Harness AI to show each visitor exactly what they are most likely to be interested in. Choose from various algorithms to present recommendations for anonymous visitors on the homepage, buy it together for a product page, algorithms specifically for emails, items that are on sale from the visitor’s wishlist, or “Buy It Again” for perishables.

Personyze includes different algorithms for different channels and states of the visitor in their journey, and the flexibility to leverage your customer expertise to build algorithms with the exact blend of data and strategies that perform best for you.

Products recommended for you
This machine learning-based algorithm uses the totality of user data to find patterns in how users of various types interacted with all of the items in your catalog, and makes a specific recommendation for any given user.

Frequently bought together:
Recommend cross-sale items that were bought together often with the product they’re currently viewing, have in their cart, or have recently purchased.

Those who viewed this product also viewed/bought:
Suggesting the right alternatives, based on similarity or crowd data, will increase the number of products viewed and time spent in a convenient shopping flow, and reduce the time necessary to find what they’re looking for and convert. 

Those who bought products from this category also bought:
Recommend items frequently bought from other categories by customers who bought from the category they are viewing or just purchased from.

New in stock
Help customers stay up-to-date on items that are new on your shelves, with the option to fine-tune for items added to the site in the last 7 days, before the last visit, or based on visitors’ interests.

View it again/buy it again
Show the items they recently viewed, so they can pick up where they left off, or items they bought in the past so they can easily resupply. 

Fine-tune your product recommendations and make them more relevant with filters:

• Discount = greater or less than X%
• Product feed attributes, such as interest, color, brand, or price. 
• Within X miles of distributions center’s location, offer free shipping
• Visitor’s gender, age, client type, industry, etc.
• Less than X units in stock

Content recommendation algorithms are specifically designed for various pages and scenarios, with a variety of algorithms for the homepage, article pages, search page, and more. 

Those who read this also read this
Shows visitors on a content page what other posts have been most popular among other visitors who read the current post. 

Most popular/commented from this category or topic
Shows the visitor the most viewed, commented, favorited etc. content from the same category or topic as the content currently being viewed. 

Inspired by your recent reading
This recommendation will show a list of most popular content based on those categories most recently read by the visitor. 

Trending now content
This recommendation will show the content that is most popular recently. 

Demographic or CRM
When you have demographic information on visitors, such as gender or age, you can show items that were popular among their similar demographic. Demographic/CRM variables can be any custom variable you have on your visitors.
Those who read this bought these products
This recommendation is good for guide and info pages, and shows products frequently bought by visitors who read the same content currently being read. Technically a product algorithm, but relevant for content pages.

Most popular content from visitor’s favorite author/topic
This will show the visitor the most viewed/commented/liked content from their most read author/topic on the site. 

Now on sale from past interests/left in cart
This recommendation shows the visitor items that have recently changed in price, based on what they previously interacted with or left in their cart. 

Cart abandonment email drip
This type of email recommendation reminds the visitor of items they left in their cart, as well as recommends similar items, in emails sent at intervals determined by you.

Time to resupply
This recommendation type reminds the visitor to re-stock perishable items, and can be sent only at certain intervals determined by you.

Most recommended for you
This email recommendation uses machine learning to detect patterns in the crowd data, and based on the visitor’s characteristics, will show them what they are most likely to be interested in. 

Cross-sell based on last purchase
This email recommendation suggests items which other visitors who made the same purchase as this visitor’s most recent purchase also bought, or items which have a cross-sale association in the catalog with the visitor’s most recent purchase. 

Newly listed items/accessories for you
This email recommendation will suggest items to the visitor that have been newly added, which go together with items previously purchased. 

Send my cart to me email button 
This button on the site allows the visitor to opt-in for their cart to be sent to their email, as a reminder. If their email is not known yet, it can trigger an email form to be launched in the form of a popup, which can then be used for other remarketing emails, as well.