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You’ve setup your online clothing store on Shopify. You’ve got your inventory filled with the hottest brands. You’ve got your marketing strategy nailed. You’re ready to conquer the world.
And then: this
Somehow that needs to be turned into this:
You can think of a product feed as essentially a massive spreadsheet with each row being a product and each column being a piece of data on that given row’s product. Think size, color, price, brand, shipping weight, etc. This spreadsheet is then sent off to various platforms such as Google Merchant Center, Shopify, Bing Merchant Center, Facebook, and Amazon. Now, the actual data can be formatted in any number of ways such as XML, JSON, plain text, but the format doesn’t change anything in terms of transformations (as long as you’re using a service). Product feeds are also being used for other text based platforms as well now, and with volume and variability of products, feeds have become a must in 2019.
So you have this gargantuan inventory filled with thousands of products. Each of these products in one form or another is going to be viewed by a customer — whether that be a shopping ad, a text-based ad on a search engine, a display ad, a video ad, or a social ad. This product will eventually be seen by a customer. We use a product feed to break out the individual pieces of data into every row’s columns. How these individual pieces of data on products are utilized, combined, and transformed will make the difference in whether your user clicks or even looks at your ad.
YES. Here’s why:
It is worth noting, you will still need to keep an eye on things. Not every management service is perfect, and there can certainly be problems caused on the feed management service’s side.
For the purpose of example, I’m going to make up a small feed of clothing products.
ID | Product Title | Price | Size | Gender | Brand |
1 | Jacket | 23.99 | L | Mens | REI |
2 | Sweater | 45.99 | XL | Womens | JCPenney |
3 | Hat | 36.99 | S | Unisex | Adidas |
4 | Fancy Sweater | 219.99 | M | Womens | JCPenney |
It’s always helpful to remember that a product feed is just a spreadsheet. Each row is a product. Each column is a piece of data on that product. Given the type of data in the column, there are different types of operations we can perform on that data. We also need to understand some basic logical operations to help us fine tune what we want to transform. Note that while most managed feed services will support the all of these, some may not, so make sure to do your research on which one you choose.
The logic might seem a bit complicated, and depending on the scale of your feed it might seem like overkill. But once you start working with large feeds, these types of transformations are a must have. Also the first 10 operations are just the tip of the iceberg of the kind of optimizations you can do. There are also other types of operations, but while regex is the most complex to understand and apply, it really can do almost anything.
Given the example table above, here is an example title optimization we could make using the some of operations we discussed previously. Say we wanted the ad title to include the brand and the price, but ONLY include the price if it’s less than $100.00. We could easily do this by using an if conditional (operation 2) combined with a basic mathematical conditional (operation 8) [price < 100] to transform this title. How you apply these conditionals is dependent on the service you use, but the logic is exactly the same. (Side note, the double quotes just denote a piece of text in between those quotes. So ” ” would simply denote an empty space). The formula that we would then want would be Final Ad Title = [Gender + ” ” + Brand + ” ” + Product Title + Size + “|” + (IF Price < 100 then + ” $” + Price). This would give us the following titles:
ID | Final Title |
1 | Mens REI Jacket | L $23.99 |
2 | Womens JCPenney Sweater | XL $45.99 |
3 | Unisex Adidas Hat | S $36.99 |
4 | Womens JCPenney Fancy Sweater | M |
This can allow us fine tuned control over things we may or may not want the customer to see initially in an ad like a high price. Even with this superficial example, you can see the massive power that data transformations can have.
The optimal title length for both Google and Bing is around 60-80 characters. Google will allow up to 150, but it is recommended to keep it at or under 70 as anything after is being truncated visually. Another important reason to keep title lengths not overly long is that different devices will have vastly varying amounts of space to display your ads. This means that on mobile devices (which is probably the majority of your traffic or will be soon), there will be an even smaller amount of space for your ad, meaning more truncated title. Shoot for 60-80 characters. Some truncation is unavoidable, but the imperative thing is that most important data goes left to right, so that the least important things get truncated if they do end up being truncated. Make sure to optimize so that you’re using as much space as you can, without truncating the most vital information in your titles.
Product feeds and data transformations are an absolute must in 2019. Here at Digital Position we take utmost care in managing and maintaining your feeds.
You need someone in your corner willing to track, strategize, and, not just manage, but absolutely conquer your marketing. Luckily for you, that’s what we do best.
We’re ready when you are.
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