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Mage

Mage

Overview

What is Mage?

Mage is a tool that helps product developers use AI and their data to make predictions. Use cases might be predictions for churn prevention, product recommendations, customer lifetime value and forecasting sales.

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Recent Reviews

Mage Review

9 out of 10
December 06, 2022
Incentivized
Mage helped us with 1. The probability score for uptake of every product is calculated for customers using ML/ Regression models 2. Pick …
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Pricing

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Hobby

$0

Cloud
per user

Pro

$2,000

Cloud
per month per user

Entry-level set up fee?

  • Setup fee optional
For the latest information on pricing, visithttp://mage.ai/pricing

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services
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Product Demos

Lost Ark Sorceress Endgame Gameplay Demo | Mage | Reflux Build

YouTube
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Product Details

What is Mage?

Mage is a tool that helps product developers use AI and their data to make predictions. Other features include churn prevention, product recommendations, customer lifetime value and forecasting sales.

Mage Competitors

Mage Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

Mage is a tool that helps product developers use AI and their data to make predictions. Use cases might be predictions for churn prevention, product recommendations, customer lifetime value and forecasting sales.

Mage starts at $0.

EasyProf, Course Merchant, and dominKnow | ONE are common alternatives for Mage.

The most common users of Mage are from Enterprises (1,001+ employees).
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Comparisons

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Reviews and Ratings

(3)

Reviews

(1-1 of 1)
Companies can't remove reviews or game the system. Here's why
December 06, 2022

Mage Review

Verified User
Account Manager in Sales
Market Research Company, 1001-5000 employees
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Use Cases and Deployment Scope
Mage helped us with 1. The probability score for uptake of every product is calculated for customers using ML/ Regression models 2. Pick Top customers for a product/Top products for a customer, based on the requirement. 3. Identify popular product combinations using 4. Association rules from Market Basket Analysis (or affinity Analysis)\Bundle these products as combos 5. Alternatively, use fast-selling products as carriers to sell high-margin but low-selling products.
Pros and Cons
  • Channel sales decomposition.
  • Investment vs incremental impact.
  • Optimum channel mix.
  • Acquisition Contribution.
  • Business Intelligence Reporting.
  • Data Destinations.
Likelihood to Recommend
Mage is well-suited for probability score for uptake of every product is calculated for customers using ML/ Regression models, choosing customers for a product/ Top products for a customer, based on the requirement and Identifying popular product combinations using association rules from Market Basket Analysis (or affinity Analysis)\Bundle these products as combos.
Most Important Features
  • Real-time monitoring.
  • Alerting
  • Advanced Drill Downs.
Return on Investment
  • Business Understanding.
  • Data Acquisition and Understanding.
  • Data Modeling and Evaluation.
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