This publish 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 publish are these of the third-party writer and AWS isn’t chargeable for the content material or accuracy of this publish.
Principal Monetary Group® is an built-in international monetary providers firm with specialised options serving to folks, companies, and establishments attain their long-term monetary objectives 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 strong AWS Cloud infrastructure already in place, they chose a cloud-first method to create a extra customized and seamless expertise for his or her clients that might:
- Perceive buyer intents via pure language (vs. contact tone experiences)
- Help clients with self-service choices the place doable
- Precisely route buyer calls primarily based on enterprise guidelines
- Help engagement middle brokers with contextual knowledge
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 know buyer intents. Consequently, this may cut back fallback intent charges, enhance useful intent achievement charges, and result in higher buyer experiences.
On this publish, 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 purposes.
Genesys Cloud, an omni-channel orchestration and buyer relationship platform, gives a contact middle platform in a public cloud mannequin that permits fast and easy integration of AWS Contact Heart Intelligence (AWS CCI). As a part of AWS CCI, Genesys Cloud integrates with Amazon Lex, which allows self-service, clever routing, and knowledge 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 knowledge.
Resolution overview
Principal required a reporting and analytics answer that might 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 achievement) analytics to establish and implement tuning and coaching alternatives.
The answer used a QuickSight dashboard that derives these insights from the next buyer interplay knowledge used to measure VA efficiency:
- Genesys Cloud knowledge corresponding to queues and knowledge actions
- Enterprise-specific knowledge corresponding to product and name middle operations knowledge
- Enterprise API-specific knowledge and metrics corresponding to API response codes
The next diagram reveals the answer structure utilizing Genesys, Amazon Lex, and QuickSight.
The answer workflow entails the next steps:
- Customers name in and work together with Genesys Cloud.
- Genesys Cloud calls an AWS Lambda routing operate. This operate will return a response to Genesys Cloud with the mandatory knowledge, to route the client name. To generate a response, the operate fetches routing knowledge from an Amazon DynamoDB desk, and requests an Amazon Lex V2 bot to supply a solution on the person intent.
- The Amazon Lex V2 bot processes the client intent and calls a Lambda achievement operate to satisfy the intent.
- The achievement operate executes customized logic (routing and session variables logic) and calls obligatory APIs to fetch the information required to satisfy the intent.
- The APIs course of and return the information requested (corresponding to knowledge to carry out a self-service transaction).
- The Amazon Lex V2 bot’s dialog logs are despatched to Amazon CloudWatch (these logs shall be used for enterprise analytics, operational monitoring, and alerts).
- Genesys Cloud calls a 3rd Lambda operate to ship buyer interplay experiences. The Genesys report operate pushes these experiences to an Amazon Easy Storage Service (Amazon S3) bucket (these experiences shall 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 operate.
- 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 experiences 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:
- Price optimization – The answer makes use of Amazon S3 Bucket Keys to optimize on prices:
- Encryption – The answer encrypts knowledge at relaxation with AWS KMS and in transit utilizing SSL/TLS.
- Genesys Cloud integration – The mixing between the Amazon Lex V2 bot and Genesys Cloud is completed utilizing AWS Id and Entry Administration (IAM). For extra particulars, see Genesys Cloud.
- Logging and monitoring – The answer displays AWS assets 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 obligatory permissions to makes use of and providers.
- Information privateness – The answer handles buyer delicate knowledge corresponding to personally identifiable data (PII) in line with compliance and knowledge safety necessities. It implements knowledge masking when relevant and applicable.
- Safe APIs – APIs carried out on this answer are protected and designed in line with compliance and safety necessities.
- Information varieties – The answer defines knowledge varieties, corresponding to time stamps, within the Information Catalog (and Athena) as a way to refresh knowledge (SPICE knowledge) in QuickSight on a schedule.
- DevOps – The answer is model managed, and modifications are deployed utilizing pipelines, to allow quicker 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 corresponding to the overall 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 knowledge:
- Genesys Cloud knowledge (corresponding to queues and knowledge actions)
- Enterprise-specific knowledge (corresponding to product and name middle operations knowledge)
- Enterprise API-specific knowledge and metrics (corresponding to response codes)
- The dashboard can’t combine with exterior knowledge:
- The dashboard can’t be custom-made so as to add further views and knowledge.
Pattern dashboard
With this reporting and analytics answer, Principal can consolidate knowledge 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 publish, 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 supply 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 corresponding 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 lowered 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 inspiration of the answer described on this publish. In a second publish, Principal will current how they automate the deployment and testing of recent Amazon Lex bot variations.
AWS and Amazon usually are 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 suggestion.
Insurance coverage merchandise issued by Principal Nationwide Life Insurance coverage Co (besides in NY) and Principal Life Insurance coverage Firm®. Plan administrative providers 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 corporations are members of the Principal Monetary Group®, Des Moines, IA 50392. ©2025 Principal Monetary Companies, Inc. 4373397-042025
In regards to 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 intensive expertise in constructing and delivering software program at enterprise scale.
