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Personal Recommendations is a machine learning algorithm that predicts which product a customer is most likely to purchase next. The algorithm analyzes each customer’s individual behavior, the behavior of look-a-like customers, and the catalog itself to surface the most relevant items for every shopper. Maestra offers two variations of the Personal Recommendations algorithm: a standard version that recalculates once a day, and an event-based version that recalculates in real time. Choose the one that best matches the surface where you plan to show recommendations.

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Personal Recommendations

Personal Recommendations is a machine learning algorithm that predicts which product a customer wants to buy. It takes into account:
  • All of the customer’s product views.
  • The customer’s order history.
  • Wishlist and other list actions the customer has taken.
  • The behavior of similar customers, identified through Look-A-Like algorithms.

Algorithm type

Personal Recommendations

Audience

Identified customers

How it is invoked

API (recommendation widget), email

Recalculation frequency

Once per day
Automatic checks. Before a product is shown to a customer, Maestra automatically verifies that:
  • The product is available in the customer’s delivery zone.
  • The product’s brand matches the customer (relevant for multi-brand projects).
  • The customer has not already purchased the product.
Use cases. Personal Recommendations works best for mechanics that don’t require an immediate reaction to customer behavior, such as:
  • Next-order offers in triggered or bulk emails.
  • Reactivation campaigns for lapsed customers.
You can create up to 5 Personal Recommendations algorithms per project.

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Event-Based Personal Recommendations

Event-Based Personal Recommendations is a machine learning algorithm that predicts which product a customer wants to buy and recalculates the recommendation set in real time as the customer browses, adds items to cart, or completes other actions on your site.

Algorithm type

Personal Recommendations

Audience

Identified and anonymous customers

How it is invoked

API (recommendation widget), email

Recalculation frequency

Real time
Automatic checks. The algorithm runs the same automatic validations as the standard Personal Recommendations algorithm:
  • The product is available in the customer’s delivery zone.
  • The product’s brand matches the customer.
  • The customer has not already purchased the product.
Use cases. Because the algorithm responds instantly to customer activity, it is a good fit for surfaces where the customer expects fresh, in-session relevance:
  • Home page.
  • Customer account pages.
  • 404 / not-found pages.
  • Search results pages.
You can create 1 Event-Based Personal Recommendations algorithm per project.

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Create the algorithm

Follow these steps to set up a Personal Recommendations algorithm in Maestra.
1

Open Product Recommendations

In the main menu, go to Content → Product Recommendations and click Add mechanic.
2

Choose the algorithm

Select Personal Recommendations or Event-Based Personal Recommendations, depending on the surface and use case you want to power.
3

Name the algorithm

Give the algorithm a clear, descriptive name so you can identify it later in widgets, emails, and reports. Click Continue.
4

Configure general settings

Adjust the general settings for the algorithm. If you only want recommendations to be drawn from a specific part of your catalog, choose a scheduled segment of products. This is optional — leave it empty to recommend from the full catalog.
5

Launch the algorithm

Activate the algorithm. After launch, the Product Recommendations page shows the algorithm’s current status and the timestamp of its most recent update.

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After launch

Once the algorithm is live:
  • The Product Recommendations page displays the current status of the algorithm and the last update time, so you can confirm that data is being refreshed on schedule.
  • You can plug the algorithm into a recommendation widget on your website via the API, or insert it into transactional and bulk emails.
  • Standard Personal Recommendations updates once per day. Event-Based Personal Recommendations updates the recommendation set in real time as new customer events arrive.
Pair Personal Recommendations with a fallback algorithm (for example, bestsellers or new arrivals) so that customers with too little behavioral data still see a relevant set of products.

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Choosing between the two algorithms

Use this quick comparison to pick the right variant for each placement.
PropertyPersonal RecommendationsEvent-Based Personal Recommendations
AudienceIdentified customersIdentified and anonymous customers
RecalculationOnce per dayReal time
Best forEmail next-order offers, reactivationHome page, account, 404, search
Limit per project5 algorithms1 algorithm
Automatic checksStock zone, brand, already purchasedStock zone, brand, already purchased
InvocationAPI widget, emailAPI widget, email
Both algorithms automatically exclude products the customer has already bought, so you don’t need to add a manual filter for purchased items.