Building a continuing company intelligence dashboard for the Amazon Lex bots

Building a continuing company intelligence dashboard for the Amazon Lex bots

You’ve rolled away an interface that is conversational by Amazon Lex, with an objective of enhancing the user experience for the clients. Now you wish to monitor how good it is working. Are your prospects finding it helpful? Just exactly How will they be deploying it? Do they want it adequate to keep coming back? How could you analyze their interactions to add more functionality? With no view that is clear your bot’s user interactions, concerns such as these could be tough to respond to. The present launch of conversation logs for Amazon Lex makes it simple to obtain visibility that is near-real-time exactly how your Lex bots are doing, according to actual bot interactions. All bot interactions can be stored in Amazon CloudWatch Logs log groups with conversation logs. 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 make use of CloudWatch Logs Insights to evaluate your bot interactions. This post goes one action further by showing you the way to integrate with an Amazon QuickSight dashboard to get company insights. Amazon QuickSight allows you to effortlessly produce and publish dashboards that are interactive. You are able to pick from a considerable collection of visualizations, maps, and tables, and include interactive features such as for instance drill-downs and filters.

Solution architecture

In this company cleverness dashboard solution, you can expect to make use of an Amazon Kinesis Data Firehose to constantly stream discussion log information from Amazon CloudWatch Logs to an amazon bucket that is s3. The Firehose delivery flow employs A aws that is serverless lambda to change the natural information into JSON information documents. Then you’ll usage an AWS Glue crawler to automatically learn and catalog metadata because of this information, therefore that you could query it with Amazon Athena. A template is roofed below which will produce an AWS CloudFormation stack for you personally containing a few 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 make your dashboard in Amazon QuickSight and connect with Athena as a databases.

This solution enables you to make use of your Amazon Lex conversation logs information to produce visualizations that are live Amazon QuickSight. As an example, with the AutoLoanBot through the earlier mentioned article, you are able to visualize user demands by intent, or by intent and individual, to get an awareness about bot use and individual pages. The after dashboard shows these visualizations:

This dashboard shows that re payment task and loan requests are many greatly utilized, but checking loan balances is utilized notably less usually.

Deploying the clear answer

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

For our instance, we’re making use of the AutoLoanBot, but this solution can be used by you to construct an Amazon QuickSight dashboard for just about any of the Amazon Lex bots.

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

  • Welcome – reacts to a greeting that is initial the consumer
  • ApplyLoan – Elicits information including the user’s title, address, and Social Security Number, and produces a loan request that is new
  • PayInstallment – Captures the user’s account number, the final four digits of the Social Security quantity, and re payment information, and operations their month-to-month installment
  • CheckBalance – utilizes the user’s account quantity plus the final four digits of the Social Security quantity to produce their outstanding stability
  • Fallback – reacts to virtually any demands that the bot cannot process with all 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 name, enter a true title for the stack. This post utilizes 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 regarding the CloudWatch Logs log team where your discussion logs are configured.

The bot is used by this post AutoLoanBot as well as the log team car-loan-bot-text-logs:

  1. Select Then.
  2. Include any tags you might wish for the CloudFormation stack.
  3. Select Then.
  4. Acknowledge that IAM functions is supposed to be produced.
  5. Select Create stack.

After a few momemts, your stack ought to be complete and support the following resources:

  • A Firehose distribution stream
  • An AWS Lambda transformation function
  • A CloudWatch Logs log team for the Lambda function
  • An bucket that is s3
  • An AWS Glue crawler and database
  • Four IAM roles

This solution makes use of the Lambda blueprint function kinesis-firehose-cloudwatch-logs-processor-python, which converts the natural information from the Firehose delivery flow into specific JSON information documents grouped into batches. To find out more, see Amazon Kinesis information Firehose Data Transformation.

AWS CloudFormation should also provide effectively subscribed the Firehose delivery flow to your CloudWatch Logs log team. You can observe the subscription within the AWS CloudWatch Logs system, for instance:

As of this point, you ought to be in a position to examine 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 use the AutoLoanBot, you need to use a test script to build log data (discussion 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 to your S3 bucket. The crawler is configured to operate every 10 moments (you may also run it anytime manually via the system). Following the crawler has run, you can easily query your computer data via Athena. The screenshot that is following a test question you can look at into the Athena Query Editor:

This query demonstrates that some users are operating into problems wanting to check always their loan stability. You are able to put up Amazon QuickSight to do more in-depth analyses and visualizations for this information. To work on this, finish the following actions:

  1. Through the system, launch Amazon QuickSight.

If you’re perhaps not already using QuickSight, you can begin with a totally free test utilizing Amazon QuickSight Standard Edition. You will need to offer a free account name and notification current email address. Along with choosing Amazon Athena as being an information source, remember to are the bucket that is s3 your discussion log information is kept (you will get the bucket title in your CloudFormation stack).

Normally it takes a couple of minutes to create up your bank account.

  1. If your account is prepared, select New analysis.
  2. Select Brand Brand New information set.
  3. Choose Anthena.
  4. Specify the info 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 when you look at the true title).

Incorporating visualizations

You will add visualizations in Amazon QuickSight. To produce the 2 visualizations shown above, finish the steps that are following

  1. Through the + include symbol near the top of the dashboard, select Add visual.
  2. Drag the intent industry to your Y axis in the artistic.
  3. Include another artistic by saying the initial two actions.
  4. From 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 exactly how well your bot is doing. For instance, it is possible to effectively evaluate how your bot is giving an answer to your users by drilling down into the needs that dropped until the fallback intent. To achieve this, replicate the preceding visualizations but change the intent measurement with inputTranscript, and put in a filter for missedUtterance = 1. The graphs that are following summaries of missed utterances, and missed utterances by individual.

The screen that is following shows your term cloud visualization for missed utterances.

This kind of visualization offers a view that is powerful just just how your users are getting together with your bot. In this instance, you could utilize this understanding to enhance the existing CheckBalance intent, implement an intent to assist users put up automatic re re re payments, industry basic questions regarding your car loan services, and also redirect users up to a cousin bot that handles mortgage applications.


Monitoring bot interactions is crucial in building effective conversational interfaces. You’ll know very well what your users are making an effort to achieve and just how to streamline their consumer experience. Amazon QuickSight in tandem with Amazon Lex conversation logs allows you to produce dashboards by streaming the discussion information via moneykey login Kinesis information Firehose. It is possible to layer this analytics solution in addition to all of your Amazon Lex bots – give it an attempt!

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