This submit was cowritten by Mulay Ahmed, Assistant Director of Engineering, and Ruby Donald, Assistant Director of Engineering at Principal Monetary Group. The content material and opinions on this submit are these of the third-party writer and AWS isn’t accountable for the content material or accuracy of this submit.
Principal Monetary Group® is an built-in international monetary companies firm with specialised options serving to individuals, companies, and establishments attain their long-term monetary targets and entry better monetary safety.
With US contact facilities that deal with thousands and thousands of buyer calls yearly, Principal® needed to additional modernize their buyer name expertise. With a sturdy AWS Cloud infrastructure already in place, they chose a cloud-first strategy to create a extra personalised and seamless expertise for his or her clients that may:
- Perceive buyer intents via pure language (vs. contact tone experiences)
- Help clients with self-service choices the place potential
- Precisely route buyer calls primarily based on enterprise guidelines
- Help engagement middle brokers with contextual information
Initially, Principal developed a voice Digital Assistant (VA) utilizing an Amazon Lex bot to acknowledge buyer intents. The VA can carry out self-service transactions or route clients to particular name middle queues within the Genesys Cloud contact middle platform, primarily based on buyer intents and enterprise guidelines.
As clients work together with the VA, it’s important to constantly monitor its well being and efficiency. This enables Principal to establish alternatives for fine-tuning, which may improve the VA’s means to grasp buyer intents. Consequently, this may cut back fallback intent charges, enhance purposeful intent success charges, and result in higher buyer experiences.
On this submit, we discover how Principal used this chance to construct an built-in voice VA reporting and analytics answer utilizing an Amazon QuickSight dashboard.
Amazon Lex is a service for constructing conversational interfaces utilizing voice and textual content. It gives high-quality speech recognition and language understanding capabilities, enabling the addition of refined, pure language chatbots to new and present functions.
Genesys Cloud, an omni-channel orchestration and buyer relationship platform, gives a contact middle platform in a public cloud mannequin that allows fast and easy integration of AWS Contact Middle Intelligence (AWS CCI). As a part of AWS CCI, Genesys Cloud integrates with Amazon Lex, which allows self-service, clever routing, and information assortment capabilities.
QuickSight is a unified enterprise intelligence (BI) service that makes it easy inside a corporation to construct visualizations, carry out advert hoc evaluation, and shortly get enterprise insights from their information.
Resolution overview
Principal required a reporting and analytics answer that may monitor VA efficiency primarily based on buyer interactions at scale, enabling Principal to enhance the Amazon Lex bot efficiency.
Reporting necessities included buyer and VA interplay and Amazon Lex bot efficiency (goal metrics and intent success) analytics to establish and implement tuning and coaching alternatives.
The answer used a QuickSight dashboard that derives these insights from the next buyer interplay information used to measure VA efficiency:
- Genesys Cloud information comparable to queues and information actions
- Enterprise-specific information comparable to product and name middle operations information
- Enterprise API-specific information and metrics comparable to API response codes
The next diagram reveals the answer structure utilizing Genesys, Amazon Lex, and QuickSight.
The answer workflow includes the next steps:
- Customers name in and work together with Genesys Cloud.
- Genesys Cloud calls an AWS Lambda routing perform. This perform will return a response to Genesys Cloud with the mandatory information, to route the client name. To generate a response, the perform fetches routing information from an Amazon DynamoDB desk, and requests an Amazon Lex V2 bot to offer a solution on the consumer intent.
- The Amazon Lex V2 bot processes the client intent and calls a Lambda success perform to meet the intent.
- The success perform executes customized logic (routing and session variables logic) and calls essential APIs to fetch the information required to meet the intent.
- The APIs course of and return the information requested (comparable to information to carry out a self-service transaction).
- The Amazon Lex V2 bot’s dialog logs are despatched to Amazon CloudWatch (these logs can be used for enterprise analytics, operational monitoring, and alerts).
- Genesys Cloud calls a 3rd Lambda perform to ship buyer interplay reviews. The Genesys report perform pushes these reviews to an Amazon Easy Storage Service (Amazon S3) bucket (these reviews can be used for enterprise analytics).
- An Amazon Information Firehose supply stream ships the dialog logs from CloudWatch to an S3 bucket.
- The Firehose supply stream transforms the logs in Parquet or CSV format utilizing a Lambda perform.
- An AWS Glue crawler scans the information in Amazon S3.
- The crawler creates or updates the AWS Glue Information Catalog with the schema data.
- We use Amazon Athena to question the datasets (buyer interplay reviews and dialog logs).
- QuickSight connects to Athena to question the information from Amazon S3 utilizing the Information Catalog.
Different design issues
The next are different key design issues to implement the VA answer:
- Value optimization – The answer makes use of Amazon S3 Bucket Keys to optimize on prices:
- Encryption – The answer encrypts information at relaxation with AWS KMS and in transit utilizing SSL/TLS.
- Genesys Cloud integration – The combination between the Amazon Lex V2 bot and Genesys Cloud is finished utilizing AWS Id and Entry Administration (IAM). For extra particulars, see Genesys Cloud.
- Logging and monitoring – The answer displays AWS sources with CloudWatch and makes use of alerts to obtain notification upon failure occasions.
- Least privilege entry – The answer makes use of IAM roles and insurance policies to grant the minimal essential permissions to makes use of and companies.
- Information privateness – The answer handles buyer delicate information comparable to personally identifiable data (PII) based on compliance and information safety necessities. It implements information masking when relevant and applicable.
- Safe APIs – APIs applied on this answer are protected and designed based on compliance and safety necessities.
- Information sorts – The answer defines information sorts, comparable to time stamps, within the Information Catalog (and Athena) so as to refresh information (SPICE information) in QuickSight on a schedule.
- DevOps – The answer is model managed, and modifications are deployed utilizing pipelines, to allow sooner launch cycles.
- Analytics on Amazon Lex – Analytics on Amazon Lex empowers groups with data-driven insights to enhance the efficiency of their bots. The overview dashboard gives a single snapshot of key metrics comparable to the full variety of conversations and intent recognition charges. Principal doesn’t use this functionality as a result of following causes:
- The dashboard can’t combine with exterior information:
- Genesys Cloud information (comparable to queues and information actions)
- Enterprise-specific information (comparable to product and name middle operations information)
- Enterprise API-specific information and metrics (comparable to response codes)
- The dashboard can’t combine with exterior information:
- The dashboard can’t be custom-made so as to add further views and information.
Pattern dashboard
With this reporting and analytics answer, Principal can consolidate information from a number of sources and visualize the efficiency of the VA to establish areas of alternatives for enchancment. The next screenshot reveals an instance of their QuickSight dashboard for illustrative functions.
Conclusion
On this submit, we offered how Principal created a report and analytics answer for his or her VA answer utilizing Genesys Cloud and Amazon Lex, together with QuickSight to offer buyer interplay insights.
The VA answer allowed Principal to keep up its present contact middle answer with Genesys Cloud and obtain higher buyer experiences. It gives different advantages comparable to the flexibility for a buyer to obtain assist on some inquiries with out requiring an agent on the decision (self-service). It additionally gives clever routing capabilities, resulting in diminished name time and elevated agent productiveness.
With the implementation of this answer, Principal can monitor and derive insights from its VA answer and fine-tune accordingly its efficiency.
In its 2025 roadmap, Principal will proceed to strengthen the muse of the answer described on this submit. In a second submit, Principal will current how they automate the deployment and testing of recent Amazon Lex bot variations.
AWS and Amazon are usually not associates of any firm of the Principal Monetary Group®. This communication is meant to be academic in nature and isn’t meant to be taken as a advice.
Insurance coverage merchandise issued by Principal Nationwide Life Insurance coverage Co (besides in NY) and Principal Life Insurance coverage Firm®. Plan administrative companies supplied by Principal Life. Principal Funds, Inc. is distributed by Principal Funds Distributor, Inc. Securities supplied via Principal Securities, Inc., member SIPC and/or impartial dealer/sellers. Referenced firms are members of the Principal Monetary Group®, Des Moines, IA 50392. ©2025 Principal Monetary Providers, Inc. 4373397-042025
Concerning the Authors
Mulay Ahmed is an Assistant Director of Engineering at Principal and well-versed in architecting and implementing complicated enterprise-grade options on AWS Cloud.
Ruby Donald is an Assistant Director of Engineering at Principal and leads the Enterprise Digital Assistants Engineering Workforce. She has in depth expertise in constructing and delivering software program at enterprise scale.
