Fisher Martin

by Fisher Martin | Ecommerce Strategy

Outside of Title Optimizations and improving product approval rate in the Google Merchant Center, the most effective way to increase Google Shopping performance is to level up your Shopping Structure. Through intentional, strategic segmentation that matches the client’s goals, an advertiser can:

  • Effectively highlight hero product groups
  • Ensure that Shopping is a profitable channel relative to both ROAS and final ROI
  • Bid more accurately on Top Products and Bottom / Low-Performing Products

There are many ways to segment a Shopping structure – this is not intended to be exhaustive, but to act as a starting point for your strategy brainstorms.


Client KPIs / Nature of Business – The primary consideration when determining Shopping segmentation is aligning your strategy with core performance indicators; the overall nature of your business will make or break your success in this channel. This determination should be informed by decisions that are higher level than Shopping Segmentation as a topic, but two guiding principles: (A) is our goal focused on Scalability or Return? (B) does the business lend itself towards several distinct product groups or is the product load concentrated within a niche where users typically search along the same lines?

Data Aggregation and Spend / Return Control – another primary consideration when planning a structure. Finding the proper balance here is essential for several reasons:

  • Smart Shopping sees stronger performance in nearly all cases where it has more data to pull from when making bid decisions. In fact, Google does not recommend running a Smart campaign that converts less than 30 conversions a month.
  • Standard Shopping relies on either tROAS automated bidding – which plays by the same conversion volume rules as Smart Shopping – or more traditional CPC bids among product groups. The more information accessible to base bid adjustments on, the more likely that those bid adjustments will be accurate.
  • Too much consolidation = easily missed opportunities for additional revenue and black holes in performance

Feed Quality – This secondary consideration cannot be overlooked when exploring approaches to Shopping. Are there limiting factors within the data feed which eliminate strategies from our toolkit? The classic example of this is Margin Breakouts – many businesses are unable to generate margins at a SKU level, which prevents the feed manager from grouping products based on margins. This is discussed below in our Profitability section.

Product Categories: The 7-iron of Shopping

Product Type / Categories as a segmentation strategy is likely the most flexible high-level approach to Shopping out there. It can be used to drive efficiency, product launches, or overall growth. In most cases this strategy plays best with Growth KPIs – due to a more spread out structure, it is easier to evenly allocate spend across a larger portion of the feed. This also allows clear visibility into surging product categories, so we easily know what areas of the product load can receive increased investment.

How to break out products into Category campaigns? The same balances referenced in the Data Aggregation / Spend Control section apply here: (1) determine your comfort level between conversion volume and results visibility, then (2) split products into similar category groups that meet the balance you are seeking.

Here’s an example of what this looks like in Smart Shopping:
Example of splitting campaigns into product groups for better control

Profitability: ROI is King

Return on Investment for revenue channels is paramount for certain businesses. To optimize for the highest channel profitability possible, we can segment based on Product Margin.

This requires the client to send an additional field at the Child SKU level, housing the margin field itself on a product-by-product basis. From there we can bucket products into margin groups within a Custom Label for campaign segmentation.

Regarding campaign breakouts, we typically break out into 3 campaigns along these lines:

  • Highest Margin
  • Average / Profitable Margin
  • Low Margin

In some cases, a Loss Margin campaign is relevant as well – but it may make sense for you to block Loss Margin products from campaigns instead. Additionally (assuming enough volume is present), there’s nothing wrong with running more than 3 campaigns!

Here is an example of this structure in action:
Example of campaigns segmented by product margin

This approach plays best with Smart Shopping, but can be used with Standard Shopping as well. If using Standard, we still recommend tROAS bidding.

Historical Performance: Profitability + Aggregation for Complex or Limited Feeds

In situations where some form of Profitability or highly aggregated approach aligns with our goals, but feed information is limited, Shopping efforts can be segmented by Historical Performance. This approach plays best with Smart Shopping – which naturally optimizes towards the 80/20 principle in retail.

Similar to our Profitability section, 3 campaigns are recommended as follows:
Example of campaign structure by historical performance

These segmentations can be effectively grouped by:

  • Revenue
  • Average Order Value
  • Conversion Rate (must use more than 6 months of data, minimum)
  • CPC (similar to Conversion Rate)

Groupings can happen at the ID or Brand level, but ID is more effective where there is enough conversion volume.

If you are a business looking for help with your shopping segmentation strategy, please contact us.