Cross Platform Advertising Template

The cross platform advertising template shatters data siloes and unleashes insights around your overall paid ad campaigns across different channels and platforms.

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Cross Platform Advertising Template

Features:

  • Easy-access Filters: Quickly filter the template by account, platform, provider or by campaign.
  • Performance Overview: Compare campaigns and channels at a glance, measuring spend vs. impressions, clicks and conversions.
  • Weekly Trends: Analyze your campaign spend week over week against clicks and conversions to challenge test different creative against specific dates.
  • Top Ads: Highlight the ads that drive the best spend-to-CTR ratio, so you can double down on that creative for peak performance.
  • 100% Customizable: Every template you use is fully customizable, so you can adapt it to your use case seamlessly.

Less reading. More trying.

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Why track campaigns with the cross platform advertising template?

Most digital marketers run holistic advertising campaigns across many different platforms. Unfortunately, comparing and analyzing cross-platform campaigns in a single data set is a pipe dream for most marketers.

Here’s why: every advertising platform traps data inside their in-app analytics. To extract the data, you have to download clunky CSVs, which make it nearly impossible to blend data sets.

Instead of analyzing cross-platform data in silos, we recommend using ad data connectors that can extract your data (like a rescue mission) from different platforms, and merge it all in a centralized reporting tool like Google Data Studio.

Benefits of analyzing blended cross-platform data

When you analyze blended, cross-platform data, you immediately get a perk of consistency. Rather than having to waste time and brain power trying to figure out how metrics or dimensions with different names relate, Joinr automatically gives you clear and consistent naming conventions.

For example, Google Ads calls their metric “Amount Spent”, while Facebook calls it “Spend”. That’s a simple example that only gets exacerbated when dealing with more complicated metrics like video views and link clicks.

Here are some other benefits of analyzing blended cross-platform data:

  1. Optimizations: comparing all ad platforms in one data set lets you quickly and easily highlight platforms that are working, and which require tweaks to improve or optimize.
  2. Clarity: a lot of ad platforms provide unclear metrics that you have to look up to understand. With a blended data set, you always get clear and digestible metrics and dimensions that just make sense.
  3. Time saving: Manually blending data is a labor-intensive process that Limit the audience scope so you are more targeted in your advertising. Although this casts a smaller net, it helps to suppress costs.
  4. If you’re running search ads, consider bidding for longer tail keywords. This will reduce your competitive pool, and will help keep ad spend dow.

How to manually blend cross-platform data (if you must)

Since you’re on this template page, you’ve already discovered the easy way to blend cross-platform data. However, if you’re keen on doing this process manually, we’ve written a great article about how to do it using Excel formulas in a spreadsheet.

In a nutshell, here’s the process:

  1. Download CSVs of all your data from each platform
  2. Insert the data into one common spreadsheet
  3. Create a new tab for blended data, where you’ll run VLOOKUP formulas to match date ranges
  4. Manually rename certain metrics and dimensions so they’re aligned
  5. Use SUM formulas to add up all the metrics together to create one holistic view.

This 5 step summary may be a bit reductive, but checking out the full article will give you a much greater sense of the effort required manually blend data (hint: it’s a lot!).

For those who use Google Data Studio often, you can achieve a blended view of your data using JOIN CONFIGURATIONS, which are a key feature in Data Studio. They allow you to “left join” two dimensions together, to create a holistic view of the data.

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