Principal金融集团通过采用云优先策略，借助Amazon Lex、Genesys Cloud和QuickSight构建集成式语音虚拟助手（VA）报告和分析解决方案，显著提升了客户呼叫体验。该方案利用自然语言理解客户意图，提供自助服务，并基于业务规则准确路由呼叫，同时为客服代表提供上下文数据。通过QuickSight仪表板，Principal能够监控VA的健康状况和性能，识别优化机会，从而提高意图识别率，改善客户体验。该方案不仅优化了成本，还确保了数据安全和隐私，实现了更高效的客户服务。

🤖 Principal金融集团通过Amazon Lex构建语音虚拟助手(VA)，理解客户意图，实现自助服务或根据意图和业务规则将客户路由至Genesys Cloud联络中心。

📊 该解决方案利用QuickSight仪表板，整合Genesys Cloud数据、业务特定数据和API指标，监控VA的性能，包括客户互动和Amazon Lex机器人的表现，从而发现改进机会。

☁️ 解决方案架构涉及用户与Genesys Cloud互动，Genesys Cloud调用AWS Lambda路由函数，该函数从DynamoDB获取路由数据，并请求Amazon Lex V2机器人回答用户意图，最终通过QuickSight连接Athena查询S3中的数据。

🔑 该方案注重成本优化，采用Amazon S3 Bucket Keys减少对AWS KMS的请求；同时，通过AWS KMS加密静态数据，并使用SSL/TLS加密传输中的数据，保障数据安全。

This post was cowritten by Mulay Ahmed, Assistant Director of Engineering, and Ruby Donald, Assistant Director of Engineering at Principal Financial Group. The content and opinions in this post are those of the third-party author and AWS is not responsible for the content or accuracy of this post.

Principal Financial Group® is an integrated global financial services company with specialized solutions helping people, businesses, and institutions reach their long-term financial goals and access greater financial security.

With US contact centers that handle millions of customer calls annually, Principal® wanted to further modernize their customer call experience. With a robust AWS Cloud infrastructure already in place, they selected a cloud-first approach to create a more personalized and seamless experience for their customers that would:

Understand customer intents through natural language (vs. touch tone experiences) Assist customers with self-service offerings where possible Accurately route customer calls based on business rules Assist engagement center agents with contextual data

Initially, Principal developed a voice Virtual Assistant (VA) using an Amazon Lex bot to recognize customer intents. The VA can perform self-service transactions or route customers to specific call center queues in the Genesys Cloud contact center platform, based on customer intents and business rules.

As customers interact with the VA, it’s essential to continuously monitor its health and performance. This allows Principal to identify opportunities for fine-tuning, which can enhance the VA’s ability to understand customer intents. Consequently, this will reduce fallback intent rates, improve functional intent fulfillment rates, and lead to better customer experiences.

In this post, we explore how Principal used this opportunity to build an integrated voice VA reporting and analytics solution using an Amazon QuickSight dashboard.

Amazon Lex is a service for building conversational interfaces using voice and text. It provides high-quality speech recognition and language understanding capabilities, enabling the addition of sophisticated, natural language chatbots to new and existing applications.

Genesys Cloud, an omni-channel orchestration and customer relationship platform, provides a contact center platform in a public cloud model that enables quick and simple integration of AWS Contact Center Intelligence (AWS CCI). As part of AWS CCI, Genesys Cloud integrates with Amazon Lex, which enables self-service, intelligent routing, and data collection capabilities.

QuickSight is a unified business intelligence (BI) service that makes it straightforward within an organization to build visualizations, perform ad hoc analysis, and quickly get business insights from their data.

Solution overview

Principal required a reporting and analytics solution that would monitor VA performance based on customer interactions at scale, enabling Principal to improve the Amazon Lex bot performance.

Reporting requirements included customer and VA interaction and Amazon Lex bot performance (target metrics and intent fulfillment) analytics to identify and implement tuning and training opportunities.

The solution used a QuickSight dashboard that derives these insights from the following customer interaction data used to measure VA performance:

Genesys Cloud data such as queues and data actions Business-specific data such as product and call center operations data Business API-specific data and metrics such as API response codes

The following diagram shows the solution architecture using Genesys, Amazon Lex, and QuickSight.

The solution workflow involves the following steps:

Users call in and interact with Genesys Cloud. Genesys Cloud calls an AWS Lambda routing function. This function will return a response to Genesys Cloud with the necessary data, to route the customer call. To generate a response, the function fetches routing data from an Amazon DynamoDB table, and requests an Amazon Lex V2 bot to provide an answer on the user intent. The Amazon Lex V2 bot processes the customer intent and calls a Lambda fulfillment function to fulfill the intent. The fulfillment function executes custom logic (routing and session variables logic) and calls necessary APIs to fetch the data required to fulfill the intent. The APIs process and return the data requested (such as data to perform a self-service transaction). The Amazon Lex V2 bot’s conversation logs are sent to Amazon CloudWatch (these logs will be used for business analytics, operational monitoring, and alerts). Genesys Cloud calls a third Lambda function to send customer interaction reports. The Genesys report function pushes these reports to an Amazon Simple Storage Service (Amazon S3) bucket (these reports will be used for business analytics). An Amazon Data Firehose delivery stream ships the conversation logs from CloudWatch to an S3 bucket. The Firehose delivery stream transforms the logs in Parquet or CSV format using a Lambda function. An AWS Glue crawler scans the data in Amazon S3. The crawler creates or updates the AWS Glue Data Catalog with the schema information. We use Amazon Athena to query the datasets (customer interaction reports and conversation logs). QuickSight connects to Athena to query the data from Amazon S3 using the Data Catalog.

Other design considerations

The following are other key design considerations to implement the VA solution:

Cost optimization – The solution uses Amazon S3 Bucket Keys to optimize on costs:
Reduce the number of Amazon S3 requests to AWS Key Management Service (AWS KMS) to complete encryption operations. Reduce the number of AWS KMS events in AWS CloudTrail logs.
Encryption – The solution encrypts data at rest with AWS KMS and in transit using SSL/TLS. Genesys Cloud integration – The integration between the Amazon Lex V2 bot and Genesys Cloud is done using AWS Identity and Access Management (IAM). For more details, see Genesys Cloud. Logging and monitoring – The solution monitors AWS resources with CloudWatch and uses alerts to receive notification upon failure events. Least privilege access – The solution uses IAM roles and policies to grant the minimum necessary permissions to uses and services. Data privacy – The solution handles customer sensitive data such as personally identifiable information (PII) according to compliance and data protection requirements. It implements data masking when applicable and appropriate. Secure APIs – APIs implemented in this solution are protected and designed according to compliance and security requirements. Data types – The solution defines data types, such as time stamps, in the Data Catalog (and Athena) in order to refresh data (SPICE data) in QuickSight on a schedule. DevOps – The solution is version controlled, and changes are deployed using pipelines, to enable faster release cycles. Analytics on Amazon Lex – Analytics on Amazon Lex empowers teams with data-driven insights to improve the performance of their bots. The overview dashboard provides a single snapshot of key metrics such as the total number of conversations and intent recognition rates. Principal does not use this capability due to the following reasons:
The dashboard can’t integrate with external data:
Genesys Cloud data (such as queues and data actions) Business-specific data (such as product and call center operations data) Business API-specific data and metrics (such as response codes)
The dashboard can’t be customized to add additional views and data.

Sample dashboard

With this reporting and analytics solution, Principal can consolidate data from multiple sources and visualize the performance of the VA to identify areas of opportunities for improvement. The following screenshot shows an example of their QuickSight dashboard for illustrative purposes.

Conclusion

In this post, we presented how Principal created a report and analytics solution for their VA solution using Genesys Cloud and Amazon Lex, along with QuickSight to provide customer interaction insights.

The VA solution allowed Principal to maintain its existing contact center solution with Genesys Cloud and achieve better customer experiences. It offers other benefits such as the ability for a customer to receive support on some inquiries without requiring an agent on the call (self-service). It also provides intelligent routing capabilities, leading to reduced call time and increased agent productivity.

With the implementation of this solution, Principal can monitor and derive insights from its VA solution and fine-tune accordingly its performance.

In its 2025 roadmap, Principal will continue to strengthen the foundation of the solution described in this post. In a second post, Principal will present how they automate the deployment and testing of new Amazon Lex bot versions.

AWS and Amazon are not affiliates of any company of the Principal Financial Group®. This communication is intended to be educational in nature and is not intended to be taken as a recommendation.

Insurance products issued by Principal National Life Insurance Co (except in NY) and Principal Life Insurance Company®. Plan administrative services offered by Principal Life. Principal Funds, Inc. is distributed by Principal Funds Distributor, Inc. Securities offered through Principal Securities, Inc., member SIPC and/or independent broker/dealers. Referenced companies are members of the Principal Financial Group®, Des Moines, IA 50392. ©2025 Principal Financial Services, Inc. 4373397-042025

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About the Authors

Mulay Ahmed is an Assistant Director of Engineering at Principal and well-versed in architecting and implementing complex enterprise-grade solutions on AWS Cloud.

Ruby Donald is an Assistant Director of Engineering at Principal and leads the Enterprise Virtual Assistants Engineering Team. She has extensive experience in building and delivering software at enterprise scale.