The avg. impr. freq. per user (7 or 30 days) is the average number of times a person is shown a video ad from your campaign in the past 7 or 30 days (either for a single day or for multiple days).
Why it is important
Tracking the Average Impression Frequency per user is important as it provides advertisers with insights into how often an individual is exposed to their video ads over a specified period, either 7 or 30 days.
This metric is crucial for managing ad fatigue, avoiding overexposure, and making informed decisions about campaign optimization, ensuring a balanced and effective delivery of video content to the target audience.
Impressions / Unique Reach
In Google Ads, you want to look at the avg. impr. freq. per user (7 days) from January 29, 2020-January 30, 2020 for a selected campaign. Google Ads will look at the sum of total impressions in the lookback windows for January 29, 2020 and January 30, 2020 and divide the amount by the sum of total unique users for each day in the date range.
Let’s say you have the following impressions and unique users for your campaign:
1,400 impressions and 700 unique users in the past 7 days from January 29, 2020. 1,000 impressions and 200 unique users in the past 7 days from January 30, 2020. Your avg. impr. freq. per user in the past 7 days would be 2.7 (or [1,400+1,000] / [700+200] = 2.7).
Frequency capping Unique Reach
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1. How does the Average Impression Frequency per user differ across different product categories or campaigns within your e-commerce company, and what can be learned about the best frequency for different types of products or services? 2. Can the relationship between Average Impression Frequency per user and KPIs such as conversion rates and click-through rates be studied to determine the ideal frequency for maximizing engagement and conversions? 3. Are there particular audience segments or demographics where adjusting the Average Impression Frequency per user could lead to more effective ad delivery and improved campaign performance? 4. How does seasonality or promotional events affect the Average Impression Frequency per user, and how can this data be used to plan strategic adjustments to ad exposure during peak periods? 5. Can the correlation between ad creative variations and Average Impression Frequency per user be examined to identify which creatives are more resistant to ad fatigue and remain effective over time? 6. What can be learned by comparing the Average Impression Frequency per user for different ad formats, such as in-stream versus bumper ads, and how can this information inform the overall video ad strategy? 7. How does the Average Impression Frequency per user contribute to brand recall and awareness, and what strategies can be implemented to find the right balance between exposure and avoiding audience fatigue in the context of an e-commerce business?
Possible causes of worsening
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