Trends
You can access the Trends & Journeys report via the left menu.
Or via the Paid Performance table when you select the magnifying glass.
The 'Trends' dashboard offers a comprehensive view of your selected channel, campaign, ad group, ad, or creative performance over time. Choose from daily, weekly, monthly, quarterly, or yearly analysis options, and tailor your insights by selecting specific metrics.
Important options that are highlighted on the screenshot are:
Filters: the channels and campaigns dropdown can be used to subset all the line chart and cards on the page to specific channels or even campaigns
The dimension dropdown determines the level at which the cards and the individual line charts are grouped:
If you select 'Campaign', each card will contain a single campaign, while if you select 'Adsets/Adgroups' each card will contain a single adset/adgroup etc.
This also determines the dimension of the line chart if you unselect the total button. For example
With dimension 'Campaign' and total button unselected I have a line chart per campaign
With dimension 'Channel' I get a line chart per channel
Benchmarks
You are able to compare performance with other ad groups or create custom benchmarks for deeper insights.
This can help you answer questions such as:
How do my branded campaigns perform vs my non-branded campaigns?
What is the trend for the CPM of my different top of the funnel campaigns, for example YouTube vs Meta?
How is the ROAS of a specific group of ads evolving vs the total of that channel?
Further down the page, a summary of performance metrics per ad set (or chosen dimension) provides a quick overview of each asset's contribution to your goals.
To create a benchmark you do the following:
Navigate to the “Trends & Journeys” page
Select the total (or subset of ads you want to see) as a total. You can do this by configuring the following settings:
2.1 Using the dropdown options at the top, you can filter specific channels and campaigns.
2.2 Using the metric selection, you can pick which metric to look at. You can also determine whether you want to look at the total of all the items aggregated, or have a line chart per item. For example, in the image I have selected the dimension 'Adset/Adgroup', so if I unselect the total button I will get a line graph per 'Adset/Adgroup' instead of their total.
2.3 Using the buttons in the cards you can also filter on the 'Adset/Adgroups' that constitute the total. If I would select the top three items here, the total graph would only display the aggregate of these three.
Now you've created the line you want to compare your benchmark to, you are ready to create your benchmark. You do this by clicking on the 'benchmarks' dropdown in the top right. You will see several default options:
All Adset/Adgroups: this will add a comparison line in the chart based on all Adset/Adgroups (or any different dimension you've chosen)
Selected Adset/Adgroups: this will add a comparison line in the chart based on the aggregate of the 'Adset/Adgroups' you've selected in the cards
Compare period: this will add a comparison line in the chart based for the selected 'Adset/Adgroups' in the comparison period
A final option is to create your own benchmark. To do this click on 'Add new benchmark. You should see the screen below. After creating it we will save your benchmark, so you can use it again in the future.
i) Using the name field you can store your benchmark under a certain name
ii) Using the filters you can use the options to subset any of your ads to a specific group
iii) Using the compare period button, you can pick to have the benchmark not show the data of the period you've selected, but of the comparison period.
To display the power of using benchmarks in the trends analyzer, I will give an example below. We are going to answer the question:
"How does the ROAS trend of my Google campaigns look compared to the trend of only my Google Fashion campaigns? And that of my Meta beauty campaigns?"
We first subset the data to only contain Google campaigns
We then create two new benchmarks. One for Google campaigns containing the word "FA" and the other for Meta campaigns containing the word. "beauty".
After these easy steps we are analyse the graphs for all three
We can see that the performance for Google Fashion is almost identical to that of all Google campaigns, while the performance of Meta is similar, but seems to fluctuate more.
We could also compare our benchmarks to individual Google campaigns by unselecting the 'total' button. In the example below we are comparing our top three Google campaigns by orders to the benchmarks:
Journeys
Navigate to the 'Journeys' tab to visualize the journey users go through to achieve the goal you have specified at the top of the page. Click on the individual sources or paid channels to view their respective goal metrics.
Below the Customer Journey Analyzer, you can find an Ad Channel Overlap image, which offers insights into how your paid channels synergize to achieve your selected goal. You are able to (de-)select the channels you like to include in this view by clicking on them on the left.
Reach & Frequency
Reach
Reach represents the unique number of people who have been exposed to a specific advertisement or piece of content at least once during the selected date range. Think of reach as the number of people who have seen your ad. For example, if an ad campaign has a reach of 50,000, it means 50,000 unique individuals saw the ad, regardless of how many times.
Frequency
Frequency refers to the average number of times each person within your reach has been exposed to the ad in the selected date range. It highlights the depth of exposure for your audience. For instance, if the frequency is 3, then each person, on average, has seen the ad three times.
Why both are important
Balancing reach and frequency is key to effective marketing. High reach with low frequency might mean the ad doesn’t “stick,” while low reach with high frequency may lead to oversaturation for a small group, wasting resources. The ideal combination depends on the campaign goals:
Awareness campaigns might prioritize high reach.
Retention or conversion campaigns might benefit from a slightly higher frequency to reinforce the message.
Why can you not just sum up reach if you want to go from ad to campaign?
The reason is because of the uniqueness of the individual, see the below example:
Person A has seen Ad 1 of Campaign 1
Person B has seen Ad 2 of Campaign 1
Person A has seen Ad 2 of Campaign 1
Person C has seen Ad 3 of Campaign 1
Person A has seen Ad 3 of Campaign 1
If we measure this, the reach would be:
ad 1 = (only person A has seen it) = 1
ad 2 = (person A and person B have seen it) = 2
ad 3 = (person A and person C have seen it) = 2
If we sum this up, it becomes ad 1 + ad 2 + ad 3 = 1 + 2 + 2 = 5.
But if we look at the reach of campaign 1, only person A, person B, and person C have seen it, so the campaign reach = 3. The difference is 5 - 3 = 2. It can happen that the same person is targeted within different ads within the same campaign.
What is the difference between the last 90 days and the daily reach/frequency?
For simplicity, let's assume that you are selecting one day in the date filter: (01-01-2024).
Daily reach and frequency represent the reach and frequency for that specific day. So how many individuals saw the ad at least once (reach) and how many times on average did one individual see the ad (frequency).
Last 90 day reach/ frequency represents the metrics taking into account the last 90 days. So the definition is:
Reach: how many individuals saw the ad at least once in the last 90 days.
Frequency: on average, how many times did one individual see the ad during the last 90 days.
These variables are taken from the marketing channel on ad/ adset/ campaign/ channel level separately with a daily range and a last 90 days range for one specific day. If you are selecting multiple dates in the date filter, we are using an average to come to one number.
Why can’t you just take the sum of the daily reach?
This is again because of the uniqueness. See the below example:
Person A has seen campaign 1 on date 03-10-2023
Person A has seen campaign 1 on date 03-11-2023
Person B has seen campaign 1 on date 03-11-2023
Person A has seen campaign 1 on date 01-01-2024
So the daily reach of campaign 1 on 01-01-2024 is 1
So the daily reach of campaign 1 on 03-11-2024 is 2
So the daily reach of campaign 1 on 03-11-2024 is 1
So the sum of the daily reach would be 1+2+1 = 4.
But if we look at the last 90 days window, we see that only two people (A and B) have seen the campaign. So actual reach should be 2.
What about frequency?
Using another example:
Person A has seen campaign 1 two times on date 03-10-2023
Person A has seen campaign 1 on date 03-11-2023
Person B has seen campaign 1 on date 03-11-2023
Person A has seen campaign 1 on date 01-01-2024
The daily frequency of campaign 1 on 03-10-2023 is 2 (person A, has seen the campaign 2 times)
The daily frequency of campaign 1 on 03-11-2023 is 1 (person A and person B have seen the campaign 1 time)
The daily frequency of campaign 1 on 01-01-2024 is 1 (person A has seen the campaign 1 time)
So the average of the daily frequency of campaign 1 is 2 + 1 + 1 (daily frequency) divided by 3 (the number of days) = 1.333.
But the actual frequency is 4 (total number of views) / 2 (number of persons) = 2.