Azure Personalizer – A Guide to Delivering Precise Customer Experience

Azure Personalizer is a service that uses machine learning to deliver personalized experiences to your customers. It's built on top of Azure Machine Learning.

With Azure Personalizer, you can create a more engaging and relevant experience for your customers by learning their behavior and preferences. This is done through a process called contextual decision-making.

Azure Personalizer can be used in a variety of scenarios, including recommending products, content, or services to customers based on their behavior and preferences.

You can get up and running quickly with Azure Personalizer by adding just two lines of code. This makes it easy to embed and start using the service.

AI Personalizer works with your data in any form, so you can start with no data or tap into an existing dataset to jump-start reinforcement learning.

In apprentice mode, the service learns alongside your existing solution without being exposed to users until it meets your performance threshold. This means you can fine-tune the service without affecting your users.

You can pay only for what you use with no upfront costs. With AI Personalizer, you pay as you go based on the number of transactions.

Here are the benefits of using Azure Personalizer:

Azure Personalizer is a cloud-based API service that helps developers create rich, personalized experiences for each user of their app. It uses Microsoft's reinforcement learning technology to learn from user-app interactions and deliver tailored responses.

The API provides a user-friendly interface and enables developers to prioritize content and tailor experiences to improve user engagement. Personalizer comes under the Decision Suite, a new addition to Azure Cognitive Services.

Personalizer's learning cycle runs at digital speed and learns from a simple reward score that optimizes towards your business's goals. It's designed to deliver smarter experiences for every user that improves over time.

To use Personalizer in a web application, you need to add a learning loop. This involves determining which experience to personalize, what actions and features you have, what context features to use, and what reward you'll set.

Here's a step-by-step guide to adding a learning loop to a web application:

Once you've personalized the experience, you need to call Activate for the event, wait for user selection of ranked content, and then call the Reward API to specify how well the output of the Rank API did.

Personalizer decides the best action based on collective behavior and reward scores across all users. It uses data sent to the API, along with the Rank and Reward calls, to train the model and attain higher accuracy.

Here's a simplified overview of how Personalizer works:

To get the most out of Azure Personalizer, you need to meet certain expectations. You can apply Personalizer in situations where you have a business or usability goal for your application.

Personalizer is best suited for applications with fewer than 50 actions to rank per call. This means you can't use it for complex decision-making processes with many options.

To use Personalizer effectively, you need a place in your application where making a contextual decision of what to show to users will improve your business goal. This could be a product recommendation, a personalized message, or a tailored experience.

You should also have a way to measure the success of your personalized content. This means you need a scored result, such as a reward score, to determine how well the ranked choice worked for your application.

Another important consideration is the context in which you're using Personalizer. You need sufficient context features, at least 5, to help make the right choice. This could include information like user location, time of day, or device type.

Here are the key expectations for using Azure Personalizer:

By meeting these expectations, you can unlock the full potential of Azure Personalizer and create a more personalized and engaging experience for your users.