Clean product data on google shopping campaigns

Why Clean Product Data is Key for Google Shopping Campaigns

The Google Shopping Campaigns product was designed with enterprise retail in mind. We have heard from more than one Google Product Manager that the new framework for campaign management was inspired by the hierarchies found in large product catalogs, and how brick-and-mortar storefronts are presented and organized.


Multi-node categories provide a clear taxonomy, brands add another dimension, and additional attributes – such as color, gender and size – provide granularity.

This is great in theory. However, product catalog data is not always so clean. It may be pooled together from disparate systems, entered in by different merchandising teams, muddied by legacy nomenclature, or all of the above. In order to succeed with Google Shopping Campaigns, we are finding that feed data must be uniform, complete, and hierarchical. As you transition to Google Shopping Campaigns, be sure to clean up your product data as much as possible.

Clean up your Categories

Categories are the most crucial feed elements, as they are critical building blocks for successful Google Shopping Campaigns. For any large catalog, product categorization should be complete, distinct, and schematic. Like products should grouped together, and non-product qualifications – such as “Clearance” – should not be used.

Clean product data in Google Shopping Campaigns - categories

The challenge for many online marketers is that they may not have access or good tools to sanitize categories within their backend data systems. DemandStream can help by consolidating products into a logical category schema during integration or through rules-based real-time transformations.

Clean up your Brands

Similarly, clean Brand data is paramount to creating well-structured Google Shopping Campaigns. Shoppers often look to their favorite brand for an item within a given category. Thus, it’s essential to have every permutation of Brands and Categories within your catalog expressed in your Google Shopping Campaigns.

To do so, of course, you’ll need each product in your catalog neatly housed within a distinct, singular brand. We often see several variations of what is intended as a single brand appearing within the same feed. For example, “Hewlett Packard” vs. “Hewlett-Packard” vs. “HP.” In some cases, we’ll see Brand missing entirely for a subset of products. Make sure that Brand values within your Google feed are standardized, distinct, and complete.

Clean Product Data in Google Shopping Campaigns - brands

Content rules within DemandStream allow you to standardize and consolidate your brands so they can be used reliably as a product grouping parameter.

Use Custom Labels to Pursue Retail Goals

Once you have a basic structure in place, fine tune your Shopping Campaigns by layering on additional granularity. Use Custom Labels to refine product groups and segment out key sets of products. Be deliberate with the intent of each campaign, and measure results to evaluate their contribution to your overall retail goals.

If profitability is key to your program, for example, use cost of goods sold (COGS) or an analog to group products by margin contribution using a custom label. Then, bid accordingly to direct traffic toward high-margin groupings. If your promoted products are central to your program, establish a custom label for each promotion and bid aggressively to get a high volume of traffic to promoted products.

The Results of Clean Product Data: What we’ve seen so far

It’s still early, but we’ve seen some great results from Google Shopping Campaigns built to mirror legacy PLA campaigns. Simply switching to Google Shopping Campaigns does not guarantee success; it’s important to follow the best practices above in addition to ongoing management best practices.

We aggregated data from three large retailers from different verticals to obtain the following results:

  • Click-through Rate improved modestly, from 2.23% on legacy PLA campaigns to 2.28%on Google Shopping Campaigns
  • Conversion Rate improved modestly, from 3.57% on legacy PLA campaigns to 3.69% on Google Shopping Campaigns
  • Return on Ad Spend improved substantially, from $7.09 (or 709%) on legacy PLA campaigns to $8.97 (or 897%) on Google Shopping Campaigns
  • Cost-Per-Click improved substantially, from $1.00 on legacy PLA campaigns to $0.84on Google Shopping Campaigns

Here’s the takeaway: by leveraging clean, complete data to build semantic, top-down Google Shopping Campaigns, product groupings enter better auctions, more accurately match to shopper intent, and bring greater efficiency to campaigns.

Google Shopping Campaigns Data

The DemandStream platform enables you to clean, complete, and standardize key product data, thus giving you a solid foundation for effective Google Shopping Campaigns. For a demo of how DemandStream can help your product catalogs and improve your Google Shopping Campaigns, contact us today.