Customer satisfaction is a potent metric that directly influences the profitability of an organization. Establishing highly efficient contact centers requires significant automation, the ability to scale, and a mechanism of active learning through customer feedback. There is a challenge at every point in the contact center customer journey--from long hold times at the beginning to operational costs associated with long average handle times. In traditional contact centers, one solution for long hold times is enabling self-service options for customers using an Interactive Voice Response system (IVR). An IVR uses a set of automated menu options to help reduce agent call volumes by addressing common frequently asked requests without involving a live agent.

Let's say you have identified a use case in your organization that you would like to handle via a chatbot. You familiarized yourself with Amazon Lex, built a prototype, and did a few trial interactions with the bot. You liked the overall experience and now want to deploy the bot in your production environment, but aren't sure about best practices for Amazon Lex. In this post, we review the best practices for developing and deploying Amazon Lex bots, enabling you to streamline the end-to-end bot lifecycle and optimize your operations. We have covered the planning, design, and configuration phases in previous blog posts.

Conversational AI can deliver powerful, automated, interactive experiences through voice and text. Amazon Lex is a service that combines automatic speech recognition and natural language understanding technologies, so you can build these sophisticated conversational experiences. A common application of conversational AI is found in contact centers: self-service virtual agents. We're excited to announce that you can now use Amazon Chime SDK Public Switched Telephone Network (PSTN) audio to enable conversational self-service applications to reduce call resolution times and automate informational responses. The Amazon Chime SDK is a set of real-time communications components that developers can use to add audio, messaging, video, and screen-sharing to your web and mobile applications.

Rich messages are more than important in while building a bot. In contrast to the plain text, rich messages offer a cleaner, simpler, more intuitive and informative, and effortless conversation flow. In this post, we will learn how to add rich message button response in Amazon Lex bot. Buttons are the most widely used form of rich messaging. Buttons make it much easier for users to navigate through your chatbot.

This post is co-written by Arte Merritt, co-founder and CEO of Dashbot. In their own words, "Dashbot is an analytics platform for chatbots and voice skills that enables enterprises to increase engagement, satisfaction, and conversions through actionable insights and tools." After you have deployed a bot, it is critical to analyze bot interactions, learn from this analysis, and use these learnings to improve the end-user experience. Conversational interfaces are easier to analyze than websites and mobile applications. You can infer user behavior directly from conversations instead of guessing what your users want by stitching together page views and choosing events.