This article covers the following topics
- What are Product recommendations?
- Prerequisites for using Product recommendations on Freshmarketer
- Steps to set up Product recommendations in email campaigns
What are Product recommendations?
Product recommendations are a list of products suggested to customers based on their behavior, engagement, interactions, buying patterns, previous transactions, shopping preferences, trends, and more.
Freshmarketer’s Product recommendation engine powered by Freddy AI analyzes customer data and suggests hyper-personalized recommendations that shoppers are likely to buy, improving sales and customer lifetime value.
Prerequisites for using Product recommendations on Freshmarketer
To use product recommendations, products on your Shopify/WooCommerce store need to be synced to Freshmarketer.
Read this article to learn more about product sync to Freshmarketer.
Steps to set up Product recommendations in email campaigns
You can send Product recommendations in bulk email campaigns or as journey emails in customer journeys like abandoned cart recovery.
Once you decide the type of email campaign you want to send,
To add Product recommendations to your email, drag the Product recommendations block under the Content column on the right panel.
Click on Configure to customize the Product recommendations
Select the type of product recommendations you want to offer your customers. Freshmarketer enables you to send different types of Product recommendations such as:
Personalized: Freddy will suggest products based on the customer’s browsing activity and purchase history.
Bestsellers: Freddy will suggest products that have sold well over time and are likely to interest customers.
Trending: Freddy will suggest products that are currently popular among other customers.
Similar products: Freddy will suggest products similar to the ones that customers have abandoned in the past. For this to work, the “Abandoned cart” event should be used in customer journeys.
Bought together: Freddy will suggest products that you can cross-sell based on the customer’s recent orders. For example, a customer who purchases a mobile phone would get recommendations for accessories like chargers, earphones, etc. For this to work, the “Placed order” event should be used in customer journeys.
Latest arrivals [Coming soon]: Recommendations on products that have been recently added to your catalog.
- Add the number of products to recommend and choose the layout to display products.
- You can also add product details like image, title, description, product type, price, and comparison price. Select the information you want to add by clicking on them.
- Add a discount coupon if any.
- Format the button by modifying the button text, color, style, borders, etc.
- You can preview how product recommendations would look for your contacts by selecting their email address.
- Click on Save to return to the email editor.
- Continue setting up the email by clicking on Save and Next.
- Let Freddy generate the subject line, pre-header. Finish adding any other information needed.
- Your email campaign with personalized Product recommendations is ready to send.
- Click on Schedule if you want to send the email campaign at a later time or click on Send campaign to send the campaign immediately.
You can use hyper-personalized Product recommendations on Freshmarketer for your regular marketing campaigns, abandoned cart/session recovery journeys, cross-sell/up-sell, or even a part of post-purchase journeys to drive more conversions and increase order value.
Note: Product recommendations is only applicable for customers with atleast 500 products and 10 orders per product.
Note: If there are insufficient number of products that Freddy can recommend for the type of product recommendation chosen for any particular user, Freddy will use advanced ML insights to suggest other relevant product recommendations ensuring your customers will always get personalized recommendations.Please reach out to firstname.lastname@example.org if you face any challenges while setting up Product recommendations.