![]() Enable the conversation logs feature for your Amazon Lex bot.To implement the solution, we need to complete the following tasks: ![]() You can import this file into your Amazon Lex console or use your own Amazon Lex bot. For the export of this bot, download OrderFlowers.zip. To make the analytics more interesting, we added custom intents like BusinessHoursIntent, OffersIntent, and MyFallbackIntent. The default sample only comes with one intent: OrderFlowers. Solution overviewįor this post, we created an Amazon Lex bot using the sample OrderFlowers blueprint. ![]() You can choose from an extensive library of visualizations, charts, and tables, and add interactive features such as drill-downs and filters. QuickSight lets you easily create and publish interactive dashboards. We use the Aurora connector in QuickSight to pull in the data, create datasets and analysis, and publish a conversation analytics dashboard. For more information, see Building a business intelligence dashboard for your Amazon Lex bots. The architecture comprises streaming the conversation logs from Amazon CloudWatch to Amazon Kinesis Data Streams and having a stream consumer (an AWS Lambda function) transforming the data to be written into an Amazon Aurora database that serves as the analytics store.ĭepending on your project’s scale and your organizational needs and preferences, you may want to look into a data warehousing solution like Amazon Redshift or use Amazon Athena and Amazon Simple Storage Service (Amazon S3). The following diagram illustrates the architecture of our solution. Some of the metrics we cover in this post include: We use Amazon QuickSight to create a dashboard to visualize business KPIs, identify trends, and provide training data for bots to learn from their past failures. Amazon Lex is a service for building conversational interfaces into any application using voice and text. In this post, we build a real-time conversational analytics solution using the conversational logs from Amazon Lex. Whether you’re a product owner looking for user engagement insights or a conversation designer wanting to review missed utterances, a conversational analytics dashboard plays a vital role in serving these needs. These additional insights can help you identify how to improve your bot and user engagement continuously. By analyzing your bot’s customer conversations, you can discover challenges in user experience, trending topics, and missed utterances. They offer faster service, 24/7 availability, and lower service costs. Conversational interfaces like chatbots have become an important channel for brands to communicate with their customers, partners, and employees.
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