In today’s customer-centric business world, providing excellent customer service is essential to success. Contact centers play a critical role in shaping the customer experience, and analyzing post-call interactions can improve agent performance, identify areas for improvement, and improve overall customer satisfaction. Gain valuable insight into how you do things.
Amazon Web Services (AWS) has AI and generative AI solutions that can be integrated into existing contact centers to improve post-call analytics.
Post Call Analytics (PCA) is a solution that does most of the heavy lifting associated with providing an end-to-end solution that can handle call recordings from existing contact centers. PCA provides actionable insights to identify emerging trends, identify agent coaching opportunities, and assess the general sentiment of calls.
To complement PCA, there is Live Call Analytics with Agent Assist (LCA), which performs real-time analysis during calls and provides AI and generative AI capabilities.
In this post, you’ll learn how to leverage powerful post-call analytics and visualization to help your organization make data-driven decisions and drive continuous improvement.
Enhance and enhance your post-call recording files with Amazon Q and Amazon Quicksight
Amazon QuickSight is an integrated business intelligence (BI) service that provides modern, interactive dashboards, natural language queries, paginated reports, machine learning (ML) insights, and built-in analytics at scale.
Amazon Q is a powerful new feature in Amazon QuickSight that you can use to ask questions about your data using natural language and share your presentation-ready data stories to share your insights with others. .
These features greatly enhance your post-call analytics workflow and make it easier to gain insights from your contact center data.
To start using Amazon Q with QuickSight, you first need Quicksight Enterprise Edition. You can sign up for this by following the process below.
QuickSight’s Amazon Q provides users with a new set of generative BI capabilities.
Depending on your user role, you have access to different feature sets. For example, Reader Pro users can create data stories and executive summaries. If you are an Author Pro user, you can also create topics and build dashboards using natural language. The following diagram shows the available roles and their functionality.
Below are some of the key ways Amazon Q for QuickSight improves your post-call analysis productivity.
- quick IObservations: Instead of spending time building complex dashboards and visualizations, you can give your users instant answers to questions about call volume, agent performance, customer sentiment, and more. QuickSight’s Amazon Q understands the context of your data and generates relevant visualizations on the fly.
- one time beanalysis: QuickSight’s Amazon Q allows you to perform one-time analysis on post-call data without any prior configuration. When you ask questions using natural language, QuickSight provides relevant insights, allowing you to explore your data in new ways and uncover hidden patterns.
- nature language interface: QuickSight’s Amazon Q has a natural language interface that is accessible to non-technical users. Business analysts, managers, and executives can ask questions about data post-call without having to learn complex query languages or data visualization tools.
- depending on the context rRecommendation: QuickSight’s Amazon Q can provide contextual recommendations based on your questions and available data. For example, if you ask about customer sentiment, we might offer to analyze sentiment by agent, call duration, or other relevant aspects.
- automation dash board: Amazon Q helps you accelerate dashboard development based on your questions, saving you the effort of manually building and maintaining dashboards for post-call analysis.
Using Amazon Q with QuickSight, organizations can streamline post-call analytics for faster insights, better decisions, and an improved customer experience. Amazon Q enables users of all levels to explore and understand post-call data more efficiently with natural language interfaces and automated visualizations.
Let’s take a closer look at some of the features available to Pro users, including creating executive summaries and data stories for post-call analysis.
Abstract
When users are just starting to explore new dashboards that are shared with them, they often take time to understand what is included in the dashboard and where to look for important insights. . Executive summaries are a great way to use AI to highlight key insights and draw users’ attention to specific visuals that contain metrics worth investigating further.
You can create an executive summary on any dashboard you have access to. For example, the dashboard shown in the following image.
You can change to a different sheet or apply filters and regenerate the overview to get a new set of highlights for the filtered data set, as shown in the following image.
The main benefits of using executive summaries are:
- automation IObservations: Amazon Q automatically uncovers key insights and trends from your post-call data, allowing you to quickly create executive summaries that highlight the most important information.
- customized view: Executives can customize visualizations and summaries generated by Amazon Q to ensure that executive summaries are tailored to their specific requirements and preferences.
Data storytelling
After users find interesting trends or insights within a dashboard, they often need to communicate with other users to make decisions about what to do next. That decision may be made in a meeting or offline, but a presentation with key metrics and a structured narrative is often the basis for presenting the argument. This is exactly what Data Stories is designed to support. Unlike taking a screenshot and pasting it into a document or email, where you lose all governance and your data becomes static, QuickSight stories are interactive, managed, and updated with a click. .
To build your story, always start with your dashboard. Next, choose visuals to support your story and enter prompts that tell you what your story is about. In this example, you will generate stories to gain insights and recommendations to improve your call center’s operations (see the image below).
After a while, you’ll see a fully structured story with visuals and insights, including recommendations for next steps, as shown in the following image.
Key benefits of using data stories:
- story eexpedition: Amazon Q allows you to explore post-call data through a narrative approach and ask follow-up questions based on the insights generated. This allows you to build a compelling data story that reveals the underlying patterns and trends of your contact center operations.
- depending on the context rRecommendation: Based on your question and available data, Amazon Q can provide contextual recommendations for additional visualization or analysis. These recommendations will help you uncover new perspectives and enrich your data storytelling.
- automation nstory: Amazon Q can generate automated narratives that explain visualizations and insights, making it easier to tell the story of your data to stakeholders who may not be familiar with the technical details.
- interaction panger: Create interactive data storytelling experiences by integrating Amazon Q with QuickSight presentation mode. Executives and stakeholders can ask questions during presentations, and Amazon Q generates visualizations and insights in real-time, enabling a more engaging and dynamic data storytelling experience.
conclusion
QuickSight’s Amazon Q capabilities allow you to gain valuable insights from call recordings and post-call analytics data. These insights help you make data-driven decisions to improve the customer experience, optimize contact center operations, and improve overall business performance.
In the customer-centric era, post-call analytics have become a game-changer for contact center operations. By leveraging the power of Amazon Q and Amazon QuickSight along with PCA data, you can unlock rich insights, optimize agent performance, and deliver superior customer experiences. Embrace the future of customer service with cutting-edge AI and analytics solutions from AWS and stay ahead of your competitors in today’s customer-centric environment.
About the author
Daniel Martinez is a Solutions Architect for Iberia Enterprise, part of the Worldwide Commerce Sales Organization (WWCS) at AWS.
