Creating company cleverness dashboard for the Amazon Lex bots
You’ve rolled away a conversational program driven by Amazon Lex, with a target of enhancing the consumer experience for the clients. So Now you desire to monitor exactly how well it is working. Are your web visitors finding it helpful? Just just How will they be utilizing it? Do they enjoy it sufficient to keep coming back? How will you evaluate their interactions to add more functionality? With out a view that is clear your bot’s user interactions, concerns such as these could be hard to respond to. The current launch of conversation logs for Amazon Lex makes it simple to obtain visibility that is near-real-time exactly just 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 can make use of this conversation data to monitor your bot and gain insights that are actionable boosting your bot to enhance the consumer experience for the customers.
In a previous article, we demonstrated just how 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 the way to incorporate with an Amazon QuickSight dashboard to get company insights. Amazon QuickSight allows you to easily produce and publish interactive dashboards. You are able to pick from a substantial collection of visualizations, maps, and tables, and include interactive features such as for instance drill-downs and filters.
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 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 with this information, therefore with Amazon Athena that you can query it. A template is roofed below which will produce an AWS CloudFormation stack for moneykey login you personally containing many of these AWS resources, along with the required AWS Identity and Access Management (IAM) roles. By using these resources set up, after that you can make your dashboard in Amazon QuickSight and hook up to Athena being a databases.
This solution lets you make use of your Amazon Lex conversation logs information to generate real time visualizations in Amazon QuickSight. As an example, utilising the AutoLoanBot through the mentioned before post, you can easily visualize individual demands by intent, or by intent and individual, to get a knowledge about bot use and individual pages. The dashboard that is following these visualizations:
This dashboard shows that re payment task and loan requests are many greatly utilized, but checking loan balances is utilized not as usually.
Deploying the answer
To have 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 you can make use of this solution to create an Amazon QuickSight dashboard for almost any of the Amazon Lex bots.
The AutoLoanBot implements a conversational user interface to allow users to start that loan application, look at the outstanding stability of the loan, or make that loan re payment. It includes the following intents:
- Welcome – Responds to a short greeting from an individual
- ApplyLoan – Elicits information including the user’s title, target, and Social Security quantity, and creates a brand new loan demand
- PayInstallment – Captures the user’s account number, the past four digits of these Social Security quantity, and re payment information, and operations their monthly installment
- CheckBalance – makes use of the user’s account quantity and also the final four digits of the Social Security quantity to present their outstanding stability
- Fallback – reacts to your needs that the bot cannot process because of the other intents
To deploy this solution, finish the steps that are following
- Once you’ve your bot and discussion logs configured, use the button that is following introduce an AWS CloudFormation stack in us-east-1:
- For Stack title, enter title for the stack. This post utilizes the true title lex-logs-analysis:
- Under Lex Bot, for Bot, enter the title of the bot.
- For CloudWatch Log Group for Lex discussion Logs, enter the title regarding the CloudWatch Logs log team where your discussion logs are configured.
This post utilizes the bot AutoLoanBot additionally the log team car-loan-bot-text-logs:
- Select Upcoming.
- Include any tags you might wish for the CloudFormation stack.
- Select Then.
- Acknowledge that IAM roles is going to be developed.
- Select Create stack.
After a few minutes, your stack should always be complete and support the following resources:
- A Firehose distribution 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 natural information from the Firehose delivery flow into specific JSON data documents grouped into batches. To find out more, see Amazon Kinesis information Firehose Data Transformation.
AWS CloudFormation should have successfully subscribed also the Firehose delivery flow to your CloudWatch Logs log team. You can view the registration when you look at the AWS CloudWatch Logs system, for instance:
Only at that true 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 stream, and query your discussion log information making use of 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 streams the info towards the 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 your computer data via Athena. The after screenshot shows a test question you can look at when you look at the Athena Query Editor:
This question reveals that some users are operating into problems attempting 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 get this done, finish the following actions:
- Through the system, launch Amazon QuickSight.
You can start with a free trial using Amazon QuickSight Standard Edition if you’re not already using QuickSight. You’ll want to offer a free account notification and name current email address. Along with selecting Amazon Athena as being an information source, remember to range from the S3 bucket where your discussion log data is saved (you will get the bucket title in your CloudFormation stack).
Normally it takes a few momemts setting up your account.
- As soon as your account is prepared, choose New analysis.
- Select Brand New information set.
- Select Anthena.
- Specify the info supply auto-loan-bot-logs.
- Select Validate connection and confirm connectivity to Athena.
- Select Create databases.
- Find the database that AWS Glue created (which include lexlogsdatabase into the title).
You can now include visualizations in Amazon QuickSight. To produce the 2 visualizations shown above, finish the following actions:
- Through the + include symbol at the top of the dashboard, select Add visual.
- Drag the intent field into the Y axis from the artistic.
- Include another visual by saying the initial two actions.
- In the 2nd visual, drag userid to your Group/Color industry well.
- To sort the visuals, drag requestid towards the Value field in each one of these.
You are able to produce some extra visualizations to gain some insights into exactly how well your bot is doing. As an example, it is possible to assess exactly how efficiently your bot is giving an answer to your users by drilling on to the needs that dropped until the fallback intent. To achieve this, replicate the visualizations that are preceding replace the intent measurement with inputTranscript, and include a filter for missedUtterance = 1 ) The following graphs reveal summaries of missed utterances, and missed utterances by individual.
The after screen shot shows your term cloud visualization for missed utterances.
This type of visualization supplies a powerful view into exactly 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 simply help users put up automatic re payments, industry basic questions regarding your car finance solutions, and also redirect users to a sis bot that handles mortgage applications.
Monitoring bot interactions is crucial in building effective interfaces that are conversational. It is possible to determine what your users want to achieve and just 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. It is possible to layer this analytics solution along with all of your Amazon Lex bots – give it a go!