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Frequency Capping Best Practices
Frequency Capping Best Practices

Learn how to change your frequency capping and its best practices.

Nicolas Hemidy avatar
Written by Nicolas Hemidy
Updated over a week ago

Frequency capping allows you to control the number of times an ad is shown to a household within a specified time frame. Vibe offers different ad frequency options to suit your objectives.

Understanding Frequency Capping Options:

Frequency capping can be changed on your campaign strategy page, under the Bidding module, in the Advanced options.

Vibe provides five frequency capping choices: 1 per day, 3 per day, 5 per day (default), 7 per day, and 10 per day. Each option represents the maximum number of times an ad can be displayed to a household within a single day.

Frequency capping can impact delivery, especially if it’s low, so make sure that your campaign targets are broad enough to guarantee solid delivery.

The Delivery Estimates on the right side of your campaign dashboard will give you a deliverability assessment to help guide your capping changes.

Recommendations for Awareness Campaigns:

For branding campaigns, it is generally recommended to set a frequency capping of 5 ads per household per day.

This choice strikes an optimal balance between capturing the user's attention without overwhelming them with excessive repetition. By limiting the number of ad impressions, you ensure that your brand remains visible without causing fatigue or annoyance.

Recommendations for Performance Campaigns:

For performance campaigns, higher repetition can lead to better results.

In alignment with your campaign objectives and target audience, consider adjusting your frequency capping settings to accommodate different scenarios. For web-traffic campaigns, opting for a larger frequency capping, such as 7 or 10 impressions per day, may be beneficial. This strategy aims to enhance ad exposure, engagement, and drive conversions.

Alternatively, for retargeting campaigns, the frequency is dynamically managed by our algorithm trained to optimize performance. In this case, the algorithm is designed to adapt and determine the ideal frequency for achieving the best results. By entrusting frequency management to the algorithm, you ensure a more personalized and effective approach tailored to individual user behaviors and preferences.

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