Increase ecommerce sales and revenue with Mage

First published on May 9, 2022


4 minute read

John Patrick Hinek



Create accurate predictions, eliminate guesswork, and create magical customer experiences with Mage. 


What Mage can do for your business:

  1. Create accurate predictions

  2. Save time and money

  3. Boost revenue 

How customers use Mage: 

  1. Predictive product recommendations 

  2. Data insights

  3. Creating magical customer experiences 

What Mage can do for your business:

1. Boost revenue

Create and integrate models that boost website traffic and increase revenue.


of what people buy on Amazon is the result of their recommendation system. Integrating machine learning (ML) into your product can create a similar result for your business. 

Personalization is key to success in ecommerce; not using ML to create personalized product recommendations is a missed opportunity for generating powerful data insights that can increase revenue and customer retention. 

2. Create actionable predictions

Data-driven predictions enable ecommerce companies to drive more sales and become a leader in their field. 

A study conducted by


found 26% of companies who use ML have gained a competitive advantage over their market peers. Using ML helps ecommerce companies keep up with current trends and stand out from less tech-savvy competitors. 

Building models on Mage with customer data gives personalized insights on the ways that product managers and data teams can make more informed decisions to boost revenue and growth. 

3. Save time and money

Mage allows anyone on your team who's familiar with data to quickly build and deploy custom models. 

Lack of ML experience and the traditional time commitment required to build ML are both factors which have prevented ecommerce companies, specifically smaller ones, from using the tech. 

Building a model on Mage takes minutes; compared to the weeks it takes to build a conventional model. This quick deployment not only saves building and setup costs, but allows you to start benefiting from your ML models much quicker. 

Historically only large companies have had the budget to integrate machine learning. Mage empowers any size company with full-scale ML capabilities to quickly get the most accurate insights and predictions from their data.  

How customers use Mage: 

1. Predictive product recommendations

Using ML to integrate product recommendations gives the most accurate predictions for creating successful customer outreach and advertising. 

Customers use Mage’s ranking model to integrate product recommendations into their website. Taking data from user profiles, search and purchase history, Mage’s ranking model sorts ecommerce products to find the most relevant products for a particular customer. 

Adding product recommendation technology into your systems ensures that relevant products appear as soon as a customer enters your website; maximizing chances of a sale, and minimizing the occurrence of churn. 

2. Data insights

Mage provides a data insights page for every model, allowing teams to extract greater information about the statistical makeup of their data. 

As discussed, ML predictions are extremely powerful for ecommerce companies to learn more about their customers and implement intelligent product solutions. Knowing as much as you can about the makeup of your data enables these predictions to thrive. 

Instead of relying on third party apps to draw stats from your data, Mage creates an organized and intuitive data insights page for every model. 

Obtaining accurate and easy to read data visualization enables teams across the organization to apply data-driven decisions into their projects. Identifying and advertising top sellers and the best bundling packages are both reliant on having access to good data insights. 

3. Creating magical customer experiences 

ML models give ecommerce companies the best chance to enhance their customer experience. 

Using customer insights, a successful model will inform businesses about their customer behavior–which customers are most likely to churn, and which customers are most likely to buy a certain product. 

Companies are then able to get ahead of churn prevention efforts–increasing their customer base while simultaneously increasing customer satisfaction by offering product suggestions most relevant to an individual customers' needs.