This guide is designed to help fashion brands and retailers understand how Google shopping campaigns should be set up and managed, as well as best practices for on-going optimisation.
The fashion industry continues to both move online and grow in size, with competition at every level and price point becoming more and more fierce and online acquisition channels becoming more competitive.
The fashion industry also has its challenges when it comes to Google shopping. The landscape is deeply impacted by seasonal trends and fashion trends whilst retailers also need to consider and manage stock, profit margin, competitor activity and much, much more. With this in mind, we decided to pull together an article focused on how fashion brands can use advanced Google Shopping techniques to manage these factors and drive an improved ROI.
Over the last few years, the visibility of Google Shopping listings has increased considerably, with shopping ads now driving 80% of Google Ads non-branded, retail query traffic. In terms of the benefits for merchants, Google Shopping typically delivers lower CPCs and a higher conversion rate than standard text ads and is generally one of the quickest and most efficient ways to promote products online.
Google Shopping delivers ads by indexing product data within your shopping feed to match a users search query. It also uses historical and competitor data matching GTINs and MPNs to identify which products are best to serve.
The initial overhead around shopping is often lower (dependent on product data, which we’ll come onto later) than search ads, as you don’t need to build out ads and campaigns for thousands of keywords. For retailers new to paid media, launching Google Shopping is one of the most efficient ways to build out your activity. You can then run search term reports to identify queries which deliver sales and perform well, gradually building out product level text ad campaigns based on these keywords.
The number of products served in the SERP within shopping has steadily increased from 5 to 30, with a range of new ad formats being rolled out which take up more space on the page. In addition to this, an increase in mobile traffic (where shopping ads are more prominent) has also contributed to the massive increase in Google Shopping traffic.
Firstly, to create some context, we should cover the difference between Standard Shopping campaigns and Smart Shopping campaigns.
In May 2018 Google announced its new solution for Google Shopping, this is what we have come to know as Smart Shopping. So, what is Smart shopping? Google takes your product feed, campaign objective, budget and country settings and uses its machines learning to do the rest. Google's algorithm decides where and when to serve your ads, how much to bid, and which audiences to target.
Simplified campaign management – by leaving the optimisation to Google, this reduces the resource required to fully manage the campaign yourself, and Google’s machine learning capabilities allow for large amounts of data and optimisations to be processed quickly which a human would struggle to compete with.
Smart shopping combines search with display which allows advertisers to expand their reach, this includes both dynamic remarketing and dynamic prospecting.
Smart Shopping uses machine learning to serve the most relevant combinations of your visual and textual assets to deliver the highest return.
Reporting is limited - placement, search query and audience data is not available. Whilst this is likely due to Google making all optimisations, and therefore the campaign manager has no use for the data, the lack of visibility means you have no knowledge of what may and may not be working. These could also be learnings you could have applied to other campaigns
Search query reporting is also significant as you want to see which products are serving to which queries. You would also want to add in the top performers as exact match keywords
Attribution becomes more difficult
No ability to add negatives i.e. irrelevant or low intent search terms
No control over your products. Google recommends advertisers target all available products in one campaign which Google then manages. However this means all products will be treated the same i.e. Google won’t factor into bidding the following; products on sale which an advertiser may want to push more aggressively, products with higher margins, high value products which you want more exposure on, or products with few sizes left in stock which you may want to pull back on etc.
We often find that Smart Shopping can spend more budget and drive higher CPCs on under-performing products. Go to Reports > Pre-Defined Reports (Dimensions) > Shopping > Item ID. You want to make sure that the top spending SKUs are driving the best return. We often find that higher ROI SKUs and the GA top selling products are not being aggressively pushed by Google. They will often find a number of SKUs which achieve around the ROAS target and then simply allocate the spend here whereas there is much more opportunity to push products driving multiple sales at a strong ROI but only have a low impression share.
All of our more sophisticated strategies (below) are also not possible via smart shopping
In summary, whilst Smart Shopping could be ideal for smaller businesses with small budgets and less time, for more advanced advertisers we would recommend building standard shopping campaigns and taking advantage of the following techniques.
It all begins with the campaign structure, see an example for a shoe retailer below.
Split your campaigns by brand or product category, followed by subcategories via product data and then ID, this allows for greater control of bids and management. This will also enable an advertiser to apply location, audience and device bids by category and most importantly cross match negatives.
Another benefit of splitting by product IDs, is if a product goes out of stock or is almost out of stock, you can either bid lower or exclude altogether.
Campaign (By Brand)
Ad group (Product Category)
Subcategories for bid management, and to exclude all other products.
Leather (broken down by Product ID)
Fabric (Broken down by product ID)
Exclude all other products which fall under the category Women’s Trainers.
Heel (broken down by Product ID)
Flat (broken down by Product ID)
Exclude all other products which fall under the category Women’s Sandals.
One of the other main benefits of a granular structure is cross match negative keywords. In this example, you would add in Converse as a phrase match negative to all other campaigns to ensure only Converse appears to Converse queries. This sounds basic but as you move down to the next category and excluding the ph
rase leather from non-leather ad groups, it will massively improve the SKUs you are serving to user queries.
Another level would be taking all your campaigns and separating these by brand (or manufacturer) and generic queries. The campaigns themselves will be a duplicate of one another, however your generics will include the brand terms as phrase match negatives, and your brand campaign will need to hold a list of all generic exact match terms (to be continuously updated either manually or by a script). We have a script which automatically updates the generics negatives into your branded campaign.
You will then set your brand campaign priority as low, and your non brand campaign to medium or high, your brand campaign will also have a higher bid than your generics. The idea is Google will initially prioritise the non-brand (high priority) campaign first, this campaign remember also has the lowest bid, but if the search term includes the brand term which is of higher value to us, it will then switch to serving ads from the low priority campaign which has the highest bid.
More than likely - conversion rate and user intent will be much higher when a user searches for the brand and you would want to increase your bids here. You can also reduce spend on generics and bid higher only on past visitors for example.
You can use custom labels in your feed to subdivide products based on variables i.e. best sellers, sales items etc. You can then use these labels for monitoring, reporting and bidding.
We use a feed solution which automatically populates top sellers (with higher than average ROIs) with a custom label. We then have a high priority campaign which targets all SKUs with that custom label. Top sellers are then pushed and then if the ROI drops the label is removed and the SKU begins serving in its original campaign.
Similarly, we use our Google analytics API to automatically monitor SKU coverage on top sellers. If top sellers on site are driving only minimal traffic - we will get an alert to review. We can then ensure best sellers on site have good coverage across Google.
We use a number of scripts to automatically monitor and apply negative keywords as well, as we have seen many examples where new SKUs can appear for broad, irrelevant terms. Although we monitor this regularly - we have scripts which flag under-performing terms so we can quickly identify and exclude these. Likewise we have a SKU level script which flags when a product spends over a certain amount and does not convert.
Another good use of technology within Google shopping is where we have applied logic based on the stock available. There are hundreds of possibilities to be more sophisticated with APIs and data sources to optimise your shopping e.g. profit margin, weather, competition, site data etc but one example we have seen work effectively is stock. We run sizing analyses for clients looking at both purchase rate by size but also returns rate (to help support sizing information on PDPs). For one retailer we have set up a script which flags when our main small sizes go out of stock and when the larger (under-performing SKUs) begin to increase in spend we get an alert. We also do this for shoe sizes as well and have seen considerable improvements to ROI by reducing spend when top sizes are OOS. Shopping titles do not always fit in the size and it’s important to not waste ad spend if users land on the site and then realise the most common size is out of stock before bouncing.
Here you can apply first party audience lists based on users you want to treat differently and bid higher or lower based on their likelihood to convert. You can create audiences from your customer lists or analytics audiences and segment by on-site behaviour and purchase history i.e. general site visitors, basket abandoners, recent converters and loyal customers.
You can bid higher or lower by Geo (City level recommended), Demo (Age and Gender) or Device. We would recommend initially targeting all and based on the data you gather optimise accordingly.
Across Google shopping campaigns you are reliant on Google showing your products to users based on your product titles and descriptions. By applying negative keywords, you can avoid paying for unwanted clicks i.e. for irrelevant search terms, and can better reach your target audience. For instance, if you are a luxury fashion brand you may not want your ads to show for users when they use phrases such as ‘cheap’, or show ads when users search with the phrase ‘returns’ or ‘head office’. You can apply these negatives at an account, campaign or ad group level.
Where Google uses your feed data to match products to a user’s query, you want to ensure your shopping feed is optimised. There is a huge amount of detail needed for optimising feeds, but at a top level you want to make sure that your product title includes the core terms users are searching before buying. You then want to massively build out descriptions to ensure they include all possible searches which are relevant to your product. Product type is an optional field which also factors into ranking and a tactic here is to add in competitor terms (at a lower layer to not jeopardise account structure). This will help you appear against similar and competitor brands.
There are various bid strategies an advertiser can take advantage of in the Google ads platform i.e. maximise clicks, maximise conversion value, target return on ad spend and ECPC. These strategies are great in driving as many clicks as possible for your brand (Maximise clicks), or setting bids to maximise your conversion value (Maximise Conversion Value) or ROAS (Target ROAS), or even adjusting your manual bids on the likelihood of that click resulting in a conversions (ECPC).
We would initially recommend managing your campaigns with manual bidding, this will allow you to ensure bids are well optimised, and towards the top selling categories and SKUs as well as considering profit margin.