Delivering personalization to individuals at scale requires a combination of the right data and the right technology. The algorithms used by Amazon Personalize are designed to overcome common problems when creating custom recommendations – such as new users with no data, popularity biases, and evolving intent of users – to deliver high-quality recommendations that respond to specific needs, preferences, and behavior of your users.
Timing is everything. If a customer has spent time browsing products on your site, you need to understand what they’re looking for and respond with the right recommendations before they move on to another site. Amazon Personalize can blend real-time user activity data with existing user profile and product information to identify the right product recommendations for your users at that moment. With Amazon Personalize you can also easily add real-time personalization to your applications, to surface the most relevant video or article to a user.
Amazon Personalize enables companies to provide a cohesive and unique experience for every user across all channels and devices. Personalized recommendations from the model can be easily integrated into websites, mobile apps, or content management and email marketing systems, via a simple API call. Everything from on-site search, product sorting, recommendations and offers and can be tailored to individual users.
With Amazon Personalize, you can generate a custom personalization model in just a few clicks. Amazon Personalize automates and accelerates the complex machine learning tasks required to build, train, tune, and deploy a personalization model – so you can start delivering relevant experiences for your users quickly.
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, Amazon 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. Amazon 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 E-commerce 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. Amazon 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 Amazon 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.
Using machine learning, Amazon Personalize can learn from past user interactions (events) such as clicks, purchases, views etc. as well as information about the user such as age, location etc. and information about the item such as brand, price etc. to generate highly relevant recommendations for each user.
Amazon Personalize includes AutoML capabilities that take care of machine learning for you. Once you have provided your data via Amazon S3 or via real time integrations, Amazon Personalize can automatically load and inspect the data, select the right algorithms, train a model, provide accurate metrics, and generate personalized predictions.
Learn from every user interaction and continually improve your business objectives; Amazon Personalize allows you to send user events in real time and generate recommendations which respond to real time user activity. Customers can also retrain models on the latest, up to date user events, user data and item data which enables Amazon Personalize to continuously calibrate to evolving user preferences.
Amazon Personalize can be easily integrated into websites, mobile apps, or content management and email marketing systems, via a simple inference API call. The service lets you generate user recommendations, similar item recommendations and personalized reranking of items. You simply call the Amazon Personalize APIs and the service will output item recommendations or a reranked item list in a JSON format, which you can use in your application.