Data Source Product Tags allow you to reduce the number of rules you create from numerous to one. You can now recommend products that have the same product tag as your input product tags with fewer rules.

Rebuy Tag Patterns are available in all Data Sources. You can access this by selecting the Data Source you would like to edit under Data Sources > Edit.

In the Data Source, create or modify a rule and select Product Tags as the IF condition. Select any qualifier (with the exception of the ‘Regular Expression’ option).

As you enter your product tags in a comma-separated manner, they will populate below the Product Tags section. This allows you to see your tags in real time and remove them as necessary .

In the RETURN section of your Data Source, you will now see an option that says Contains Matched Input Tags.

Selecting this option will return products that match the tags that you input into the IF section of the rule. For example, entering the product tag Sunglasses and selecting Contains Matched Input Tags will now recommend products with a tag of Sunglasses.

You can also choose to return products with specific tags of your choosing using the Contains field. Product tags have to be comma separated in order to include multiple tags. Note that if you list multiple tags in this field, Rebuy will search for products that Contain All of the tags listed.

For advanced users, we have also now built out compatibility with Regular Expression. Using Regular Expression as a condition qualifier will allow you to select product tags based off the Regular Expression statement you produce.

Regular expressions are like super-powered word detectives.

Imagine you have a super code that helps you find exactly what you're looking for in a sea of words.

In Shopify, where Merchants sell all sorts of things, you can use these codes (regular expressions) to sort products super quickly when you apply them to product tags.

For instance, say you want to use a "If... Product with tags" rule in Rebuy to recommend only products that are red in color. You might use a Regular Expression like "color:red" in a product's tagging convention, so that Rebuy and other apps can easily "find the word after 'color:'". So, if they see a tag like "color:red" on a stylish dress, you know the dress is red.

The point of using RegEx (Regular Expression) rules in your Data Sources is to minimize the number of rules required to get a high degree of relevancy among your product recommendations:

Data Source Example

Formatting Example:
This isn't the only way to format Regular Expressions but it seems to be the most common in an e-commerce setting:

"category:value"

Where "category" would represent, for example "color", like in the demo data source above; and "value" would represent the color itself - in this case "red".

When inputting Regular Expression categories in your Data Source, you'll need to format it like this: "/category:.*/"; or "/color:.*/" as in the case of our demo Data Source above.

We're telling the Data Source's input to look for products tagged with the same color by using RegEx in our tagging convention. Using a colon ":" to separate the category from the value, the input requires a "/" before the category, and ".*/" after the colon when we return Products with tags > contains Matched Input Tags in order for the Data Source to output what we're expecting.

Essentially, it's going to match the "value" in our RegEx, which in this case is the color "red" on product recommendations output by this rule.

Regular Expressions can make finding products in shops with thousands of SKUs smarter and faster. 🛍️🔍