A Data Framework That Just Works
Joinr’s ad data framework takes hundreds of trackable data points, and distills them down to the essential metrics that matter most.
Over nearly a decade of working with ad data, we realized that too many metrics between Facebook Ads, Google Ads, and other ad platforms provide misleading results. Compounding the issue, a lot of metric labels are misaligned, making cross-channel comparison a painful and manual process.
A data framework provides a defined and repeatable method for unifying and standardizing data, and is totally unbiased. When marketers use a good data framework, they get a consistent view of data, no matter the size or scope of their campaigns.
300+ Data Points become 36 Aligned Metrics and Dimensions
Point and proof: using our data framework, we’ve stripped away unnecessary metrics from Google and Facebook Ads, delivering 36 must-see cross-channel metrics that are perfectly aligned.
Metrics (click to expand)
Dimensons (click to expand)
Why Frameworks Matter
Example #1 – Misaligned Metrics
Let’s say you’re hoping to simply see the amount you spent total between Facebook Ads and Google Ads. What would normally be a simple SUM equation becomes a bit more complex when you realize that Facebook refers to spend as “Amount Spent”, and Google Ads refers to spend as “Cost”.
If you’re using a spreadsheet, this is a simple problem to fix. But if you’re dynamically syncing your data from API to a BI tool, querying the data with different labels isn’t as easy as it sounds.
Joinr’s ad data framework blends Amount Spent and Cost into one metric: Spend.
Example #2 – Video Views with Facebook
Here’s an example of misleading metrics.
Let’s say you’ve run a video ad through Facebook, and you’re looking to measure engagement. You’d start by adjusting columns in ads manager to show your video metrics:
After selecting a specific ad, the first metric you’d likely look at would be impressions, to see the number of times your ad was viewed:
After that, most marketers would look at video plays, to see what % of impressions led to a video view:
Here is the first time you might be misled. 30,152 video plays out of 46,924 impressions. That’s a 64% view rate! Show that to your boss and you can already see the promotions lining up.
Only one issue: video plays doesn’t mean how many times your video was played to completion. It only means how many times your video started to play.
So you take a step back and look for the metric that might tell you how many times your video was actually viewed, so you can start analyzing its impact on revenue.
You see a metric called ThruPlays, and think, “this is it”. That’s until you read the fine print and see that ThruPlays tells you the number of times a video was viewed to completion OR at least 15 seconds. Not exactly the accurate metric you were hoping for.
Joinr’s ad data framework solves this problem with a single click.
This is just one of the many examples of misleading metrics. Sure, these metrics can be solved with extra effort. But when compounded over time across multiple campaigns, additional humans and more providers, they can create unnecessary confusion in data analysis. Joinr removes all the guesswork for you.
Wrapping Up: Why Joinr’s Ad Data Framework is Perfect For You
No More Formulas: With a framework, you don’t need fancy formulas in Google Data Studio or v-lookups in Excel/Sheets to find or merge metrics together.
Forget SQL Queries: With Joinr, there is no need to connect to reporting APIs and build custom SQL queries to extract the data you need.
Hassle-free: If you’ve built custom queries in the past, you know what a hassle it can be to manage the database and fix any errors. Joinr’s framework does this automatically.
Goodbye Semantic Layers: Adding semantic layers to data to manage mismatched naming conventions across platforms was (and is) the most error-prone part of ad data analysis. It’s something Joinr handles seamlessly with a single click.