Product Recommendations Questions and Answers

  • Updated

Can I create a machine-learning (ML) model for Product Recommendations?

At present, you can use only the ML models made by Optimizely.

Can I import and use external data with ML models?

Can I import and use external data, such as Geo data regarding point-of-sale location, store type, and Enterprise resource planning (ERP) stock information in the ML models for product recommendations?

This kind of data would not be directly imported into the ML models, but the models could use this attribution contextually to refine the results returned from the model. For instance, if a product can only be sold in location A or to customer A, you can set up tracking to ensure that only the appropriate users can see the recommended products. 

The specific focus is on User > info parameter in JSON variable tracking format. You can pass information about a specific user as they enter the site, such as location=A. Product Recommendations uses this information to only provide recommendations or products available for that location to that user.

Stock information is passed to the engine through the product feed. Each product has a quantity of stock assigned to it. You can link your ERP information to send the stock levels to the feed. If you have high or quick turnover, update the feed periodically to ensure up-to-date stock levels. Typically, clients upload one feed daily, but the stock levels only update in Product Recommendations when the feed is refreshed.

How do Product Recommendations work with personalization?

How does your recommendation engine work with personalization, and how are insights used for product recommendations for personalized experience and vice versa? Are there two separate products or module architectures, or are personalization and product recommendation closely linked?

Product Recommendations was originally built as a standalone product and, therefore, has a separate architecture from the other forms of personalization that Optimizely offers. They are, in essence, two separate products. However, you can link the tools together. For instance, you could use visitor group personalization only to render a block of product recommendations when a user meets certain criteria. You could also leverage personalized Search & Navigation to create automatic landing pages, at which point you could leverage a product recommendations block on this page.

You also can link the A/B testing capabilities within Optimizely Content Management System (CMS) and Optimizely Feature Experimentation to the A/B testing capabilities within product recommendations. Still, there is currently no standard, native way of doing so. Instead, you must do custom work to sync cookies between the products.

How do I get started with Product Recommendations?

The two fundamentals of product recommendations are product feed and tracking.

  • Product feed – provides the products that can be recommended and contains the relevant information for a given product, such as title, price, category, and stock. You can pass any attribution in the feed. If you are using the Optimizely Commerce platform, there is an export product feed job within the Admin console that automatically creates an XML feed for the client:
  • Tracking – relates to page, click, and order tracking. Each page type needs to be tracked. Then, widgets need to be tracked to ensure all clicks are marked, and this needs to be consistent across the entire user journey.

Work with Optimizely to set up top-level widgets. However, you can affect the widgets through merchandising campaigns. See Set up an Optimizely Product Recommendations campaign.

Can I run an ML-based recommendation?

Can I run my ML-based recommendation and showcase these through your engine if we do modeling beyond what Optimizely provides?

You cannot do this directly through Product Recommendations, but you can combine your modeling with the Optimizely recommendations. For example, suppose you have a widget that serves ten recommendations. In that case, the first five recommendations can come from your in-house ML model, and the remaining five can come from Optimizely. You can do this because you have complete front-end rendering control over the widget's display. 

However, Optimizely cannot track the performance of those first five recommendations served by your ML model unless there is some way for Optimizely to serve those products manually.

How does Optimizely track KPIs?

Can you elaborate on how you help us track KPIs related to product recommendations, such as engagement, conversions, and sales?

Optimizely tracks KPIs through the tracking configuration during the onboarding phase. The main KPIs Optimizely tracks are as follows:

The following data are described from a product recommendations widget perspective. Optimizely tracks the full user journey and can also track performance from a top level, which can help provide even greater insights into your site's performance.
  • Clicks – Optimizely tracks every click made on a widget and compares this against the number of impressions generated. (Impressions are the number of times Optimizely sends a widget. For example: if one widget is served on the homepage and the page is refreshed ten times, then this results in 10 impressions, (one impression per page load).

    Clicks and impressions calculate the click-through rate, which provides insights into how many customers are engaging with recommendations

  • Orders – Optimizely tracks every order that is carried out on the site, and Optimizely can identify whether an order contains a product from a recommendations widget. This lets Optimizely track how many orders contained products from a personalized experience

    The product recommendations tracking has a 30-day attribution window, meaning that if a user clicks on a recommendation on day 1, as long as they purchase this product within 30 days, it is attributed towards Optimizely.

  • Products sold – Similar to orders, Optimizely can identify whether products are contained in a user's basket and order, and can identify which of those products were added to the basket through a widget or recommendation block.
  • Revenue – From identifying the number of products sold through a recommendation widget, Optimizely can extract how much revenue was derived directly from a widget engagement.

    If a user has a basket of 10 products totaling to $100 and one of those products was added to the basket through a recommendation widget, at a value of $10, when the user completes their order, $10 of revenue is assigned to revenue generated by Optimizely.

  • Conversion – Conversion is tracked in the form of clicks to purchase (CTP). This is a ratio between the number of clicks on a widget and the number of items purchased. So, if there are 1,000 clicks and 500 items purchased from those clicks, then CTP would be 50%