Amazon Personalize (Pricing, Features, Advantages, and Disadvantages)

Last updated on by Editorial Staff
Amazon Personalize

The use of machine learning is becoming more and more common, but many people are still unsure about which software to go with. 

You may have many options in the machine learning software, but you should have some guidance to select a better choice for your company.

We will provide you with complete guidance on the Amazon Personalize ML software. This blog post will help you understand what it is, how it works, and its features. You can then compare its pros and cons to other alternatives and decide which is better for you.

What is Amazon Personalize? 

This software uses the power of machine learning to make it easy to create personalized recommendations for customers of all sizes, including Amazon.com shoppers and Amazon Web Services (AWS) customers.

In addition, this machine-learning service makes it easy for developers to create individualized recommendations. 

It makes it easy to start with machine learning by providing a data processing pipeline, an algorithm selection console, and pre-built models optimized for Amazon products and services.

Webpage of Amazon Personalize

How does it work?

  • Bring all your data together to create good customer experiences throughout the journey.
  • It helps to increase revenue and brand loyalty.
  • Quick setup of a personalization engine without the knowledge of ML is also possible.
  • You Can do Customization in a few days.
  • It enables you to change your recommendations based on what customers want.

Reason to choose Amazon Personalize

  • It accelerates digital transformation with ML.
  • It integrates easily with websites, email marketing, and other applications.
  • Recommendations for things like retail, media, and entertainment faster and easier by automating the process of creating and maintaining them.
  • You can use machine learning to run more effective prospecting campaigns. That is done by dividing users into groups based on their preferences for products, categories, and brands.
  • This uses information from product descriptions, reviews, or other unstructured text to generate more applicable recommendations.
  • Using business goals, it customizes the product according to the customer’s need and promotion purpose.

Amazon Personalize Pricing

A free trial is available. For the first two months, it provides the below facility using recommendations.

  • Data processing and data storage: Up to 20 GB P/M AWS region
  • Training: Up to 100 training hours P/M per eligible region
  • Recommendations: Up to 50 hours of real-time recommendations

Other pricing options are

  • Use case-optimized recommenders
  • Use segmentation
  • Custom recommendation solutions
Amazon Personalize Pricing

Features

Recommenders for retail, media, and entertainment

Tailored recommenders help to improve the performance of media, entertainment, and retail by making it faster and easier.

User segmentation

With Amazon Personalize, you can automatically divide your customers into groups based on their interests. This will help you target them more effectively through your marketing channels. 

They have two recipes to help with this:

  • AWS-item-affinity
  • AWS-item-attribute.

Automated machine learning

The software can automatically load and inspect the data, choose appropriate algorithms, train a model, and provide accurate metrics. This will generate personalized recommendations for your users.

Real-time recommendations

You can recommend your users and personalize their experience with a single API call. This will help improve user engagement, conversions, and marketing campaign performance.

New user new item recommendation

Able to take care of new user recommendations. And finds the new relevant item.

Batch recommendation

For many users and items, a batch recommendation is possible within a context.

Similar item recommendation

People can see similar items to what they are looking at.

Integrates with existing tools

 It can be easily integrated into your website, mobile app, content management, and email marketing systems. You can do this by making a simple inference API call.

This is very helpful in creating user recommendations, similar item recommendations, and re-ranking the items.

Customers

  • Warner Bros Discovery
  • Fox
  • BUNDESLIGA
  • FANFIGHT
  • Pulselive
  • VIEWLIFT 

Alternatives

  • TensorFlow
  • PyTorch
  • Keras
  • Scikit Learn
  • Machine learning in Python
  • V7
  • Kubeflow
  • Microsoft machine learning server

Advantages

  • It is unique and innovative, useful for small companies
  • A credit card can be attached and can save address and other details
  • Create a quality environment for buyers and accurate data
  • High-quality and specific recommendations for the engineers
  • Webinars are useful for easy conversation
  • Clients can write recommendations that help to make modifications

Disadvantages

  • Transactions are high priced
  • Users are disappointed with slow programming
  • For non-technical, it isn’t very easy to navigate
  • Some users are not happy with its accuracy

FAQs

How does a developer get started with Amazon Personalize?

Whether using the JavaScript API or one of the Server-Side SDKs, getting started with Amazon Personalize is quick and easy. The setup wizard walks you through everything and helps you start personalizing your applications for your users.

After you’ve created an account and accessed the developer console, you’re ready to start sending activity stream data to Amazon Personalize in real-time. Then, you can be up and running with personalized recommendations for your users with just a few clicks.

What data should be provided by the developer?

-Event data
-Catalog data
-User data

Conclusion

We hope this blog post explains Amazon Personalizing, its working strategy, the reason to recommend it, its favorable features, and its drawbacks which make you think and compare with competitors.

Even after all the advantages and disadvantages, Amazon personalize strives hard to maintain its dignity in online marketplaces! 

Reference

Amazon Personalize Documentation