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Hi there!

I'm looking to create personalized upsell flows in Klaviyo. While I'm familiar with setting up upsells and cross-sells using segments, I'm interested in learning how to set up more granular product recommendations.

For example, if a customer purchases blue leggings, I’d like to automatically recommend a matching blue sports bra and a white purse in a follow-up email. Does anyone have experience with automating this level of specificity in Klaviyo? Is there an app or integration that can help with this?

I've attached an example from a brand I came across. Does anyone know how to implement recommendations like this in Klaviyo?

Thanks in advance for your help!

 

Steps to Set Up Granular Product Recommendations in Klaviyo

  1. Create a Custom Property:
    • In Klaviyo, you can create custom properties for products. For example, when a customer buys blue leggings, you can assign related products (like a blue sports bra and a white purse) to that purchase. This will allow you to pull relevant recommendations later.
  2. Set Up a Triggered Flow:
    • Create a new flow that triggers based on a specific event, such as "Placed Order." This flow will send follow-up emails to customers after they make a purchase.
  3. Use Conditional Splits:
    • Within your flow, use conditional splits to determine the products purchased. For instance, if a customer purchased blue leggings, set a condition that checks for that specific product.
  4. Dynamic Product Blocks:
    • Use Klaviyo’s dynamic product blocks in your email template. You can pull in specific products based on the customer’s previous purchases. This allows you to display the recommended blue sports bra and white purse automatically.
  5. Set Up Product Recommendations:
    • In the dynamic product block, you can either manually add the recommended products based on the specific purchase or use Klaviyo's recommendation engine, which analyzes past purchases to suggest complementary items.
  6. Test Your Flow:
    • Before launching, test your flow with test customers or dummy data to ensure that the recommendations appear correctly based on the purchased items.

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