Enhancing Google Shopping Performance through Performance-Based Product Categorisation

In the dynamic landscape of Google Ads, understanding product performance is pivotal. At our agency, we don’t just rely on superficial metrics. We dive deeper, categorising products based on performance. This approach uncovers hidden insights, guides our optimisation strategies, and drastically improves campaign outcomes.

The Challenge: Why Categorise Products by Performance?

Navigating through the Google Ads interface only provides a surface-level view of product performance. To truly understand the nuances of each product’s impact on our campaigns, we need a method that could spotlight our top performers and identify those lagging behind.

We also look at products on an individual profit basis, uncovering which products are driving the business’s profitable growth and which products may be returning decent revenue but might be losing the business money overall.

The Solution: Performance-Based Product Categorisation

Over-index Products: The Cream of the Crop

• Criteria: At least 3 times the average clicks needed for a conversion and performance exceeding the target by 20%.
• Outcome: Identifies top-performing products, allowing for strategic ad spend allocation.

Index Products: Our Reliable Heroes

• Criteria: Clicks equivalent to the average required for a conversion, and performance meeting the set target.
• Outcome: Spotlight on consistent performers. These products form the backbone of our campaigns.

Near-index Products: The Dark Horses

• Criteria: Performance is at least the target minus 20%. No specific click threshold.
• Outcome: Potential future stars. They might not have the highest clicks, but their ROAS and profitability are commendable.

Under-index Products: The Underachievers

• Criteria: Performance doesn’t exceed the target by 20%.
• Outcome: Identifies weak links in the campaign, offering areas for improvement.

No-index Products: The Invisible Ones

• Criteria: Impressions don’t surpass a certain threshold.
• Outcome: Products that are either seasonal or have improperly optimized data, reducing their Google Shopping visibility.

Implementation: Translating Insights into Actions

Historical analysis of these product groups offers a trajectory of performance. By evaluating how products shift between categories over time, we strategise our optimisation efforts.

This rich data allows us to:

1. Refine Ad Spend: By limiting poor-performing groups, we conserve the budget and potentially achieve a higher ROAS.
2. Optimise Product Data: Many products in the ‘No-index’ category often suffer from suboptimal product data. By enhancing this data, we boost their visibility on Google Shopping.

Results: Client Success Stories

Our performance-based product categorisation strategy has been a game-changer for numerous clients. By allocating budgets more efficiently, refining product data, and focusing on potential winners, we’ve consistently elevated campaign ROAS, reduced wasteful ad spend, and improved overall Google Shopping performance.

Here’s an example of the report we can build with this data.

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For this client, we could see that the majority (87%) of their products were not being shown or underperforming. These underperforming products were also nearly 66% of total ad spend, which meant that they were losing the client a lot of money.

On the back of this data we:

• Moved underperforming products to a standard shopping campaign with a Max CPC
  • This allowed us to work out the optimal CPC for those products to return a good ROAS and move back to their normal campaign.
• Removing underperforming products from our PMax campaigns
  • Now the budget is more focussed on our better-performing products and our no-index products, which will hopefully start to show more
• Optimised Merchant Centre data
  • We also took a look into our product data to make changes to titles, descriptions, and other available fields in the hope that the improved data helps the products show for more searches.