The right campaign structure is the foundation of a successful Shopping campaign. The trick is to get a strategy in place—once you have a process it often builds on itself, leading to even greater successes.
There are three elements to effective Shopping Campaigns: strategy, optimization, and the tools or platforms to automate and analyze your campaigns.
Strategy: Consider the 20/60/20 Rule
Retailers can use a version of the 20/60/20 rule when planning their Shopping strategy. The top 20% are high priority stock keeping units (SKUs)—individual top-performing products that drive a disproportionate volume of the top line of revenue. These products should each be placed in single-product product groups leveraging the item id criteria. This allows bids to be set based on each product’s performance.
The middle 60% accounts for the heart of the SKU count. These should be placed into product groups organized by category and/or brand in a structure that aligns with your company’s brand strategy. The objective is to have enough granular control without getting overwhelmed with too many product groups. Advertisers should regularly graduate top-volume products to single-product ad groups when appropriate.
Consider your business and how it approaches selling products. If you regularly run sales by product line (for example, All Running Shoes 30% Off), then having a corresponding product group using Category or Product Type criteria is going to be helpful to manage with. If you work with vendors and incremental budgets, having campaigns with Brand product groups will allow for easy management of those fluctuating ad dollars (and allow you to easily set campaign budget constraints).
The bottom 20% is the catch-all for new or low volume/exploratory products. A broadly targeted product group ensures coverage for everything else in your catalog. As data accumulates for these products, retailers can move them up into the middle section, and eventually promote the all-stars that stand out into the top section.
Pro tip: Exclude your “Everything Else” items across the other campaigns and concentrate them in a single Catch-All. This makes it easy to monitor performance and if too much volume spikes here, dig through it and break it out into prescribed campaigns.
Return on ad spend (ROAS) is a typical way for retailers to analyze performance. To figure out which SKUs are the top performers, product groups can be split into those that produce a high ROAS and those that have a low ROAS. Users can achieve this split manually in Google Ads, which is both time- consuming and error-prone, or they can automate it using Marin Software.
￼￼￼￼￼￼Optimization: Use Marketplace and First-party Signals
Retailers can optimize their Shopping campaigns by leveraging both first- and third-party data to enhance their decision making. Bringing in additional data helps retailers to be more aggressive when they expect the best returns, and to decide when to pull back on products that aren’t selling well.
For example, analyzing inventory quality could influence how a retailer is bidding. If certain products are in stock, but only in very large sizes such as XXL and XXXL, that can be considered low-quality inventory. The retailer may want to decrease the bids or back out of the auction until supplies are replenished.
Inventory quality goes beyond just whether or not a product is in stock, which is what many retailers focus on. Failing to assess the quality of inventory can drive conversion rates down if consumers are clicking product ads only to find the product isn’t available in their size.
Rather than getting hung up on the false- positive of whether products are in or out of stock, retailers should try and get a deeper understanding of high- and low-quality stock to make smarter decisions around which products to bid on.
Automation Can Lead to Better Bidding
Retailers can also use feed and competitor data to make better bidding decisions. Working with an independent platform like Marin, users can track up to 10 competitors in every auction they’re bidding on, to understand if their products are overpriced or underpriced relative to the market. If they discover they’re overpriced compared with their competitors, retailers might not want to bid as heavily on those auctions, because a lot of today’s consumers are driven by price points.
Conversely, retailers may want to double down in auctions where their price is better than competitors’. Retailers are often surprised to find out their prices are less competitive than they thought. This insight can strengthen their decision making and allow retailers to make smart pricing adjustments.
Automated bidding is critical for large retailers managing millions of SKUs, as it enables them to separate the top performers from the rest. Doing this at scale and bringing in data outside of what’s natively available in Google will set them apart from their competition.
To discover other key tips to apply to your Google Shopping campaigns to increase clicks and revenue, download our guide, Shop ‘Til You Click: Creating Shopping Campaigns at Scale. Also be sure to set up a demo to see how Marin’s bidding engine leverages first- and third-party data to automatically adjust bids and keep users ahead of the curve.