Personalize your customer experiences
As the ability to deliver more sophisticated digital experiences has evolved over time, so has the expectation and demand from customers to receive a more personalized experience from the brands they engage with across retail, media and entertainment, travel and hospitality and more. Consumers today expect real-time, curated experiences across digital channels as they consider, purchase, and use products and services.
Machine learning (ML) can help organizations deliver highly personalized experiences, resulting in improvements in customer engagement, conversion, revenue, and margin and create differentiation in a digital world.
AWS offers machine learning solutions that deliver higher-quality personalized experiences for your customers across digital channels, all tailored to your business needs.
Benefits
Deliver better personalized experiences
Solve common problems like “popularity bias” (merely showing a customer the most popular products or content) and “cold start” (where no user, item, or content history exists), which dilutes the customer experience and ability to discover new items or content in an organization’s catalog.
Increase customer engagement
Increase engagement and conversion by providing dynamic customer experiences and the optimal product or content recommendations using a blend of real-time user activity data and user profile information.
Personalize every touchpoint
Easily integrate personalization into your existing websites, apps, SMS, and email marketing systems to provide a unique customer experience across all channels and devices.
Customer stories
"We’re focused on how we can use data to personalize and enhance the online fan experience for our clients through the Pulselive Platform. With Amazon Personalize, we’re now providing sports fans personalized recommendations enabled by machine learning. We don’t consider ourselves machine learning experts, but found Personalize to be straight forward and the integration was complete in a few days. For one of our clients, a premier European football club with millions of fans globally, we immediately increased video consumption by 20% across their website and mobile app. Their fans are clearly embracing the new recommendations. Leveraging Amazon Personalize, we will be able to further push the limits in building data driven 1-to-1 personalized experiences for sports fans everywhere."
Wyndham Richardson, Managing Director & Co-Founder - Pulselive
"To enable us to be more customer centric, scale our reach, and increase uptake by users, we turned to Amazon Personalize to enable over 600,000 users of our M Coupon mobile app to save on their in store shopping experience. By using Amazon Personalize, we have seen a 5x increase in response to recommended products compared to our prior big data analytics solution resulting in increased revenue per month. In particular, Amazon Personalize has increased the number of products that the customer has never purchased before up to 40%. The new recommendation service powered by AWS is the first of a much broader roll-out of AI technologies across our organization."
Jaehyun Shin, Big Data Team Leader - Lotte Mart
"Zalando's values revolve around customer focus, speed, entrepreneurship, and empowerment. We decided to standardize our machine learning workloads on AWS to improve customer experiences, give our team the tools and processes to be more productive, and push the needle in our business. Using Amazon SageMaker, Zalando can steer campaigns better, generate personalized outfits, and deliver better experiences for our customers. With this AWS-powered solution, our engineers' and data scientists’ productivity has increased by 20%."
Rodrigue Schäfer, Director Digital Foundation - Zalando
"At Zappos, we are measurably improving the ecommerce customer experience using analytics and machine learning solutions that allow us to personalize sizing and search results for individual users while preserving a highly fluid and responsive user experience. With Amazon SageMaker, we can predict customer shoe sizes. AWS is our enterprise standard for ML/AI because AWS services allow engineers to focus on improving performance and results rather than DevOps overhead."
Ameen Kazerouni, Head of Machine Learning Research and Platforms - Zappos
Use cases
Retail
Deliver unique homepage experiences
Personalize your users homepage with product recommendations based on their shopping history.
Help customers discover products faster
Help users quickly find relevant new products, deals, and promotions.
Enhance marketing communication
Personalize push notifications and marketing emails with individualized product recommendations.
Refine product recommendations
Recommend similar items on product detail pages to help users easily find what they are looking for.
Relevant product rankings
Easily re-rank relevant product recommendations to drive tangible business outcomes.
Boost upsell and cross-sell
Combine Amazon Personalize with business logic to create high quality cart upsell and cross-sell recommendations.
Media and Entertainment
Increase content consumption
Deliver highly relevant, individualized content recommendations for videos, music, e-books, and more.
Highly curated content carousels
Create personalized content carousels for every user based on their content consumption history.
Highlight new content offerings
Help users find fresh, new content based on their unique tastes and preferences.
Create personalized ad placements
Personalize pre-roll, mid-roll, and post-roll ad placements within audio and video content.
Improve marketing communication
Personalize push notifications and marketing emails with individualized content recommendations.
Enhance genre based recommendations
Add individualized recommendations to genre based on content carousels and lists.
Ready to get started?
Contact us for more information on machine learning solutions for personalization
Contact the AWS Partner Network, to work with our global technology and consulting partners
Leverage Amazon AI Services to add personalization capabilities to your business application
Do it yourself
With Amazon Personalize, you can implement a customized personalization recommendation system, powered by ML, in just a few clicks without the burden of building, training, and deploying a “do it yourself” ML solution. Organizations that prefer to develop their own machine learning models for recommendation engines can use Amazon SageMaker.
Amazon Personalize enables developers to build applications with the same ML technology used by Amazon.com for real-time personalized recommendations – with no ML expertise required.
Amazon Personalize automates many of the complicated steps required to build, train, and deploy a ML model, making it easy to develop applications for a wide array of personalization use cases, including specific product or content recommendations, individualized search results, and customized marketing communications.
You can use the following AWS Solutions Reference Architectures as a reference.
AWS Solutions Reference Architectures are a collection of architecture diagrams, created by AWS. They provide prescriptive guidance for applications, as well as other instructions for replicating the workload in your AWS account.
Personalize and improve the customer experience by identifying known and unknown guests across all channels. Utilize customer interaction activity across all channels to present offers and campaigns that deliver high Return on Investment (ROI)
Personalize and improve the customer experience by identifying known and unknown travelers across all channels. Utilize customer interaction activity across all channels to execute offers and campaigns that delivery high return on investment (ROI).
Or you can deploy the following AWS Solution Implementations.
AWS Solutions Implementations help you solve common problems and build faster using the AWS platform. All AWS Solutions Implementations are vetted by AWS architects and are designed to be operationally effective, reliable, secure, and cost efficient. Every AWS Solutions Implementation comes with detailed architecture, a deployment guide, and instructions for both automated and manual deployment.
This solution helps you build custom Amazon Personalize experiences for your product portfolio. Amazon Personalize allows you to create custom recommendation models at scale. This solution streamlines and accelerates the development and deployment of your personalization workloads through end-to-end automation and scheduling of updates for resources within the Amazon Personalize service
Resources
Offer your customers real-time personalized recommendations using machine learning
Amazon Personalize can now create up to 50% better recommendations for fast changing catalogs of new products and fresh content
How dely uses Amazon SageMaker to provide personalized recipe recommendations