Getting started with Smart Recommender

Marketing efforts can be expensive, especially when targeting potential customers. Without the right strategy, visitors may leave your website without making a purchase, resulting in a wasted marketing budget. Discounts and promotions can help, but for those nearest to the conversion funnel, merely displaying related content might not be enough.

Smart Recommender is an AI-powered recommendation system designed to optimize your website's performance. By suggesting products aligned with your potential customers' interests, Smart Recommender enhances your chances of conversion. It focuses on providing personalized, contextual, and relevant content at every stage of the customer journey.

Smart Recommender not only boosts conversions and conversion rates but also enhances the Average Order Value (AOV). Delivering tailored recommendations, users are more likely to engage, spending more time on your site and increasing Click-Through Rates (CTRs). This intelligent solution transforms your marketing budget into a strategic investment, ensuring a higher return on investment and more satisfied customers

Product Catalog of Smart Recommender

First of all, there should be data about what users do and what they are interested in to be trained. This data is collected from the desktop and mobile web which can also be called clickstream data and is processed to be used on recommendations on the Web, Email, App, InStory, and Web Push.Additionally, the catalog of the stores should be collected in order to generate recommended product lists for each algorithm.

You can collect product catalog information in three different ways:

• Clickstream data
• Catalog Integration through XML 
• Catalog Integration through API

Algorithms of Smart Recommender

Smart Recommender can suggest products using various algorithms that align with the interests of your potential users. The machine learning-driven algorithms of Smart Recommender can be applied to different types of pages and are designed to optimize click-throughs and conversions.

• In Generic Algorithms, product recommendations are based on product performance rather than individual consumer behavior or current context.
• In Contextual Algorithms product recommendations are based on the current contexts of individual consumers such as the product category of the current page, but not based on consumer behavior or activity.
• As for Collaborative Algorithms, personalized approaches seek to identify the right products to recommend content based on what similar users have previously engaged in taking product visits, purchases, and items added to the cart into account.

To get a better understanding of Smart Recommender, you might find these articles useful:

• Requirements for Web Smart Recommender
• How to create a Smart Recommender campaign