Generative artificial intelligence is transforming how enterprises do business. Organizations are using AI to improve data-driven decisions, enhance omnichannel experiences, and drive next-generation product development. Enterprises are using generative AI specifically to power their marketing efforts through emails, push notifications, and other outbound communication channels. Gartner predicts that “by 2025, 30% of outbound marketing messages from large organizations will be synthetically generated.” However, generative AI alone isn’t enough to deliver engaging customer communication. Research shows that the most impactful communication is personalized—showing the right message to the right user at the right time. According to McKinsey, “71% of consumers expect companies to deliver personalized interactions.” Customers can use Amazon Personalize and generative AI to curate concise, personalized content for marketing campaigns, increase ad engagement, and enhance conversational chatbots.
Developers can use Amazon Personalize to build applications powered by the same type of machine learning (ML) technology used by Amazon.com for real-time personalized recommendations. With Amazon Personalize, developers can improve user engagement through personalized product and content recommendations with no ML expertise required. Using recipes (algorithms prepared to support specific uses cases) provided by Amazon Personalize, customers can deliver a wide array of personalization, including specific product or content recommendations, personalized ranking, and user segmentation. Additionally, as a fully managed artificial intelligence service, Amazon Personalize accelerates customers’ digital transformations with ML, making it easier to integrate personalized recommendations into existing websites, applications, email marketing systems, and so on.
In this post, we illustrate how you can elevate your marketing campaigns using Amazon Personalize and generative AI with Amazon Bedrock. Together, Amazon Personalize and generative AI help you tailor your marketing to individual consumer preferences.
How exactly do Amazon Personalize and Amazon Bedrock work together to achieve this? Imagine as a marketer that you want to send tailored emails to users recommending movies they would enjoy based on their interactions across your platform. Or perhaps you want to send targeted emails to a segment of users promoting a new shoe they might be interested in. The following use cases use generative AI to enhance two common marketing emails.
Use Case 1: Use generative AI to deliver targeted one-to-one personalized emails
With Amazon Personalize and Amazon Bedrock, you can generate personalized recommendations and create outbound messages with a personal touch tailored to each of your users.
The following diagram illustrates the architecture and workflow for delivering targeted personalized emails powered by generative AI.
First, import your dataset of users’ interactions into Amazon Personalize for training. Amazon Personalize automatically trains a model using the Top Picks for You recipe. As an output, Amazon Personalize provides recommendations that align with the users’ preferences.
You can use the following code to identify recommended items for users:
For more information, see the Amazon Personalize API reference.
The provided output of recommendations is ingested by Amazon Bedrock using a prompt, which includes your user preferences, demographics, and Amazon Personalize recommended items.
For example, a marketer who wants to create a personalized email that is charming and fun for a user might use the following prompt:
Create a personalized email which is charming and fun so that the user is engaged. The user has recently watched family-friendly films. These are the recommended items – The Little Mermaid, Encanto, Spider-Man: Into the Spider-Verse.
By invoking one of the foundation models (FM) provided in Amazon Bedrock, such as Claude 2, with the prompt and sample code that follows, you can create a personalized email for a user:
For more information, see the Amazon Bedrock API reference.
Amazon Bedrock returns a personalized email for the user:
Subject: Fall in love with this recommended selection for movie night!
Dear <user name>,
Desiring the cozy feel of fall? No problem! Check our top three recommendations for movies that will have you cozy on the couch with your loved ones:
1. The Little Mermaid: This classic Disney movie is all about a mermaid princess named Ariel, who dreams of the human world. Because of her fascination, she makes a deal with the sea witch Ursula and learns a major lesson.
2. Encanto: This Disney movie is about the Madrigals, a Colombian family who lives in a magical house. Each member of the family has a unique gift, except for young Maribel who must help save her family.
3. Spider-Man: Into the Spider-Verse: This animated superhero movie is a must-see action movie. Spider-man, a Brooklyn teen named Miles Morales, teams up with other spider-powered people to save the multiverse.
With lovable characters, catchy tunes, and moving stories, you really can’t go wrong with any of these three. Grab the popcorn because you’re in for a treat!
Use case 2: Use generative AI to elevate one-to-many marketing campaigns
When it comes to one-to-many email marketing, generic content can result in low engagement (that is, low open rates and unsubscribes). One way companies circumvent this outcome is to manually craft variations of outbound messages with compelling subjects. This can lead to inefficient use of time. By integrating Amazon Personalize and Amazon Bedrock into your workflow, you can quickly identify the interested segment of users and create variations of email content with greater relevance and engagement.
The following diagram illustrates the architecture and workflow for elevating marketing campaigns powered by generative AI.
To compose one-to-many emails, first import your dataset of users’ interactions into Amazon Personalize for training. Amazon Personalize trains the model using the user segmentation recipe. With the user segmentation recipe, Amazon Personalize automatically identifies the individual users that demonstrate a propensity for the chosen items as the target audience.
To identify the target audience and retrieve metadata for an item you can use the following sample code:
For more information, see the Amazon Personalize API reference.
Amazon Personalize delivers a list of recommended users to target for each item to batch_output_path. You can then invoke the user segment into Amazon Bedrock using one of the FMs along with your prompt.
For this use case, you might want to market a newly released sneaker through email. An example prompt might include the following:
For the user segment “sneaker heads”, create a catchy email that promotes the latest sneaker “Ultra Fame II”. Provide users with discount code FAME10 to save 10%.
Similar to the first use case, you’ll use the following code in Amazon Bedrock:
For more information, see the Amazon Bedrock API reference.
Amazon Bedrock returns a personalized email based on the items chosen for each user as shown:
Subject: <<name>>, your ticket to the Hall of Fame awaits
Hey <<name>>,
The wait is over. Check out the new Ultra Fame II! It’s the most innovative and comfortable Ultra Fame shoe yet. Its new design will have you turning heads with every step. Plus, you’ll get a mix of comfort, support, and style that’s just enough to get you into the Hall of Fame.
Don’t wait until it’s too late. Use the code FAME10 to save 10% on your next pair.
To test and determine the email that leads to the highest engagement, you can use Amazon Bedrock to generate a variation of catchy subject lines and content in a fraction of the time it would take to manually produce test content.
Conclusion
By integrating Amazon Personalize and Amazon Bedrock, you are enabled to deliver personalized promotional content to the right audience.
Generative AI powered by FMs is changing how businesses build hyper-personalized experiences for consumers. AWS AI services, such as Amazon Personalize and Amazon Bedrock, can help recommend and deliver products, content, and compelling marketing messages personalized to your users. For more information on working with generative AI on AWS, see to Announcing New Tools for Building with Generative AI on AWS.
About the Authors
Ba’Carri Johnson is a Sr. Technical Product Manager working with AWS AI/ML on the Amazon Personalize team. With a background in computer science and strategy, she is passionate about product innovation. In her spare time, she enjoys traveling and exploring the great outdoors.
Ragini Prasad is a Software Development Manager with the Amazon Personalize team focused on building AI-powered recommender systems at scale. In her spare time, she enjoys art and travel.
Jingwen Hu is a Sr. Technical Product Manager working with AWS AI/ML on the Amazon Personalize team. In her spare time, she enjoys traveling and exploring local food.
Anna Grüebler is a Specialist Solutions Architect at AWS focusing on artificial intelligence. She has more than 10 years of experience helping customers develop and deploy machine learning applications. Her passion is taking new technologies and putting them in the hands of everyone and solving difficult problems by taking advantage of using AI in the cloud.
Tim Wu Kunpeng is a Sr. AI Specialist Solutions Architect with extensive experience in end-to-end personalization solutions. He is a recognized industry expert in e-commerce and media and entertainment, with expertise in generative AI, data engineering, deep learning, recommendation systems, responsible AI, and public speaking.
