You’ve rolled away a conversational user interface powered by Amazon Lex, with an objective of enhancing the consumer experience for the customers. Now you wish to monitor how good it is working. Are your prospects finding it helpful? Just just How will they be utilizing it? Do they enjoy it adequate to keep coming back? How could you evaluate their interactions to add more functionality? With no clear view into your bot’s user interactions, concerns like these are hard to respond to. The current 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, according to real bot interactions. With discussion logs, all bot interactions are kept in Amazon CloudWatch Logs log teams. You need to use this conversation information to monitor your bot and gain insights that are actionable improving your bot to boost the consumer experience for the clients.
In a previous post, we demonstrated how exactly to allow discussion logs and make use of CloudWatch Logs Insights to evaluate your bot interactions. This post goes one step further by showing you the way to integrate by having an Amazon QuickSight dashboard to get company insights. Amazon QuickSight enables you to effortlessly produce and publish dashboards that are interactive. You are able to pick from a library that is extensive of, 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 utilize an Amazon Kinesis information Firehose to constantly stream conversation log information from Amazon CloudWatch Logs to A amazon s3 bucket. The Firehose delivery flow employs a serverless aws lambda function to change the natural information into JSON information records. Then you’ll usage an AWS Glue crawler to automatically discover and catalog metadata because of this information, therefore with Amazon Athena that you can query it. A template is roofed below which will produce an AWS CloudFormation stack for you personally containing many of these AWS resources, along with the required AWS Identity and Access Management (IAM) roles. With your resources in position, after that you can make your dashboard in Amazon QuickSight and connect with Athena being a databases.
This solution enables you to make use of your Amazon Lex conversation logs information to produce real time visualizations in Amazon QuickSight. As an example, with the AutoLoanBot through the earlier mentioned post, you can easily visualize individual needs by intent, or by intent and individual, to achieve an awareness about bot use and user pages. The after dashboard shows these visualizations:
This dashboard shows that re payment task and loan requests are many heavily used, but checking loan balances is utilized a lot less often.
Deploying the clear answer
To obtain started, configure an Amazon Lex bot and conversation that is enable in the usa East (N. Virginia) Area.
For our instance, we’re utilising the AutoLoanBot, but this solution can be used by you to construct an Amazon QuickSight dashboard for just about any of one’s Amazon Lex bots.
The AutoLoanBot implements a conversational program to allow users to start a loan application, check out the outstanding stability of the loan, or make that loan payment. It includes the intents that are following
- 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 past four digits of these Social Security quantity, and payment information, and operations their month-to-month installment
- CheckBalance – utilizes the user’s account quantity as well as the final four digits of the Social Security quantity to give you their outstanding stability
- Fallback – reacts to virtually any demands that the bot cannot process aided by the other intents
To deploy this solution, finish the following actions:
- After you have your bot and discussion logs configured, use the following key to introduce an AWS CloudFormation stack in us-east-1:
- For Stack name, enter title for the stack. This post utilizes the true title lex-logs-analysis:
- Under Lex Bot, for Bot, enter the title of one’s bot.
- For CloudWatch Log Group for Lex discussion Logs, go into the name associated with CloudWatch Logs log team where your discussion logs are configured.
The bot is used by this post AutoLoanBot additionally the log team car-loan-bot-text-logs:
- Select Then.
- Include any tags you might desire for the CloudFormation stack.
- Choose Then.
- Acknowledge that IAM functions will soon be developed.
- Choose Create stack.
After a few momemts, your stack should really be complete and support the resources that are following
- A delivery stream that is firehose
- An AWS Lambda change function
- A CloudWatch Logs log team for the 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 raw information from the Firehose delivery stream into specific JSON information documents grouped into batches. To learn 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, as an example:
As of this point, you need to be able 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 utilizing Athena. If you work with the AutoLoanBot, you should use a test script to build log data (conversation logs do not log interactions through the AWS Management Console). To install the test script, choose test-bot. Zip.
The Firehose delivery flow runs every minute and channels the information to your bucket that is s3. 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 are able to query your computer data via Athena. The screenshot that is following a test question you can test into the Athena Query Editor:
This question demonstrates that some users are operating into problems attempting to always check their loan balance. You can easily setup Amazon QuickSight to do more in-depth analyses and visualizations for this information. For this, finish the following steps:
- 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 ought to offer a merchant account notification and name email. As well as selecting Amazon Athena as being a information source, be sure to through the S3 bucket where your discussion log information is kept (you will get the bucket title in your CloudFormation stack).
Normally it takes a few momemts to create your account up.
- Whenever your account is ready, choose New analysis.
- Select Brand Brand Brand New information set.
- Select Anthena.
- Specify the information supply auto-loan-bot-logs.
- Choose Validate connection and confirm connectivity to Athena.
- Select Create databases.
- Choose the database that AWS Glue created (which include lexlogsdatabase into the title).
Incorporating visualizations
You can now add visualizations in Amazon QuickSight. To generate the 2 visualizations shown above, finish the steps that are following
- Through the + include symbol at the top of the dashboard, select Add visual.
- Drag the intent industry to the Y axis in the artistic.
- Include another artistic by saying the very first two steps.
- In the 2nd visual, drag userid to your Group/Color industry well.
- To sort the visuals, drag requestid to your Value field in every one.
It is possible to produce some extra visualizations to gain some insights into just how well your bot is doing. For instance, you are able to effectively evaluate how your bot is answering your users by drilling on to the needs that dropped through to the fallback intent. For this, replicate the visualizations that are preceding replace the intent measurement with inputTranscript, and put in a filter for http://speedyloan.net/reviews/blue-trust-loans/ 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 exactly how your users are getting together with your bot. In this instance, you could utilize this understanding to boost the current CheckBalance intent, implement an intent to greatly help users put up automatic re payments, industry basic questions regarding your car finance solutions, and even redirect users to a sibling bot that handles home loan applications.
Conclusion
Monitoring bot interactions is important in building effective conversational interfaces. It is possible to know very well what your users are making an effort to achieve and just how to streamline their user 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 can easily layer this analytics solution together with all of your Amazon Lex bots – give it a go!