Developing company cleverness dashboard for the Amazon Lex bots

You’ve rolled down an interface that is conversational by Amazon Lex, with an objective of enhancing the consumer experience for your clients. So Now you desire to monitor exactly how well it is working. Are your visitors finding it helpful? Just exactly just How will they be utilizing it? Do they want it sufficient to return? How could you evaluate their interactions to add more functionality? With out a clear view into your bot’s user interactions, concerns such as these may be hard to respond to. The present launch of conversation logs for Amazon Lex makes it simple to have near-real-time presence into exactly exactly how your Lex bots are doing, centered on real bot interactions. All bot interactions can be stored in Amazon CloudWatch Logs log groups with conversation logs. You need to use this conversation data to monitor your bot and gain insights that are actionable boosting your bot to boost an individual experience for the clients.

In a blog that is prior, we demonstrated simple tips to allow discussion logs and make use of CloudWatch Logs Insights to assess your bot interactions. This post goes one step further by showing you how to incorporate having an Amazon QuickSight dashboard to get company insights. Amazon QuickSight allows you to effortlessly produce and publish interactive dashboards. You can easily 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 certainly will make use of an Amazon Kinesis information Firehose to constantly stream discussion log information from Amazon CloudWatch Logs to A amazon s3 bucket. The Firehose delivery stream employs a serverless aws lambda function to transform the raw information into JSON information documents. Then you’ll usage an AWS Glue crawler to automatically discover and catalog metadata with this information, therefore that one can query it with Amazon Athena. A template is roofed below that may produce an AWS CloudFormation stack for you personally containing most of these AWS resources, along with the required AWS Identity and Access Management (IAM) roles. With your resources set up, you may then make your dashboard in Amazon QuickSight and hook up to Athena being a repository.

This solution enables you to make use of your Amazon Lex conversation logs information to produce live visualizations in Amazon QuickSight. For instance, utilising the AutoLoanBot through the mentioned before article, you can easily visualize individual demands by intent, or by intent and individual, to achieve a knowledge about bot use and individual pages. The dashboard that is following these visualizations:

This dashboard shows that payment task and applications are many greatly utilized, but checking loan balances is utilized not as often.

Deploying the clear answer

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

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

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

  • Welcome – reacts to a greeting that is initial an individual
  • ApplyLoan – Elicits information like the user’s name, target, and Social Security quantity, and produces a loan request that is new
  • PayInstallment – Captures the user’s account number, the past four digits of these Social Security quantity, and re payment information, and processes their month-to-month installment
  • CheckBalance – makes use of the user’s account quantity therefore the final four digits of the Social Security quantity to offer their outstanding stability
  • Fallback – reacts to virtually any needs 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 button that is following introduce an AWS CloudFormation stack in us-east-1:
  2. For Stack title, enter a true title for the stack. This post utilizes the title lex-logs-analysis:
  3. Under Lex Bot, for Bot, enter the true name of the bot.
  4. For CloudWatch Log Group for Lex discussion Logs, enter the true title of this CloudWatch Logs log team where your discussion logs are configured.

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

  1. Select Upcoming.
  2. Include any tags you may desire for the CloudFormation stack.
  3. Select Upcoming.
  4. Acknowledge that IAM functions is likely to be produced.
  5. Select Create stack.

After a couple of minutes, your stack should always be complete and retain the following resources:

  • A Firehose distribution stream
  • An AWS Lambda transformation function
  • A CloudWatch Logs log team when it comes to Lambda function
  • An S3 bucket
  • An AWS Glue database and crawler
  • Four IAM functions

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

AWS CloudFormation should likewise have effectively subscribed the Firehose delivery flow to your CloudWatch Logs log team. You can view the registration into the AWS CloudWatch Logs system, for instance:

As of this true 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 utilizing Athena. You can use a test script to generate log data (conversation logs do not log interactions through the AWS Management Console) if you are using the AutoLoanBot,. To install the test script, choose test-bot. Zip.

The Firehose delivery flow operates every minute and channels the info to your S3 bucket. The crawler is configured to operate every 10 minutes(you can also anytime run it 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 look at into the Athena Query Editor:

This question indicates that some users are operating into problems wanting to always check their loan stability. You can easily setup Amazon QuickSight to do more analyses that are in-depth visualizations for this information. To get this done, finish the steps that are following

  1. Through the system, launch Amazon QuickSight.

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

It will take a few minutes to create your account up.

  1. Whenever your account is prepared, select New analysis.
  2. Select Brand New information set.
  3. Select Anthena.
  4. Specify the info supply auto-loan-bot-logs.
  5. Select Validate connection and confirm connectivity to Athena.
  6. Select Create data source.
  7. Choose the database that AWS Glue created (including lexlogsdatabase within the true title).

Incorporating visualizations

You will include visualizations in Amazon QuickSight. To produce the two visualizations shown above, complete the steps that are following

  1. Through the + include symbol at the top of the dashboard, select Add visual
  2. Drag the intent industry to your Y axis regarding the artistic.
  3. Include another artistic by saying the initial two actions.
  4. Regarding 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 extra visualizations to gain some insights into exactly how well your bot is doing. For instance, you are able to effectively evaluate how your bot is giving an answer to your users by drilling on to the needs that dropped until the fallback intent. To get this done, replicate the preceding visualizations but change the intent measurement with inputTranscript, and include a filter for missedUtterance = 1. The after graphs reveal summaries of missed utterances, and missed utterances by individual.

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

This sort of visualization supplies a effective view into exactly just exactly how your users are getting together with your bot. In this instance, make use of this understanding to boost the current CheckBalance intent, implement an intent to greatly help users put up automatic payments, industry basic questions regarding your car finance solutions, and also redirect users to a cousin bot that handles home loan applications.


Monitoring bot interactions is important in building effective conversational interfaces. It is possible to know very well what your users want to accomplish and exactly how to streamline their consumer experience. Amazon QuickSight in tandem with Amazon Lex conversation logs makes it simple to produce dashboards by streaming the discussion information via Kinesis information Firehose. You are able to layer this analytics solution together with any of your Amazon Lex bots – give it a go!

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