Mobile Monetization

How to Set Floor Prices for New App Inventory

A cautious method for testing floors without killing fill rate or hiding demand signals.

By Maya Chen · Updated June 2026 · Educational guide
Custom diagram for How to Set Floor Prices for New App Inventory

This article is written for app publishers and ad operations teams. It is not a vendor pitch and does not require a specific mediation platform, exchange, SDK, or DSP.

When this format makes sense

A cautious method for testing floors without killing fill rate or hiding demand signals. In day-to-day ad operations, the useful question is not whether a tactic sounds advanced. The useful question is whether it can be observed, tested, and explained when revenue changes. This guide focuses on signals a small app team can actually inspect.

For new inventory, avoid making several changes at once. Keep one clean baseline, record the date of each experiment, and compare results by country, format, operating system, and app version. A small change can look successful overall while damaging a valuable segment.

Implementation choices

A cautious method for testing floors without killing fill rate or hiding demand signals. In day-to-day ad operations, the useful question is not whether a tactic sounds advanced. The useful question is whether it can be observed, tested, and explained when revenue changes. This guide focuses on signals a small app team can actually inspect.

For new inventory, avoid making several changes at once. Keep one clean baseline, record the date of each experiment, and compare results by country, format, operating system, and app version. A small change can look successful overall while damaging a valuable segment.

SignalWhy it mattersWhat to check
floor priceUsually changes bid density or user tolerance.Compare before/after by format and country.
fill rateHelps explain whether the issue is demand, inventory, or policy.Inspect logs, dashboard filters, and partner notes.
eCPMOften becomes the hidden cause of revenue swings.Track it in the weekly review, not only during emergencies.

Revenue versus user experience

A cautious method for testing floors without killing fill rate or hiding demand signals. In day-to-day ad operations, the useful question is not whether a tactic sounds advanced. The useful question is whether it can be observed, tested, and explained when revenue changes. This guide focuses on signals a small app team can actually inspect.

For new inventory, avoid making several changes at once. Keep one clean baseline, record the date of each experiment, and compare results by country, format, operating system, and app version. A small change can look successful overall while damaging a valuable segment.

Testing plan

A cautious method for testing floors without killing fill rate or hiding demand signals. In day-to-day ad operations, the useful question is not whether a tactic sounds advanced. The useful question is whether it can be observed, tested, and explained when revenue changes. This guide focuses on signals a small app team can actually inspect.

For new inventory, avoid making several changes at once. Keep one clean baseline, record the date of each experiment, and compare results by country, format, operating system, and app version. A small change can look successful overall while damaging a valuable segment.

Practical checklist

A cautious method for testing floors without killing fill rate or hiding demand signals. In day-to-day ad operations, the useful question is not whether a tactic sounds advanced. The useful question is whether it can be observed, tested, and explained when revenue changes. This guide focuses on signals a small app team can actually inspect.

For new inventory, avoid making several changes at once. Keep one clean baseline, record the date of each experiment, and compare results by country, format, operating system, and app version. A small change can look successful overall while damaging a valuable segment.

Example operating note

A useful internal note is short: what changed, where it changed, when it started, which segments moved, and what action will be reversed if the test fails. This habit makes monetization experiments easier to trust.

MC
Maya Chen

Former ad operations lead focused on app inventory, ad quality, and publisher revenue operations.