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How Granular Data Structures Boosts Google Ad Performance


Granular data, as the term “granular” suggests, is data in its smallest possible pieces so that it is very specific, defined, and detailed. 

In practice, granular data can be constructed and fashioned according to the specific needs of the data analyst or data scientist. Granular data can be aggregated (combined together) or used on its own to meet the requirements of different situations. 


Here, we will discuss how we can use granular data structures to boost Google Ad performance and SEO results. Yet, let us begin by discussing the concept of granular data itself

Why Granular Data is Important

Maintaining the granulation of data and information is very important in today’s very demanding data science world: if data is not 100% granular, then it’s going to be very difficult for a data analyst or software (nowadays, AI-driven software) to mine and analyze the data. For example, if in a database, the name field is saved as a whole and haven’t been broken down into individual names, then the data is fairly useless.

Granular data, on the other hand, can be combined quite easily with other data sets from various (external) sources and can be effectively managed, measured, analyzed, and integrated.

How To Measure Granular Data in Google Ads

To take the maximum advantage of the granular data, so you can get the most actionable and accurate insights, we have to use the right measurement tools and methods.

In Google Ads, we can’t directly capture the data of what your audience does after they click on your ads, and we can’t even determine whether the ad actually contributes to a sale. Basically, in Google Ads, we don’t have any data on your user’s behavior after they see and click on the ad. 

To do this, we have to use Google Analytics so we can let the two platforms communicate together to provide more granular data structured in your reports. By linking Google Ads and Analytics accounts together and using auto-tagging in Google Ads, you can unlock a dedicated Google Ads section in Analytics’ Acquisition reports. This is how we can find more meaningful insights into our Google Ads performance.

Here are some important granular data to measure with your Google Ads campaigns:

  • How much traffic you receive from each Ads campaign
  • What pages they visit after they clicked on an ad, and how long they spend on each page
  • Which pages they visit before they leave your site
  • Which buttons get more clicks on your site, and what they do next
  • Which portions of the traffic actually convert
  • Devices used by the users (mobile, tablets, desktops, etc.)
  • Location of the users and browsers used to browse your website and click on ads

How To use Granular Data To Boost Google Ad Performance

In practice, here are some important benefits of measuring granular data by linking Google Analytics and Google Ads:

1. Avoid Fraudulent Ads as Traffic Sources

Measuring granular data at the impression level allows us to verify whether the traffic source is actually beneficial: whether they brought out traffic with the actual possibility of a conversion. You can, for example, check for a potential anomaly in conversions and adjust your approach.

If for example, an ad placement isn’t performing too well, then we’d want to reallocate the budget to other ads (or probably other channels) that can drive higher conversions. 

2. Granular Measurement as A/B Testing Tactic

Proper collection and analysis of granular data structures allow us to determine a control group so we can assess potential conversions from different banners, text content, video, and so on. 

As a result of this, we can tailor an ad campaign with the optimized conversion rates for all creative elements. 

3. Send Personalized Ads to Different Customer Segments

Segmentation of the audience is now really important if you want to maximize the effectiveness of your ad campaign by sending the right message to the right people at the right time. We can, for example, segment our audience based on the number of orders placed, the last purchase, or whether it’s a brand new customer. 

So, we can send different ads to reach the people who are already loyal to your brand and (for example), might be interested when you launch a new product. We can maximize ROI by segmenting users based on granular data structures.

4. Remarket Based on Checkout Behavior

By linking up Google Ads and Analytics, we can effectively capture granular data related to checkout behaviors. This, allows us to implement remarketing campaigns based on where the audience dropped off. 

5. Remarket Based on Google Analytics Goals

As we know, we can set up Google Analytics goals (from simple ones like a newsletter sign up to complex ones like a specific number of visits in a month). We can then set up remarketing campaigns based on whether a specific user has met a goal completion. The idea is to be more “aggressive” to users who showed more interest in your product so we can maximize conversions. 

6. Not Displaying Advertising To Those Who Recently Purchased

It’s fairly obvious that we shouldn’t need to spend money on ads and remarketing campaigns on people who just purchased your product and service since it’s highly unlikely for them to be convinced to buy another one by then in the next few days. Yet, this is often overlooked by marketers.

The solution here is very simple: simply exclude those who recently purchased your product for a certain period (i.e a week or a month, depending on your product), and then we can remarket them again after this time frame. This is only possible if we’ve properly measured granular data about their recent purchase.


Proper measurement and analysis of granular data structures can provide us with better insights into the performance of each of the Ads campaign, reallocate our budget, and adjust our ad placements and remarketing strategies according to data. This way, we can make more informed decisions and optimize the ROI of our Google Ads campaign. 


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