Amazon Personalize: Create a Customer Experience Worthy of Brand Loyalty

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Get the contact center analytics you need to offer a hyper-personalized experience

Key Takeaways:

  • Customers have many options. Why should they choose you?
  • Amazon Personalize gives them the experience they want and keeps them coming back.
  • AI and ML produce contact center analytics for continuous improvement.
  • Get ahead of the competition with precisely-tailored customer journeys.

With a seemingly endless amount of online purchasing options, the imperative for today’s sellers is to engage customers and build loyalty. One of the most effective tools in your marketing strategy goes right to the heart of customer satisfaction: Amazon Personalize. It takes the customer experience to new heights by using artificial intelligence (AI) and a machine learning (ML)-powered engine to respond to customer preferences.

Amazon Personalize, when used with Amazon Connect, enhances your call center, empowering your agents and allowing everything from searches to promotions. Contact center analytics provide the means for further fine-tuning to create the ultimate customer journey.

Amazon Personalize core features

Amazon Personalize uses customer data to make predictions that elevate the experience with real-time personalization and recommendations based on user behavior. Automated machine learning creates, trains, and deploys.

You can tune recommendations with personalized search, business rules and filters, and promotions. It’s all made possible by AI and ML.

The role of AI in modern contact centers

AI in contact centers provides more efficient and personalized customer service, leading to true customer satisfaction and brand loyalty. For example, Amazon Connect Contact Lens offers real-time contact center analytics along with management capabilities. Continuous improvement is enabled by monitoring and measuring contact quality and agent performance.

The contact center analytics provided by AI offer a complete view of customer conversations over voice and chat.

  • Analyze customer sentiment with ML-powered natural language processing.
  • Review generative AI-powered summaries of customer conversations that capture vital information from customer conversations so supervisors can know the context and follow up or take other actions. 
  • Access real-time analytics. Data streams provide issue detection with sentence-by-sentence transcripts, sentiment analysis, and categories for conversations with customers.
  • Create rules to flag customer service issues in real time with automatic alerts for supervisors whenever agents need assistance.
  • Intelligent routing means incoming calls are routed to the agent best suited to address customer queries effectively and efficiently. AI is leveraged to analyze customer profiles and predict their requirements. 

Build brand loyalty with Amazon Personalize

Customers expect their experience to be personalized, and this can significantly enhance customer satisfaction by using AI analytics. Tailoring products and services to individual needs and preferences provides seamless interaction throughout the journey. 

It’s simply psychology. A personalized approach engenders loyalty; a personalized experience offers a feeling of connection—that your company cares about them, and they will care about your company in return. And personalization pays. Fast-growing companies drive 40% more of their revenue through personalization than their competitors.

Drive repeat business

With Amazon Personalize, you can offer an experience that customers want, and stand out from your competitors, and keep them coming back for more.

  • You can adapt recommendations in real time to provide a relevant customer experience, engage new users, or promote new items. 
  • Use your AI-analyzed data to understand preferences to target upsell and cross-sell product recommendations based on customers’ past purchases.
  • Promote the right content and items. Amazon Personalize finds the most relevant items and content for each user.

Implementation of Amazon Personalize in Contact Centers

Now that you know everything Amazon Personalize can do, it’s time to integrate it with your contact center.

Prerequisites

  • Prepare your data. Amazon Personalize generates recommendations based on the interactions data you import into an interactions data set. The model created by Personalize makes recommendations based on past interactions, but you can increase the quality of these by adding data about associations between users and items.
    • You’ll need a minimum of 1,000 item interaction records from users and a minimum of 25 unique user IDs with at least two item interactions each. 
    • For quality recommendations, you need a minimum of 50,000 item interactions from at least 1,000 users with two or more interactions. 
    • Import by bulk or individually.

Once you import data into an Amazon Personalize dataset, you can upload it to an S3 bucket.  

Now, deploy the solution by creating a new folder on your desktop, and cloning the repository, and then deploying the stack to your Amazon Web Services environment.

Build a solution version

Solution refers to combining an Amazon Personalize recipe, customized parameters, and one or more trained models (aka a solution version). When you deploy the CDK, a solution with a User-Personalization recipe is automatically created. Create a solution version for the implementation.

Create a campaign

A campaign deploys a solution version with provisioned transaction capacity to generate real-time recommendations. Create a campaign for the implementation using the CDK and Amazon Software Development Kit.

Construct an event tracker

Before you can record events, you must create an event tracker. An event tracker directs new event data to the Interactions dataset in your dataset group. 

Amazon Personalize can make recommendations using real-time event data only, historical event data only, or both. When you record real-time events, you build out your interactions data. This allows Personalize to learn from your user’s most recent activity. This means your data is fresh, which improves the relevance of Amazon Personalize recommendations. 

Implement recommendations

The recommendations presented to the user will consist of product IDs that align most closely with their personal preferences, based on their historical interactions. You can use the getRecommendations API to retrieve personalized recommendations for a user by sending the associated userID, the number of results you need for the user, and the campaign ARN. You can find the campaign ARN in the menu of the Amazon Personalize console.

The items returned by the API call are those that Amazon Personalize recommends to the user based on their historical interactions.

Ingest real-time interactions

For real-time recommendations, the user interactions with the items must be ingested into Amazon Personalize in real time. These interactions are ingested into the recommendation system through the Amazon Personalize Event Tracker. The type of interaction, also called EventType, is given by the column of the same name in the interaction data dataset (EVENT_TYPE). In this example, the events can be of the type “watch” or “click,” but you can add types of events according to the needs of your application.

The exposed API that generates the events of the users with the items receives the “interactions” parameter, which corresponds to the number of events (interactions) of a user (UserId) with a single element (itemId) right now. The trackingId parameter can be found in the Amazon Personalize console and also in the response to the creation of Event Tracker request.

Validate real-time recommendations

When the interaction dataset has been updated, the recommendations will be automatically updated to consider the new interactions. To validate the recommendations updated in real-time, you can call the get Recommendations API again for the same user id, and the result should be different. 

Clean up the implementation

Avoid unnecessary charges by cleaning up the implementation by using Cleaning up resources.

Note: To choose a new recipe for your use case, refer to Real-time personalization

Measure the impact of personalization

It’s important to develop and use key metrics and key performance indicators (KPIs) to know how personalization has impacted your business and customer loyalty. 

Measuring impact and utilizing customer feedback and behavioral data means you can continuously improve and refine your personalization ML algorithms. Also, consider leveraging A/B testing to identify the best-performing algorithms to further tailor the customer experience for maximum impact.

Amazon Personalize transforms your contact center

Faster, better, and more personalized. It’s what customers want, and Amazon Personalize delivers with the help of AI and ML. You can start with what you have now and build on that as you discover more and more about your customers to continuously improve the experience and build loyalty. 

Collect the metrics you need to coach and train agents for maximum positive impact as you’re training your algorithms. It’s about personalizing an impersonal experience—buying online. With Personalize, your customers feel connected, seen, and heard. It’s all about them, so make it so.

Going forward, AI and ML will continue to power the “intelligent experience” at scale to deliver hyper-personalized interactions, anticipate customer needs, identify potential issues, and improve workforce management, all while reducing operational costs. 

Personalized help with all things AWS

Ready to implement Amazon Personalize? Then it’s time to connect with CloudHesive. We’re a cloud solutions consulting and managed service company with expertise in all things Amazon Web Services. We have eight AWS Competencies, and more than 50 AWS Certifications, plus membership in nine Partner Programs along with the knowledge and experience to help your business realize all the benefits AWS cloud offers, including a personalized customer experience. 

We work collaboratively with you to reduce operating costs and increase productivity, with a focus on security, reliability, availability, and scalability. With more than 30 years of experience, we leverage cloud-based technology to its full potential.

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