Amazon Personalize vs. Azure Personalizer

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon Personalize
Score 9.6 out of 10
N/A
Amazon Personalize uses machine learning algorithms to create recommendations that respond to the specific needs, preferences, and changing behavior of users in real-time, to drive increased customer engagement.N/A
Azure Personalizer
Score 7.3 out of 10
N/A
Personalizer, available on Microsoft's Azure platform of cloud services and applications, is a personalization engine used to boost conversion and engagement, and add real-time relevance to product recommendations, with reinforcement learning–based capabilities available only through Azure. Users can select content, optimize layouts, and personalize offers with two API calls. Part of Azure Cognitive Services, Personalizer can be used as a standalone personalization solution or to complement…N/A
Pricing
Amazon PersonalizeAzure Personalizer
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Amazon PersonalizeAzure Personalizer
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Amazon PersonalizeAzure Personalizer
Considered Both Products
Amazon Personalize

No answer on this topic

Azure Personalizer
Chose Azure Personalizer
Azure Personaliser gives features at Reasonable rate. Also Owing a small E-commerce store I would prefer Azure Personalizer based on its features .
Best Alternatives
Amazon PersonalizeAzure Personalizer
Small Businesses
SAP Emarsys
SAP Emarsys
Score 6.5 out of 10
Bloomreach - The Agentic Platform for Personalization
Bloomreach - The Agentic Platform for Personalization
Score 8.9 out of 10
Medium-sized Companies
Monetate
Monetate
Score 6.1 out of 10
Dynamic Yield
Dynamic Yield
Score 9.0 out of 10
Enterprises
Monetate
Monetate
Score 6.1 out of 10
Dynamic Yield
Dynamic Yield
Score 9.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon PersonalizeAzure Personalizer
Likelihood to Recommend
9.7
(5 ratings)
7.0
(1 ratings)
User Testimonials
Amazon PersonalizeAzure Personalizer
Likelihood to Recommend
Amazon AWS
Well suited for vast business needs, Less suitable when planning for small limited business requirements. However, if the business is vast & the business requirements become vast, its easy to comprehend with Amazon Personalize, as most of the functions would be pre-built & as users, we can customize to our needs to meet business demands.
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Microsoft
Azure Personalizer is better for freshers who wishes to reach out to their potential customers who like their products.But for a established ecommerce ownerthis one is not recomended to use it since they only provides product personalisations for 50 or fewer products efficiently.
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Pros
Amazon AWS
  • Ingest virtually unlimited quantities of transactional data.
  • Provide world-class training capabilities.
  • Provide a free-tier to start your project.
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Microsoft
  • Customer recommendation
  • High engagement Rate
  • Increase sales cycle
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Cons
Amazon AWS
  • Repetitive Recommendations.
  • Needs to improve the search speed.
  • Costly to purchase.
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Microsoft
  • Less features compared to other tool in market
  • Learning loop time consumption is little high
  • Number of items for personalisation is less ie 50 or fewer
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Alternatives Considered
Amazon AWS
Easy to integrate with existing applications, Easy to personalize for new users, Batch deploy, good recommendations and good suggestions based on current requirements, business goals get easy prioritized based on user needs and recommendations, it can work with existing tools and easily adapt the details and requirements from the existing tools.
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Microsoft
Azure Personaliser gives features at Reasonable rate. Also Owing a small E-commerce store I would prefer Azure Personalizer based on its features .
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Return on Investment
Amazon AWS
  • We managed to analyze huge amounts of data from transactional records.
  • We produced 5 different user/profile segments for our marketing team.
  • We learned how to power marketing predictions using machine learning.
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Microsoft
  • Revenue generated increased by 2 times
  • Product sale increased
  • landing page clicks increased
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ScreenShots