements. Low fill rates in specific placements often indicate poor placement quality or mistimed display. Testing different placement locations, triggering conditions, and user journey contexts helps identify which placements attract genuine demand.

Address ad blockers through native advertising. Native ads integrate into app content rather than loading through standard ad calls, making them less susceptible to network-level ad blocking. This approach preserves fill rate in segments where ad blockers are prevalent.

Monitor and resolve technical errors. Publishers should audit their SDK integrations, timeout settings, and mediation configurations regularly. Timeout values that are too short cause requests to fail before a network can respond, artificially deflating fill rate. Raising timeout thresholds incrementally and monitoring the impact provides actionable data.

Set realistic floor prices. Price floors that are too high reduce the number of bids that qualify, lowering fill rate without a proportional gain in revenue. Publishers should test floor prices against fill rate outcomes to find the optimal balance for each placement and audience segment.

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