Are you running digital advertising campaigns on multiple platforms? Sounds like having your results combined in one easy-to-use dashboard would be handy. Luckily, we’ve got you covered!
Our friends at Bind Media made a more extensive template with reports for all the connector channels included. 👇
- Google Ads
- Microsoft Ads
- Facebook Ads
- Twitter Ads
- LinkedIn Ads
- with the additional context of Google Analytics.
To see all the charts in action, follow these instructions to set up the template and connect it with your data sources.
We had a chance to have a chat and go through the dashboard and ways to enhance it. You can find the full recording here.
We’ve gone through all the steps to get you started with the template. So keep on reading!
Feel free to skip ahead to these sections:
- Overview page
- Facebook Ads report
- Google Analytics report
- How to modify the template
- Why should you use the Ad data + Google Analytics connector?
- How to enhance your report with custom metrics and dimensions
Let’s dive in!
The first page gives you an overview of the ad platforms you’ve plugged in using the Supermetrics connector.
You might notice the currency metrics don’t have the currency symbol. It’s left out intentionally, so you can choose the currency that’s relevant to your accounts.
On this page, you’ll find three filters: data source, date selection, and campaign. You’ll have more filter options in each channel report.
Next up, let’s look at the Facebook Ads report and the Google Analytics report.
Facebook Ads report
You’ll find additional filters like device, ad group, and conversion window to help you segment your data on the Facebook Ads report.
Here’s the fun part: with the Supermetrics connector, you can move away from Facebook’s default setting (28-day post-click on a 1-day post-view) and change the conversion window to whatever makes the most sense for you.
Like with the overview page, the scorecards at the top show the changes in clicks, impressions, CTR, average CPC, spend, and conversions.
Below these, you’ll see the demographic and device breakdowns. All the breakdowns and filters are fully customizable so that you can adjust them on every page.
Google Analytics report
You can get your Google Analytics data on the second to last page. The scorecards have metrics like sessions, users, bounce rate, and others. Next, you’ll see the sparkline of sessions and users as well as the geographic breakdown.
At the bottom, you’ll find a comparison between Google Analytics and your ad channels. On the right-hand side, you can find seven filter options.
The source filter has a dropdown with all your sources listed. You’ll notice there are sources called Facebook and FB as well as Twitter and Twitter paid. The connector configuration page has a setting to transform the names of these sources. For example, if the sources seem duplicated, you can change the names and see FB and Facebook’s data as one.
The feature below helps avoid GA data sampling wherever possible. Insert a value of “true” to enable that, or leave it blank if you don’t mind sampling.
If you’re trying to fetch completely unsampled data, that can slow down your load time. You might want to consider that as an option, depending on the amount of data you’re pulling.
How to modify the template
Let’s talk more about the template itself and the modular approach we’ve used. For every page listed in the template, there is a blank hidden version of that page.
For example, if you’re not happy with the data displayed on one of the channel pages or want to customize it further, take a look at one of these template pages. It’s effectively a blank version of that page.
At the end of this list, the page is a full blank page with none of the left-hand navigation selected.
Why should you use the Ad data + Google Analytics connector?
There are a couple of reasons for that.
1. You can store, transform and visualize your data in one place
The main reason is that you get a 3-in-1 product; what a sweet deal.
You can store your data, transform it, and make your metrics comparable across all the platforms you decide to plug in. After that, you can visualize your data in Google Data Studio.
We’ve touched on aggregated ad platform metrics to your clicks, impressions, cost, and other metrics, all combined across the channels that you decide to plug in. Besides, you have the option of platform and analytics conversions. If you pull goal and ecommerce metrics from Google Analytics, you can compare them to your platform data.
2. You have more accurate conversion attribution
Another benefit is the deduplication of conversions. Let’s say you have a user that interacts with multiple ad campaigns, clicks through a number of your channels over a long sales cycle. The benefit is that it would default to Google Analytics’ non-direct attribution model. Therefore, many of your paid channels might take credit for those conversions.
By connecting it just through Google Analytics, you would get that number duplicated to get a more accurate result using the connector.
If you are feeding metrics like offline conversions back to Google Analytics using the measurement protocol, these metrics will be retroactively added and attributed to the appropriate paid channel.
3. You have easy customization of your data
Lastly, the Ad data + Google Analytics connector allows you to have more flexible control of your data and customization. With Google Data Studio’s help, you can slice and dice your data with custom metrics and advanced calculations.
Ad data + Google Analytics connector for Google Data Studio
Discover the easiest way to move your cross-channel advertising metrics and Google Analytics data into Google Data Studio.
How to enhance your report with custom metrics and dimensions
You can do some data prep for your report with the Ad data + Google Analytics connector. For example, you can use custom fields like average cost per conversion and click-through rate.
There are several ways in which you can enrich your report with custom metrics and dimensions:
- Custom (calculated) metrics
- Extracting from existing dimensions (see our Google Sheet resource)
- CASE statements to group/filter based on the dimension value
- Using parameters
Let’s go through them in detail one-by-one.
1. Custom (calculated) metrics
You can create custom metrics if there are some you don’t currently see or dimensions you would like to see differently.
In this example, let’s create an average order value (or AOV) field.
To create a custom metric, copy the scorecard first and click “add a field” at the bottom right-hand corner.
Next, type in the SUM of transaction revenue metric divided by the sum of transactions. A small green checkmark at the bottom indicates that the formula is correct.
After you click “Finish”, drag the newly created Average Order Value metric to display it in the scorecard.
You can also create the ROAS calculated metric by dividing the transaction revenue sum by cost sum.
2. Extracting from existing dimensions
For this example, we’ll use the Google Sheets tool we created. This tool helps extract the parameters from the campaign dimension, which is comparable across all ad platforms. The vertical bar here is a mark that separates the campaign name parts.
Your campaigns need clear naming conventions that will allow you to extract easily relevant parts to use this tool. A good example of such naming conventions could be “BindMedia | Search | USA | Oranges”.
The substring indicates which part of the campaign you are going to extract. In this case, we’ll call it “type” as it will allow group campaigns with similar attributes together.
This is a convenient way of grouping that you can also apply to any visualization within Google Data Studio.
After you’ve copied the formula, go back to Google Data Studio and add it as a custom field. The regex we’ve placed will extract the correct value from the campaign name.
A pie chart or a stacked bar with costs would be an example.
Let’s say we want to display campaigns that contain the word “Oranges”. The formula allows grouping and displays campaigns based on specific criteria when the defined condition is met.
After you’ve added the formula, click “finish”, and your table should update accordingly.
3. CASE statements to group/filter based on dimension value
In this example, we’ll use case statements to group a filter based on dimension value.
Now, let’s add another field and call this “category”.
In this example, we use a case statement to identify when the dimension type meets the criteria you chose. In this case, when the type is “apples” or “oranges”, they are classified as “fruit”. If the type is “lemons” or “limes”, they are classified as “citrus fruit”. Everything else will be in the “vegetable” category.
Similarly, you can group or assign categories based on specific keywords or ad group names.
Just like with the previous example, click “Finish” for the table to update.
If you’re using spreadsheets, you’re probably familiar with the term concatenation.
In this example, we connect two of our fields that we’ve already made — type and category — diving deeper into the data analysis of fruits and vegetables.
Again, it’s particularly important to understand the limitations of connecting multiple channels across multiple ad platforms. Inevitably, the data isn’t going to be perfect.
We just gave you an example of the options you have in Google Data Studio in grouping your data to fit your needs.
5. Using parameters
The very last example we’re going to give is parameters. It’s a relatively new feature of Google Data Studio and a really powerful one.
In the past, you weren’t able to add static values that you could use to manipulate other forms of data pulled in through your data sources.
Let’s take a look at the ecommerce metrics in this example and assume that we have a lead generation campaign to drive free trials of a SaaS product. Using a parameter, we can calculate how many free trials turn into paying customers.
In the bottom right-hand corner, we want to select our parameter. And we need to give it a name and either a text, number, or true-false value. In this example, we’re going to give it a number value, a percentage range.
In this case, it’s going to be between 0 and 1, so 0% and 100%. By default, we assume that we convert at 40%.
After you click “finished”, you can see the parameters appear on the right-hand side. We can drag this to become the control field and change it into a slider.
Now we’ve got our slider where we can change the value from 0% right through to 100%. By default, it’s set to 40%. Using this, we can calculate the estimated number of new customers.
With a couple of calculated metrics, this parameter, and a slider, we can then see how the metrics will change. It’s great for doing some scenario planning and modeling. For example, you could input budgets for the new campaigns and see how many new customers you want to get.
This is quite a powerful business tool for forecasting, which helps you understand that if your conversion rate rises from 50% to 75%, your customer acquisition cost will drop from $20 down to $13 as a result.
We have shown a small subset of data manipulation in this example, but this, of course, will span all of the ad data in this template, and that’s where the beauty of connecting to Google Analytics comes in.
Over to you!
This concludes the demonstration of what our paid channel mix template can do!
You can get creative with various ways to visualize the data. The blank pages and the modular structure designed are there to help you achieve that.
You can also use community visualizations, including the ones added by Supermetrics, to create more advanced graphs and charts.
Getting started with the template
If you’d like to try out the template, here’s how you can do that.
First, open the Bind Media + Supermetrics | Paid Channel Dashboard.
You’ll be prompted to connect your ad accounts. Please close the prompt and continue to the next steps. You’ll be able to connect them once you’ve copied the template.
Then click on “Use template” in the top right corner.
Next, under the “New data source” field, click on the drop-down menu and choose “Create new data source.”
In the connector gallery, search for a connector called “Ad Data + Google Analytics.”
Next, complete the two-step authorization and give Supermetrics all necessary permissions.
Psst, by authorizing your account for the first time, you’ll start a 14-day free trial of Supermetrics for Data Studio.
You should choose the accounts you want to get data from and the settings you need in the next window. You don’t need to select an account for all the data sources. If you leave it blank, there simply won’t have data in that specific view of the report. If you’re unsure, don’t worry, you can always change these later.
Now, click on “Add to report” → “Copy report.”
And that’s literally it. Your paid digital marketing reporting dashboard is ready to use.
Your turn 👊
This is just the beginning. Once you get the hang of Google Data Studio and Supermetrics, feel free to add any dimensions or metrics to your report.
You can also combine your ad data with data from different sources to gain a complete picture of your marketing activities.
If you have any questions about the template, let us know!