What Is It?
The Shopping Campaign Analysis tool provides a great way to get insights into your Shopping and Performance Max Campaigns.
You can aggregate data from your shopping feed by performance metrics in Google Ads and attributes present in your feed. The current structure of your campaign does not limit the tool and works for Standard Shopping and Performance Max campaigns.
With the Shopping Analysis tool, you can get insights to determine, for example, at which price point your products have the highest ROAS (Price > ROAS) (ROAS = Return on Ad Spend).
Why Use It?
With this tool, you can:
Save time on manual analysis by viewing consolidated performance data in one place, without needing to segment campaigns individually.
Evaluate performance at a granular level to make more informed optimization decisions.
Identify exactly which products, price ranges, or attributes drive the highest return, so you can focus your budget on what performs best.
Uncover underperforming products or segments in your feed and take action by excluding, discounting, or restructuring them.
Settings
To start off, select the Standard Shopping or Performance Max campaign(s) you want to get information for and select the date range. You can select multiple campaigns from the same feed to be considered in the analysis. Custom Date ranges are also available.
If your merchant feed has not been recently synced, you’ll need to re-sync it to Optmyzr so we can get the most up-to-date version of the feed.
Select the attributes you want to aggregate data by and press on “See Data Aggregated by.” If you want to select a different hierarchy, just click 'Reset.' At the moment, the tool supports aggregating data by two attributes.
You can see data for your paused campaigns as well. Select the campaigns with suffix (paused) and a relevant date range, and you'll be able to then aggregate them based on the different attributes.
Understanding the Results
The results will be displayed by ranges, where all the products that fall under such a category will be grouped together. Taking the following analysis of Price > ROAS, you'll see the price points in ranges as 0 - 6, 6 - 10, etc. Inside each of these price ranges you have one row for each range of ROAS, and next to each one, and in parenthesis, you have the number of products contained.
For example, for price points from 0 - 6, there are 25 products that range from 3388% to a 5423% ROAS.
Sorting Results
You can sort the table for any metric by clicking on it, and easily get to identify the Brand with the highest ROAS or category with the most clicks.
Sorting will also work when you do 2 - level aggregation, for example, if you aggregate performance for Brand > Product Type → and sort for ROAS, you'll see the Brand + Product Type combination that is getting the highest returns.
This tool is an extremely useful way of analyzing data for shopping campaigns based on business goals and not just Google Ads performance.
Data Visualization
The Data Visualization feature inside the Shopping Analysis tool offers a graphical and visual way of analyzing your results. Through colors ranging from a negative red to a positive green, you compare the selected analysis you've made with a third metric and make the comparison between groups, or within the same ad group.
In the example below, the initial analysis was made with Product Type 0 > Price, and the Data Visualization was compared vs Conversions and between groups.
The analysis is broken down into separate slots, and you can hover over each to see details of its results.
Use Cases
Identify the Most Profitable Price Range
Problem: You are unsure which price points are driving profitability and which ones are hurting your return. Without clear segmentation, it becomes difficult to scale high-performing products or adjust pricing strategies effectively.
Solution: Use the Shopping Analysis tool to aggregate performance data by Price and ROAS. This allows you to clearly identify the price ranges where your products perform best and where profitability starts to decline.
Optimize Budget Allocation Across Low-Performing Products
Problem: A portion of your budget is being spent on products that generate little to no conversions, but identifying them manually is time-consuming and inefficient.
Solution: Aggregate data by Item ID or Price against Conversions to quickly identify underperforming products. Export this data and take action by excluding these products or reallocating budget to better-performing ones.
Evaluate Performance of Specific Product Groups
Problem: You want to analyze the performance of specific product categories or groups, but your current campaign structure limits your ability to do so effectively.
Solution: Leverage the tool’s ability to aggregate data by Item Group ID or other feed attributes, regardless of your campaign structure. This gives you a flexible way to evaluate performance across custom groupings.