Search analysis: An approach to Google Shopping for fashion brands
“Necessary evil”, “the Google tax”, “a problem we know we have but don’t really know how to solve” — all things we’ve heard from major fashion brands when it comes to Google Shopping.
Fashion brands have lots of products and release new products frequently, meaning they have a massive task to generate and enrich product data constantly.
Google Shopping is a must-have to stay competitive, but it is hard to track how you are performing and make sure you aren’t missing opportunities.
Most brands know that they have an issue with product data quality which affects outcomes on Google and causes increased spend. Poor data also causes lower quality ads which reduces brand trust and value, and causes longer-term damage.
Brands who can fix these issues will be at a major competitive advantage. They will be able to reduce spend thanks to better data increasing relevance and spend more effectively with actionable insights. They will also have more time to manage the process and reach more customers.
Are you doing everything you can to create an effective approach to Google Shopping? Read on to find out!
Utilise data to optimise your search
Your Google Shopping ads are ranked by a number of features, each of which impact where your ad appears in relevant search results. When looking at how to optimise Google Shopping campaigns, data is key. Similarly, When it comes to Google Shopping for fashion brands, product data is the most important feature to get right.
Product data makes sure your ads appear in the right place. It improves your ranking and helps reduce cost-per-acquisition across the board in your Google Shopping campaigns. Here are a few elements of intelligent product data management that you should make a priority:
The more relevant your product to your consumer, the less it costs to keep it visible and the more likely it is to convert. Making sure that your Google Shopping ads implement as much product attribute data (such as colour, size, target audience) as possible is key. Shoppers don’t search in broad terms, they look for items that fit specific criteria, and the Google Shopping result page allows them to filter search results by those criteria even further. Make sure your data is in place to help your products meet said criteria.
Shoppers are going to read product descriptions to verify that Google’s showing them what they want. Visibility gets your foot in the door, but descriptiveness is what leads to conversions. Figuring out the most important information in your product data and then front-loading your product descriptions with the right attributes will convince customers that they’ve found precisely what they’re looking for. This leads to clicks and conversions.
Missing and mislabelled data, as well as invalid attributes, can fundamentally undermine your efforts. To err is human, but Google Shopping for fashion brands can be made all the more accurate, not to mention less time-consuming, with intelligent product data software solutions.
The more you know your customers, the better. Keyword research tools can help you find the words and phrases that your customers are actually using to search for your products. Updating your product descriptions and data to better reflect the language used by your audience can make them all the more likely to appear in the top results. This increases their chances of converting.
The most effective way to gain insights into your customers, however, is real customer data. You need to make sure that you are collecting information on what works and why. AI-driven, ecommerce platforms can help you do that. This is a point to which we will return.
How should you be optimising your search presence?
When optimising your search ads, Google already outlines some of the key recommendations that can make your products more visible:
- Ensure that the appropriate attributes (such as price, brand, material, pattern, size, target age, gender etc.) are included in your product data.
- The more detailed and deep your product type groupings, the better. Structured product listings that separate broad categories into more specific groups help Shopping campaigns bid on different items and product groups more effectively.
- Be descriptive with your product titles. A product with a title like “Brand red women’s t-shirt small” is more likely to be relevant to a shopper’s search than “Brand t-shirt”.
- Use accurate, high-quality photos of the products that can give the right impression of your product. For fashion brands, products modelled by actual people do even better.
- Use the right search filter terms. If your t-shirt is “magenta”, this might be the most accurate term to describe your item. But, using the broader filter term ‘Red’ will make sure it appears in the less specific colour search filters supplied by Google shopping.
- Separate item variations with different product IDs. This way, your green designer clutch won’t be appearing when customers search for a blue clutch in the same brand.
Data is invaluable, but it is inefficient for a fashion brand with hundreds or thousands of products to manage it all by hand. Automated solutions are making it much easier to collect, organise and import product data from across the web – ideally this process should be integrated with your product data management so that you can instantly action the information received.
This way, you can update listings on Google Shopping, as well as your own site and any other third-party platforms with accurate, consistent data. It’s more convenient, effective, and less resource/labour intensive to automate when possible.
Creating an effective campaign strategy
Google’s best practice recommendations, when combined with the priorities listed above, give you the tools you need to create effective, low-cost ads that have a much greater chance of leading to conversion. But how does that translate into a campaign strategy?
Segment campaigns based on common key variables
Which ads your customers are likely to click depends on which items are most popular amongst them. This can be affected by factors such as which sizes are in stock, price range, seasonality, and which items are on sale.
Google Shopping only provides seven default labels to segment your campaign (Category, Brand, Item ID, Condition, Product Type, Channel, and Channel exclusivity.)
However, it does offer additional custom labels that you can use to segment product/ad groups based on the factors that impact your sales the most, including which items fall into which season and which are available.
This allows you to easily sort Shopping Ads and products into different groups and move them from one group to the other. This segmentation allows smarter bidding, meaning that, for instance, you’re not bidding high on items that aren’t in stock simply because they have the same category and product type as another item that’s currently selling well.
Using custom labels
Each custom label should be used effectively as we only have five of them. Although it can depend on your product, typical custom labels to set are:
- Price: Depending on the price of the item, labels should be split into 5-7 price bands. For example, ‘under £10’, ‘£10 to £20’, ‘£20 to £30’ and so on.
- Stock Size Availability: These labels show the kinds of sizes available for any product, for example: ‘No Popular Sizes Left’, ‘Some Sizes Missing’ and ‘All Sizes in Stock’.
- Sale Status: These labels show if a particular item is on sale or not, e.g. ‘Clearance Sale’, ‘On Sale’ and ‘Not on Sale’.
- Season: This label allows you to determine whether a product is seasonal. For example, ‘Spring/Summer’, ‘Autumn/Winter’ and ‘Not Seasonal’.
- Year: This label displays which year the product was launched, e.g. ‘2016’, 2017’ etc.
Remember, you can attach multiple set of custom labels to an item in order to make your product data as specific as possible. When you apply custom labels, they will require manually developing the shopping feed output.
Alternatively, a more efficient way to organise the labels is to use an ecommerce platform which allows for logical custom fields. For example, some labels can be fixed (such as ‘year’ or ‘season’) but others will need to be flexible (like ‘sale status’ and ‘stock size availability’). Accounting for all of these variables will help more people find your product, leading to more conversions.
Use shopping campaign hierarchies
When your products/ad groups are effectively segmented, you can establish a hierarchy that can make for even more insightful bidding. Rather than having separate groups for Autumn/Winter items, Spring/Summer items, On Sale items, Off-Sale items, All Sizes in Stock and Some Sizes in stock, you can fold the different groups into one another.
For instance, you can have both groups of seasonal items as a sub-category of items currently on sale. Then, within those seasonal categories, you can further divide them into which items have All Sizes in Stock and which items have Some Sizes In Stock. This allows for much more precise bidding, as well as the easy movement of items from one sub-category to another.
Segmenting your campaign effectively is going to be key. The more specific you can make your ad groups and their targeting methods, the better you can target customers looking for niche products and the easier it is to manage the CPA of campaigns individually.
Does AI create a more effective campaign strategy?
An effective campaign strategy in Google Shopping for fashion brands is not easy to handle manually. It takes both time and insight to maximise team efficiency and deliver improvements on CPA, CPC, impressions, and ROAS.
However, the right ecommerce product data platform can automate a lot of the work. This will allow you to focus your time on making the right choices and use insights offered by AI to improve that decision-making even further. This will improve conversions, lower costs and deliver outcomes on KPIs more effectively.
Whether you’re using purpose-built ecommerce solutions or putting the time and effort in yourself, an effective approach to Google Shopping is key to increasing visibility and conversions while reducing overspending.
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