Performance Metrics in the Heatmap
The Dayparting heatmap allows advertisers to analyze campaign performance using different metrics. These metrics help identify patterns in advertising activity across different hours of the week.
By switching between metrics, advertisers can observe how traffic, engagement, conversions, and efficiency vary over time. This information helps determine when advertising exposure should be increased or reduced.
Each metric highlights a different aspect of campaign performance, allowing advertisers to evaluate both volume and efficiency when designing Dayparting rules.
Impressions
Impressions represent the number of times an advertisement is displayed to users.
Analyzing impressions in the heatmap helps identify periods when advertising visibility is highest. Certain hours may naturally generate more impressions due to higher marketplace activity.
High impression periods may indicate strong traffic availability, while low impression periods may indicate lower marketplace demand.
However, impressions alone do not indicate engagement or sales performance, so they should be interpreted alongside other metrics.
Clicks
Clicks measure how many users interact with the advertisement by selecting it.
The clicks heatmap highlights the hours when users are most likely to engage with the ad.
High click periods may indicate strong customer interest or favorable placement visibility. However, clicks alone do not indicate whether users convert into buyers.
For this reason, click patterns are often analyzed together with conversion metrics.
Sales
Sales represent the revenue generated through advertising during each time period.
The sales heatmap highlights the hours when campaigns generate the most revenue.
These time periods often represent the most valuable advertising windows because they indicate strong purchase behavior.
Advertisers may choose to increase bids or budgets during these high-performing sales periods to maximize revenue potential.
Spend
Spend represents the total advertising cost incurred during each time period.
The spend heatmap helps identify when the largest portion of advertising budget is being used.
Analyzing spend patterns helps determine whether advertising costs align with performance outcomes.
For example, if spend is high during certain hours but sales remain low, those hours may be candidates for reduced bids or limited budget allocation.
CTR (Click-Through Rate)
Click-Through Rate (CTR) measures the percentage of impressions that result in clicks.
CTR is calculated as:
- CTR = Clicks ÷ Impressions
This metric reflects how effectively an advertisement attracts user attention.
High CTR values often indicate strong ad relevance, appealing creative content, or favorable placement.
Analyzing CTR across the heatmap helps identify time periods when ads receive stronger engagement relative to their visibility.
CVR (Conversion Rate)
Conversion Rate (CVR) measures the percentage of clicks that result in a purchase.
CVR is calculated as:
- CVR = Orders ÷ Clicks
This metric reflects how effectively traffic converts into customers.
High CVR periods indicate that users who click on the advertisement are more likely to complete a purchase.
These time periods may represent high-intent shopping behavior and can be good candidates for increased advertising exposure.
ACoS (Advertising Cost of Sales)
ACoS measures the relationship between advertising spend and the revenue generated from advertising.
ACoS is calculated as:
ACoS = Ad Spend ÷ Ad Sales
This metric reflects advertising efficiency.
In the heatmap, ACoS can reveal periods where advertising is more or less efficient.
For example:
- High ACoS periods may indicate inefficient advertising where spend is high relative to revenue.
- Lower ACoS periods may indicate stronger campaign efficiency.
Advertisers often use this metric to identify hours when advertising costs are too high relative to the revenue generated.
Using metrics together
Each metric represents only one aspect of campaign performance. For this reason, advertisers often analyze multiple metrics together before creating Dayparting rules.
For example:
- High Sales combined with strong CVR may justify increasing bids during certain hours.
- High Spend combined with poor ACoS may indicate inefficient time periods where advertising exposure should be reduced.
By switching between metrics in the heatmap, advertisers can identify time-based performance trends and design Dayparting rules that align advertising exposure with the most productive hours.