Personalized Recommendations

Hit your audience right in the feels

Benefit from the power of AI recommendations to offer your customers what they need. Let AI craft tips based on a shopper's past behavior, such as their purchase history, search queries, and browsing behavior.

Aim for stars

  • Increased and strong
    customer retention

  • Intuitive and easy
    product discovery

  • Reduced cart abandonment

  • Highly personalized
    customer journey

  • Improved cross-selling
    and upselling

  • Higher customer


Increase customer satisfaction and drive sales, offering your shoppers a personalized experience with relevant product recommendations and engaging content

  • Personalized Product Recommendations

    Use data analysis techniques to determine individual customer behavior and interests and provide tailored suggestions of products.

  • Personalized Content Recommendations

    Analyze user purchase history, browsing behavior, search queries and more to provide more
    relevant content that keeps customers engaged.

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How to Start

  • 01/

    Data Collection

    We collect data on the customer’s behavior and preferences, including purchase history, search queries,
    browsing behavior, demographics, and psychographics

  • 02/

    Data Analysis

    We analyze the data collected with the help of machine learning to identify patterns
    and trends that can be used to create personalized recommendations.

  • 03/


    We implement the personalized recommendations
    and make them displayed on various pages such as the home page, product pages, cart page, and checkout page.

What We Do

Committed to a set of core principles,
our dream team operates with unwavering
integrity, transparency, and a genuine passion for our craft.

Want to make every customer experience relevant?

Receive suggestions from our experts about conversions with no obligations from your side


Magecom Performed Tasks very Diligently and Efficiently”

Magecom established a smooth workflow through clear communication and timely deliveries. They were professional, knowledgeable, and easy to work with. Their productivity, efficiency, and cost-effectiveness were hallmarks of their work.

Tamara Bakhia

E- Commerce & Digital Marketing Manager


Any questions left?

Why use personalized recommendations in eCommerce?

Personalized recommendations are an effective way to increase conversions in eCommerce because they provide a tailored shopping experience that meets the individual needs and preferences of each customer.

By analyzing customer data such as browsing and purchase history, eCommerce businesses can use algorithms and machine learning to recommend products that are more likely to interest each customer.

How to set up eCommerce product recommendations?

To set up eCommerce product recommendations, businesses can follow these common steps:

  • ✅ Gather and analyze customer data;
  • ✅ Choose a product recommendation engine or software;
  • ✅ Configure the recommendation engine;
  • ✅ Choose the placement of product recommendations on the store;
  • ✅ Determine the timing and the number of products to display.

How do product recommendations work?

Product recommendations use customer data and algorithms to suggest products that are likely to interest and appeal to individual customers. These recommendations are based on a variety of factors, such as the customer’s browsing and purchase history, demographic information, and product popularity.

The recommendation engine analyzes this data to identify patterns and preferences and uses machine learning algorithms to make predictions about which products the customer is most likely to purchase. These recommendations can be presented to the customer in the online store, email marketing campaigns, or other channels.

What are the types of personalized product recommendations?

There are several types of personalized product recommendations used in eCommerce:

  • 🟠 Personalized product recommendations based on browsing and purchase history;
  • 🟠 Upsell and cross-sell recommendations;
  • 🟠 New product recommendations;
  • 🟠 Social recommendations;
  • 🟠 Seasonal recommendations.