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Developing a continuing company cleverness dashboard for the Amazon Lex bots

Developing a continuing company cleverness dashboard for the Amazon Lex bots

You’ve rolled out an interface that is conversational by Amazon Lex, with an objective of enhancing the consumer experience for the clients. Now you wish to monitor just how well it is working. Are your prospects finding it helpful? Exactly exactly How will they be utilizing it? Do they enjoy it sufficient to return? How could you evaluate their interactions to add more functionality? With no clear view into your bot’s user interactions, concerns such as these may be hard to respond to. The current launch of conversation logs for Amazon Lex makes it simple to have near-real-time exposure into just just exactly how your Lex bots are doing, considering actual bot interactions. With discussion logs, all bot interactions could be kept in Amazon CloudWatch Logs log teams. You should use this conversation information to monitor your bot and gain insights that are actionable boosting your bot to boost the consumer experience for the clients.

In a blog that is prior, we demonstrated how exactly to allow discussion logs and employ CloudWatch Logs Insights to evaluate your bot interactions. This post goes one action further by showing you the way to incorporate having an Amazon QuickSight dashboard to get company insights. Amazon QuickSight allows you to effortlessly produce and publish interactive dashboards. It is possible to pick from a substantial collection of visualizations, maps, and tables, and include interactive features such as for example drill-downs and filters.

Solution architecture

In this company cleverness dashboard solution, you certainly will make use of an Amazon Kinesis information Firehose to continuously stream discussion log information from Amazon CloudWatch Logs to an amazon bucket that is s3. The Firehose delivery flow employs a serverless aws lambda function to transform the natural information into JSON information documents. Then you’ll use an AWS Glue crawler to automatically learn and catalog metadata with this information, therefore as you are able to query it with Amazon Athena. A template is roofed below which will produce an AWS CloudFormation stack for you containing most of these AWS resources, along with the required AWS Identity and Access Management (IAM) roles. With one of these resources set up, then you can create your dashboard in Amazon QuickSight and hook up to Athena as being a databases.

This solution lets you make use of your Amazon Lex conversation logs information to generate visualizations that are live Amazon QuickSight. For instance, with the AutoLoanBot from the earlier mentioned post, you’ll visualize individual demands by intent, or by user and intent, to achieve a knowledge about bot use and individual pages. The after dashboard shows these visualizations:

This dashboard suggests that re re re payment activity and loan requests are many greatly utilized, but checking loan balances is utilized a lot less often.

Deploying the clear answer

To have started, configure an Amazon Lex bot and conversation that is enable in america East (N. Virginia) Area.

For our instance, we’re utilizing the AutoLoanBot, but this solution can be used by you to create an Amazon QuickSight dashboard for almost any of the Amazon Lex bots.

The AutoLoanBot implements an interface that is conversational enable users to start a loan application, check out the outstanding stability of the loan, or make a loan re re re payment. It includes the following intents:

  • Welcome – reacts to a greeting that is initial the consumer
  • ApplyLoan – Elicits information like the user’s name, target, and Social Security quantity, and produces a brand new loan demand
  • PayInstallment – Captures the user’s account number, the final four digits of these Social Security Number, and re payment information, and operations their month-to-month installment
  • CheckBalance – makes use of the user’s account quantity plus the final four digits of these Social Security quantity to deliver their outstanding stability
  • Fallback – reacts to virtually any demands that the bot cannot process because of the other intents

To deploy this solution, finish the steps that are following

  1. Once you’ve your bot and discussion logs configured, use the following key to introduce an AWS CloudFormation stack in us-east-1:
  2. For Stack title, enter title for the stack. This post makes use of the title lex-logs-analysis:
  3. Under Lex Bot, for Bot, enter the true title of one’s bot.
  4. For CloudWatch Log Group for Lex discussion Logs, go into the title associated with the CloudWatch Logs log team where your discussion logs are configured.

This post makes use of the bot AutoLoanBot and also the log team car-loan-bot-text-logs:

  1. Select Upcoming.
  2. Include any tags you might desire for the CloudFormation stack.
  3. Select Upcoming.
  4. Acknowledge that IAM functions will soon be produced.
  5. Select Create stack.

After a few momemts, your stack should always be complete and retain the resources that are following

  • A Firehose delivery stream
  • An AWS Lambda change function
  • A CloudWatch Logs log team when it comes to Lambda function
  • An bucket that is s3
  • An AWS Glue database and crawler
  • Four IAM functions

This solution utilizes the Lambda blueprint function kinesis-firehose-cloudwatch-logs-processor-python, which converts the data that are raw the Firehose delivery flow into specific JSON information documents grouped into batches. To learn more, see Amazon Kinesis Data Firehose Data Transformation.

AWS CloudFormation should also provide effectively subscribed the Firehose delivery flow to your CloudWatch Logs log team. The subscription can be seen by you into the AWS CloudWatch Logs system, for instance:

As of this point, you need to be in a position to test thoroughly your bot, see your log information moving from CloudWatch Logs to S3 through the Firehose delivery flow, and query your discussion log information making use of Athena. If you work with the AutoLoanBot, you need to use a test script to come up with log data (conversation logs try not to log interactions through the AWS Management Console). To install the test script, choose test-bot. Zip.

The Firehose delivery flow operates every minute and channels the information towards the bucket that is s3. The crawler is configured to perform every 10 mins (you may also run it anytime manually through the system). Following the crawler has run, you can easily query important computer data via Athena. The screenshot that is following a test question you can test within the Athena Query Editor:

This question reveals that some users are operating into problems wanting to always check their loan stability. It is possible to arranged Amazon QuickSight to do more analyses that are in-depth visualizations for this information. To get this done, finish the following actions:

  1. Through the system, launch Amazon QuickSight.

If you’re maybe not already making use of QuickSight, you could begin with a totally free test utilizing Amazon QuickSight Standard Edition. You will need to offer a merchant account notification and name current email address. As well as selecting Amazon Athena being a information source, remember to range from the bucket that is s3 your discussion log data is saved (you will get the bucket title in your CloudFormation stack).

It will take a couple of minutes to create up your bank account.

  1. As soon as your account is prepared, select New analysis.
  2. Select New information set.
  3. Select Anthena.
  4. Specify the information supply auto-loan-bot-logs.
  5. Select Validate connection and confirm connectivity to Athena.
  6. Select Create databases.
  7. Find the database that AWS Glue created (including lexlogsdatabase into the true title).

Including visualizations

You can now include visualizations in Amazon QuickSight. To produce the 2 visualizations shown above, finish the steps that are following

  1. Through the + include symbol towards the top of the dashboard, select Add visual.
  2. Drag the intent industry into the Y axis regarding the artistic.
  3. Add another artistic by saying the initial two actions.
  4. In the 2nd visual, drag userid to your Group/Color industry well.
  5. To sort the visuals, drag requestid to your Value field in every one.

You are able to produce some visualizations that are additional gain some insights into how good your bot is doing. As an example, you can easily assess exactly how effectively your bot is answering your users by drilling down into the demands that fell until the fallback intent. For this, replicate the visualizations that are preceding change the intent measurement with inputTranscript, and put in a filter for missedUtterance = 1. The after graphs reveal summaries of missed utterances, and missed utterances by user.

The after screen shot shows your term cloud visualization for missed utterances.

This particular visualization offers a effective view into exactly just how your users are getting together with your bot. In this instance, make use of this insight to enhance the CheckBalance that is existing intent implement an intent to assist users put up automatic re payments, industry basic questions regarding your car loan solutions, and also redirect users to a sibling bot that handles home loan applications.


Monitoring bot interactions is important in building effective interfaces that are conversational. It is possible to determine what your users want to achieve and exactly how to streamline their consumer experience. Amazon QuickSight in tandem with Amazon Lex conversation logs makes it simple to generate dashboards by streaming the conversation information via Kinesis information Firehose. You can easily layer this analytics solution together with all of your Amazon Lex bots – give it an attempt!

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