TrustRadius

Mage

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Mage
Score 8.6 out of 10
N/A
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.
$0
per user
Pricing
Mage
Editions & Modules
Hobby
$0
per user
Pro
$2,000
per month per user
Offerings
Pricing Offerings
Mage
Free Trial
Yes
Free/Freemium Version
Yes
Premium Consulting/Integration Services
Yes
Entry-level Setup FeeOptional
Additional DetailsContact vendor for pricing information.
More Pricing Information
Community Pulse
Mage
Considered Both Products
Mage
Verified User
Contributor
Chose Mage
Mage was the easiest in terms of ease of implementation due to its no-code functionality. However, Mage doesn't have a whole ecosystem like AWS and slightly falls behind there.
Top Pros
Top Cons
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Mage
Small Businesses
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Enterprises
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User Ratings
Mage
Likelihood to Recommend
8.5
(2 ratings)
User Testimonials
Mage
Likelihood to Recommend
Mage
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.
Verified User
Anonymous
Read full review
Pros
Mage
  • Ranking algorithms.
  • Cloud-based tool.
  • Increase user engagement.
Verified User
Anonymous
Read full review
Cons
Mage
  • Acquisition Contribution.
  • Business Intelligence Reporting.
  • Data Destinations.
Verified User
Anonymous
Read full review
Alternatives Considered
Mage
Mage was the easiest in terms of ease of implementation due to its no-code functionality. However, Mage doesn't have a whole ecosystem like AWS and slightly falls behind there.
Verified User
Anonymous
Read full review
Return on Investment
Mage
  • Business Understanding.
  • Data Acquisition and Understanding.
  • Data Modeling and Evaluation.
Verified User
Anonymous
Read full review
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