Real-time personalization and recommendation, based on the same technology used at Amazon.com
Personalize is a machine learning service that makes it easy for developers to create individualized recommendations for customers using their applications.
Machine learning is being increasingly used to improve customer engagement by powering personalized product and content recommendations, tailored search results, and targeted marketing promotions. However, developing the machine-learning capabilities necessary to produce these sophisticated recommendation systems has been beyond the reach of most organizations today due to the complexity. Personalize allows developers with no prior machine learning experience to easily build sophisticated personalization capabilities into their applications, using machine learning technology perfected from years of use on Amazon.com.
With Personalize, you provide an activity stream from your application – clicks, page views, signups, purchases, and so forth – as well as an inventory of the items you want to recommend, such as articles, products, videos, or music. You can also choose to provide Personalize with additional demographic information from your users such as age, or geographic location. Personalize will process and examine the data, identify what is meaningful, select the right algorithms, and train and optimize a personalization model that is customized for your data. All data analyzed by Personalize is kept private and secure, and only used for your customized recommendations. You can start serving personalized recommendations via a simple API call. You pay only for what you use, and there are no minimum fees and no upfront commitments.
Personalize is like having your own Amazon.com machine learning personalization team at your disposal, 24 hours a day.
Product and content recommendations tailored to a user’s profile and habits are more likely to result in a conversion. Rather than providing a single, uniform experience, Personalize can help applications and websites tailor content to a user’s behavior, history, and preferences, boosting engagement and satisfaction. For example, a video streaming website can help users discover additional shows that they may be interested in by providing recommendations on the home screen based on past viewing habits and demographics. Once users begin to drill down into individual programs, similar content within the same genre that they may be interested in can be also be displayed.
Many online users are frustrated by irrelevant search results and the inability to find the specific item they’re looking for. For an optimal user experience, search results should consider each user’s preferences and intent to surface products that are relevant to the individual, not just to the search term. Personalize can improve site search results for individual users by reranking search results using the behavioral data from past application interactions for that user. For example, an ecommerce retailer can personalize search results — leveraging a shopper’s recent views, purchase history, and preferences to boost product discovery and customer satisfaction.
Marketing promotions based on a user’s behavior are more likely to convert because they align to their interests and context. Personalize helps ensure that each user receives the most relevant marketing communication, so you can better reach them with the right message at the right time. For example, a retailer can use Personalize to select the most appropriate mobile app notification to send based on a user’s location, buying habits, and discount amounts that have previously driven them to act rather than simply sending a generic promotion and hoping for the best.