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 creator and AWS shouldn’t be liable for the content material or accuracy of this submit.
Principal Monetary Group® is an built-in world monetary companies firm with specialised options serving to folks, 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 strong 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 might:
- Perceive buyer intents by means of pure language (vs. contact tone experiences)
- Help clients with self-service choices the place attainable
- 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 permits Principal to determine alternatives for fine-tuning, which might improve the VA’s skill to grasp buyer intents. Consequently, this can scale back fallback intent charges, enhance practical intent achievement 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 offers high-quality speech recognition and language understanding capabilities, enabling the addition of subtle, pure language chatbots to new and current functions.
Genesys Cloud, an omni-channel orchestration and buyer relationship platform, offers 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 simple inside a corporation to construct visualizations, carry out advert hoc evaluation, and rapidly 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 determine 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 equivalent to queues and knowledge actions
- Enterprise-specific knowledge equivalent to product and name middle operations knowledge
- Enterprise API-specific knowledge and metrics equivalent to API response codes
The next diagram exhibits 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 required knowledge, to route the shopper name. To generate a response, the perform fetches routing knowledge 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 shopper intent and calls a Lambda achievement perform to satisfy the intent.
- The achievement perform executes customized logic (routing and session variables logic) and calls crucial APIs to fetch the information required to satisfy the intent.
- The APIs course of and return the information requested (equivalent to knowledge to carry out a self-service transaction).
- The Amazon Lex V2 bot’s dialog logs are despatched to Amazon CloudWatch (these logs will likely be used for enterprise analytics, operational monitoring, and alerts).
- Genesys Cloud calls a 3rd Lambda perform to ship buyer interplay stories. The Genesys report perform pushes these stories to an Amazon Easy Storage Service (Amazon S3) bucket (these stories will likely 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 stories 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 knowledge 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 completed utilizing AWS Identification and Entry Administration (IAM). For extra particulars, see Genesys Cloud.
- Logging and monitoring – The answer screens 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 crucial permissions to makes use of and companies.
- Information privateness – The answer handles buyer delicate knowledge equivalent to personally identifiable data (PII) in accordance 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 accordance with compliance and safety necessities.
- Information varieties – The answer defines knowledge varieties, equivalent to time stamps, within the Information Catalog (and Athena) with the intention to refresh knowledge (SPICE knowledge) 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 offers a single snapshot of key metrics equivalent 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 (equivalent to queues and knowledge actions)
- Enterprise-specific knowledge (equivalent to product and name middle operations knowledge)
- Enterprise API-specific knowledge and metrics (equivalent to response codes)
- The dashboard can’t combine with exterior knowledge:
- The dashboard can’t be personalized 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 determine areas of alternatives for enchancment. The next screenshot exhibits an instance of their QuickSight dashboard for illustrative functions.
Conclusion
On this submit, we introduced 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 take care of its current contact middle answer with Genesys Cloud and obtain higher buyer experiences. It gives different advantages equivalent to the flexibility for a buyer to obtain assist on some inquiries with out requiring an agent on the decision (self-service). It additionally offers clever routing capabilities, resulting in decreased 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 submit. In a second submit, Principal will current how they automate the deployment and testing of latest Amazon Lex bot variations.
AWS and Amazon aren’t associates of any firm of the Principal Monetary Group®. This communication is meant to be instructional in nature and isn’t supposed 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 companies supplied by Principal Life. Principal Funds, Inc. is distributed by Principal Funds Distributor, Inc. Securities supplied by means of Principal Securities, Inc., member SIPC and/or unbiased 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 advanced enterprise-grade options on AWS Cloud.
Ruby Donald is an Assistant Director of Engineering at Principal and leads the Enterprise Digital Assistants Engineering Crew. She has in depth expertise in constructing and delivering software program at enterprise scale.
