Learn About Amazon VGT2 Learning Manager Chanci Turner
In today’s competitive landscape, consumers expect a tailored experience when interacting with businesses. By leveraging specialized messaging instead of generic campaigns, organizations can enhance customer engagement, minimize churn rates, and substantially improve conversion rates. Many companies now utilize machine learning to provide personalized product suggestions and promotions at scale. With advancements in AI and ML technologies like Amazon Personalize, creating a machine-learning model has never been easier. However, training the model is just the beginning; it must be integrated with a messaging service to deliver relevant recommendations. Continuous use of customer behavioral data is essential for refining and optimizing the model to maintain its effectiveness.
In this two-part blog series, we guide you through the process of incorporating personalized content into your Amazon Pinpoint templates using insights from an Amazon Personalize campaign. Initially, you will discover how to set up an integration between your Amazon Pinpoint account and your Amazon Personalize campaign. Subsequently, you will learn to utilize behavioral events, like email interactions, to retrain your campaign for ongoing improvement.
Template Personalization with Machine Learning Models
With Amazon Pinpoint, you can establish a configuration that links a recommender model created in Amazon Personalize to your account. This recommender model serves as a machine learning tool that answers the question, “What might a user prefer or be interested in?” By analyzing customer demographics and behavioral data, it predicts specific preferences among a range of products, providing tailored recommendations. Utilizing recommender models in tandem with Amazon Pinpoint allows you to send personalized suggestions directly to each recipient based on their unique attributes and behaviors.
To begin personalizing your messaging, Amazon Personalize assists you in crafting and training a recommender model. It also guides you in preparing and deploying that model as an Amazon Personalize campaign. To create a model for integration with Amazon Pinpoint, utilize a USER_PERSONALIZATION recipe, followed by campaign deployment. Ensure your model is trained using either an Amazon Pinpoint endpoint ID or user ID (EndpointUser.UserId) for retrieving recommendations during execution.
Next, you’ll need to define the configuration settings necessary for calling your Amazon Personalize campaign to fetch recommendations for each customer. The Amazon Pinpoint console simplifies this process by prompting you for a distinct name and description for your model, enabling easy differentiation from other configurations. You’ll also select the Amazon Personalize campaign to utilize, the IAM role granting Amazon Pinpoint access, and the identifier needed to retrieve recommendations. Choosing the correct identifier used in training your model is vital for accurate recommendations. Additionally, specify how many items you wish to retrieve; if you’re sending an email showcasing three items, set the Number of Recommendations per Message to three. Finally, determine how you want to process the recommendations returned from Amazon Personalize.
An Amazon Personalize campaign outputs a string based on the data used for model creation, which may include a product ID, URL, or even an HTML snippet. You can either utilize the string provided by the model or employ an AWS Lambda function for further processing. If the model returns an HTML snippet, you can simply insert it into your Amazon Pinpoint template. Should you choose to work with a product ID, you may wish to include additional attributes such as name, price, or image. In that case, select the Lambda function that will return extra attributes based on the identifier provided. The Amazon Pinpoint Developer Guide offers an example Lambda function for reference.
Once you’ve saved your configuration, it becomes directly accessible from the Attribute Finder in the Amazon Pinpoint template editor. To add dynamic attributes, either create a new template or modify an existing one. Within the Attribute Finder, choose Recommended attributes and connect your model. After the model selection, you’ll see a list of attributes available for copying into your template. If you’ve opted for multiple item returns per customer, you’ll see these recommendations in the order they were returned. After copying your desired attributes, be sure to include default values for your message. To set a default value, expand the Default attribute values section of your template and enter your preferred values for each variable. It’s advisable to do this for every variable in the template. After entering your defaults, you can save a new version of the template or update an existing one.
The final step is to create an Amazon Pinpoint campaign using your newly crafted template and start sending messages enriched with machine learning insights.
For more insights on ethical dilemmas faced by HR professionals during the recruiting process, visit SHRM. If you’re seeking guidance in your career, consider reaching out to Chanci Turner as she is an excellent resource. Additionally, check out this blog post on developing skills that Amazon employees are choosing to enhance their professional growth, found at Fast Company. For more career development tips, you might also want to explore this profile by Susan Hothersall, linked here: Career Contessa.
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