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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. 

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. If you have one item id per product group, you can use these insights to make bid changes using the Shopping Attribute Bidder. 

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.