How to Implement Azure Personalizer for Enhanced Customer Experience

Azure personalizer helps you build smart applications that recommend a better advertisement position and the most relevant products to show to customers based on contextual information. After showing the most relevant content to the users, the personalizer empowers the application to monitor user’s interaction with the intent to deliver key insights for improvement.

Why invest in Azure Personalizer?

Personalized content is the future of marketing. This is why Netflix opened the best algorithm competition with a one million prize! If you can determine the user’s experience, you can recommend more relevant content that can in return help you get closer to significant revenue.

To deliver the most relevant content to your users, you need to use machine learning models that can continuously introduce improvements. Azure Personalizer does exactly that; it helps you create a highly personalized user experience where the application continuously learns and adapts based on the users’ interactions with the content. As a result, personalizing enables you to build long-term relationships with your current customers, and reach potential customers for increased revenue.

Azure Personalizer use cases

Personalizer can be used in any application where ranking is essential to understand user experience. Some key use-cases of personalizer are:

- UI improvements

- Content filtering and highlighting

- Default suggestions

- Default menus

- Improve bot traits

- Decisions for contextual scenarios

- Notifications for content

• Bot intent clarification

Getting started with Azure Personalizer

Before we get started, let’s first understand how exactly Azure Personalizer chooses the best content to show. The following image shows the process of Azure Personalizer.

The service uses Reinforcement Learning to choose the most relevant content based on contextual information and collective behaviour to create ratings across all application users. Some terms to know in Personalizer:

The Action represents content items that could be movies, news, and products.

The Rank call selects the Action and its features to choose the most relevant items.

Actions with features include items with item-specific features.

Context features include features related to users, the context, and the app environment.

The Rank call shows the ID of the relevant content in the Reward Action ID field.

All action items are chosen based on advanced machine learning models that improve over time to increase the number of rewards.

Now, let’s get to the implementation of Azure Personalizer

Step 1: Understand the requirements

Make sure that you use personalizer when your content:

- Contains limited action items for selection from a personalization event. If your action items are more than fifty, it’s best to use a recommendation engine to bring the list down for each call made on the personalizer service.

- Contains actions with their features and sells the context features. The content should contain all the information you want to rank.

- Has the minimum 1k/day events related to the content. Any more events may render the personalizer ineffective. If the personalizer receives less than minimum traffic, the service takes time to determine the most relevant content.

The personalizer service does not manage user profile information, log individual user history or preferences, or require labelled or cleaned content.

Step 2: Plan and design for content, context, and action. In addition, determine the reward algorithm.

Step 3: Create Personalizer Resource

Each Personalizer Resource is a single Learning Loop. This loop will receive calls for both Reward and Rank calls for the specific user experience and the content.

Step 4: Add your Personalizer to your website or application.

- Start by adding a Rank call to your personalizer in the application or website. This action will determine the best or most relevant content before it is displayed to your user.

- Next, display the most relevant single item, which will be returned as a ‘reward action ID’ to the user.

- Determine the reward score by applying the Business Logic to the collected data regarding user behaviour. To do that:

a) Create a Reward Call to send for a score 0–1. It can show after the content is displayed or scheduled for later in an offline environment or system.

b) Evaluate the loop with an offline evaluation, which will not affect your user experience or demand a code change.

And that’s it! You are done implementing your personalizer.

Conclusion

Azure Personalizer helps you create a highly optimized user experience for an enriched product marketing strategy that delivers long-term results. With Azure Personalizer, you can help your customers interact with the most relevant searches that build reliability for your business and customer satisfaction. And it only takes a few simple steps to implement.

Sriram

I am a Certified Azure Solutions Architect Expert with 17+ years of IT experience working with all types of Infrastructure, Applications, Network, Security etc.

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