Frequently Asked Questions

How do I customize the look and feel of the recommendations presented in my shop?

You can customize the look and feel of the recommendations using the "Widget Customizer" from Siggy.

The customizations are limited to number of items recommended, element displayed on the recommendation, color options.

What does it mean when you turn on the recommendation widget?

When the recommendation widget is turned on, a product recommendation section will be added to the Shopify product pages. The recommendation widget is turned off by default when you first install Siggy. Shopify requires the user to manually turn on any potential presentation changes to their storefront.

How is the recommendation list generated, can I manually change the order of the recommendations?

The recommendation list is generated by our pre-trained AI algorithm, we index your product and calculate the relevance score for each product and generate the recommendation list based on the most relevant products. We currently do not support the ability to manually change the order of the recommendations (e.g. globally promoted products, etc).

If you are interested to learn more about our recommendation algorithm, check out this blog post.

Where are the recommendations displayed? 

The recommendations are displayed on individual product pages under the main product area.  You can also customize the placement of the recommendation widget (learn more).

What personal information do you collect from my Shop and my customers?

We collect basic information from your shops such as your Shop name, ID, and email contact to create a user account in Siggy. We do not collect any customer information from your Shopify shop. For more information, see our privacy policy.

Is there a data limitation to Siggy? (For example, product catalog size, order data)

We currently impose a limit of 5000 products a store can have. This is to prevent abuse of the app. Siggy is currently a free app service. It means the infrastructure and computing cost adds up. We are working towards a usage model that works for stores of all sizes.