Tag Archives: Amazon Connect

Serverless architecture for optimizing Amazon Connect call-recording archival costs

Post Syndicated from Brian Maguire original https://aws.amazon.com/blogs/architecture/serverless-architecture-for-optimizing-amazon-connect-call-recording-archival-costs/

In this post, we provide a serverless solution to cost-optimize the storage of contact-center call recordings. The solution automates the scheduling, storage-tiering, and resampling of call-recording files, resulting in immediate cost savings. The solution is an asynchronous architecture built using AWS Step Functions, Amazon Simple Queue Service (Amazon SQS), and AWS Lambda.

Amazon Connect provides an omnichannel cloud contact center with the ability to maintain call recordings for compliance and gaining actionable insights using Contact Lens for Amazon Connect and AWS Contact Center Intelligence Partners. The storage required for call recordings can quickly increase as customers meet compliance retention requirements, often spanning six or more years. This can lead to hundreds of terabytes in long-term storage.

Solution overview

When an agent completes a customer call, Amazon Connect sends the call recording to an Amazon Simple Storage Solution (Amazon S3) bucket with: a date and contact ID prefix, the file stored in the .WAV format and encoded using bitrate 256 kb/s, pcm_s16le, 8000 Hz, two channels, and 256 kb/s. The call-recording files are approximately 2 Mb/minute optimized for high-quality processing, such as machine learning analysis (see Figure 1).

Asynchronous architecture for batch resampling for call-recording files on Amazon S3

Figure 1. Asynchronous architecture for batch resampling for call-recording files on Amazon S3

When a call recording is sent to Amazon S3, downstream post-processing is often performed to generate analytics reports for agents and quality auditors. The downstream processing can include services that provide transcriptions, quality-of-service metrics, and sentiment analysis to create reports and trigger actionable events.

While this processing is often completed within minutes, the downstream applications could require processing retries. As audio resampling reduces the quality of the audio files, it is essential to delay resampling until after processing is completed. As processed call recordings are infrequently accessed days after a call is completed, with only a small percentage accessed by agents and call quality auditors, call recordings can benefit from resampling and transitioning to long-term Amazon S3 storage tiers.

In Figure 2, multiple AWS services work together to provide an end-to-end cost-optimization solution for your contact center call recordings.

AWS Step Function orchestrates the batch resampling of call recordings

Figure 2. AWS Step Function orchestrates the batch resampling of call recordings

An Amazon EventBridge schedule rule triggers the step function to perform the batch resampling process for all call recordings from the previous 7 days.

In the first step function task, the Lambda function task iterates the S3 bucket using the ListObjectsV2 API, obtaining the call recordings (1000 objects per iteration) with the date prefix from 7 days ago.

The next task invokes a Lambda function inserting the call recording objects into the Amazon SQS queue. The audio-conversion Lambda function receives the Amazon SQS queue events via the event source mapping Lambda integration. Each concurrent Lambda invocation downloads a stored call recording from Amazon S3, resampling the .WAV with ffmpeg and tagging the S3 object with a “converted=True” tag.

Finally, the conversion function uploads the resampled file to Amazon S3, overwriting the original call recording with the resampled recording using a cost-optimized storage class, such as S3 Glacier Instant Retrieval. S3 Glacier Instant Retrieval provides the lowest cost for long-lived data that is rarely accessed and requires milliseconds retrieval, such as for contact-center call-recording playback. By default, Amazon Connect stores call recordings with S3 Versioning enabled, maintaining the original file as a version. You can use lifecycle policies to delete object versions from a version-enabled bucket to permanently remove the original version, as this will minimize the storage of the original call recording.

This solution captures failures within the step function workflow with logging and a dead-letter queue, such as when an error occurs with resampling a recording file. A Step Function task monitors the Amazon SQS queue using the AWS Step Function integration with AWS SDK with SQS and ending the workflow when the queue is emptied. Table 1 demonstrates the default and resampled formats.

Detailed AWS Step Functions state machine diagram

Figure 3. Detailed AWS Step Functions state machine diagram

Resampling

Table 1. Default and resampled call recording audio formats

Audio sampling formats File size/minute Notes
Bitrate 256 kb/s, pcm_s16le, 8000 Hz, 2 channels, 256 kb/s 2 MB The default for Amazon Connect call recordings. Sampled for audio quality and call analytics processing.
Bitrate 64 kb/s, pcm_alaw, 8000 Hz, 1 channel, 64 kb/s 0.5 MB Resampled to mono channel 8 bit. This resampling is not reversible and should only be performed after all call analytics processing has been completed.

Cost assessment

For pricing information for the primary services used in the solution, visit:

The costs incurred by the solution are based on usage and are AWS Free Tier eligible. After the AWS Free Tier allowance is consumed, usage costs are approximately $0.11 per 1000 minutes of call recordings. S3 Standard starts at $0.023 per GB/month; and S3 Glacier Instant Retrieval is $0.004 per GB/month, with $0.003 per GB of data retrieval. During a 6-year compliance retention term, the schedule-based resampling and storage tiering results in significant cost savings.

In the 6-year example detailed in Table 2, the S3 Standard storage costs would be approximately $356,664 for 3 million call-recording minutes/month. The audio resampling and S3 Glacier Instant Retrieval tiering reduces the 6-year cost to approximately $41,838.

Table 2. Multi-year costs savings scenario (3 million minutes/month) in USD

Year Total minutes (3 million/month) Total storage (TB) Cost of storage, S3 Standard (USD) Cost of running the resampling (USD) Cost of resampling solution with S3 Glacier Instant Retrieval (USD)
1 36,000,000 72 10,764 3,960 4,813
2 72,000,000 108 30,636 3,960 5,677
3 108,000,000 144 50,508 3,960 6,541
4 144,000,000 180 70,380 3,960 7,405
5 180,000,000 216 90,252 3,960 8,269
6 216,000,000 252 110,124 3,960 9,133
Total 1,008,000,000 972 356,664 23,760 41,838

To explore PCA costs for yourself, use AWS Cost Explorer or choose Bill Details on the AWS Billing Dashboard to see your month-to-date spend by service.

Deploying the solution

The code and documentation for this solution are available by cloning the git repository and can be deployed with AWS Cloud Development Kit (AWS CDK).

Bash
# clone repository
git clone https://github.com/aws-samples/amazon-connect-call-recording-cost-optimizer.git
# navigate the project directory
cd amazon-connect-call-recording-cost-optimizer

Modify the cdk.context.json with your environment’s configuration setting, such as the bucket_name. Next, install the AWS CDK dependencies and deploy the solution:

:# ensure you are in the root directory of the repository

./cdk-deploy.sh

Once deployed, you can test the resampling solution by waiting for the EventBridge schedule rule to execute based on the num_days_age setting that is applied. You can also manually run the AWS Step Function with a specified date, for example {"specific_date":"01/01/2022"}.

The AWS CDK deployment creates the following resources:

  • AWS Step Function
  • AWS Lambda function
  • Amazon SQS queues
  • Amazon EventBridge rule

The solution handles the automation of transitioning a storage tier, such as S3 Glacier Instant Retrieval. In addition, Amazon S3 Lifecycles can be set manually to transition the call recordings after resampling to alternative Amazon S3 Storage Classes.

Cleanup

When you are finished experimenting with this solution, cleanup your resources by running the command:

cdk destroy

This command deletes the AWS CDK-deployed resources. However, the S3 bucket containing your call recordings and CloudWatch log groups are retained.

Conclusion

This call recording resampling solution offers an automated, cost-optimized, and scalable architecture to reduce long-term compliance call recording archival costs.

New – High Volume Outbound Communication with Amazon Connect Outbound Campaigns

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/new-high-volume-outbound-communication-with-amazon-connect-outbound-campaigns/

The new high volume outbound communication capability in Amazon Connect which was announced at Enterprise Connect last year, is now generally available to all. It is named Amazon Connect outbound campaigns.

If you haven’t heard about Amazon Connect, it is an easy-to-use cloud contact center service that helps companies of any size deliver superior customer service at lower cost. You can read the original blog post Jeff wrote at launch in 2017, with amazing Lego art 🙂

Contact centers not only receive calls and communications, but they also send outbound communications to customers. There are a variety of reasons to send outbound communication: appointment reminders, telemarketing, subscription renewals, and billing reminders. The vast majority of these communications are phone calls, and in many contact centers, agents make the calls manually using customer contact lists in external systems. Since customers only answer about ten percent of calls, these agents can spend nearly half of their time dialing and waiting. This can result in millions of dollars in lost productivity each year for a contact center with as few as 200 agents.

To help you to address this challenge, today we are adding to Amazon Connect outbound campaigns a set of high-volume outbound communication capabilities that allows you to proactively reach more of your customers across voice, SMS, and email. When using this capability, you will have a scalable way for proactive outreach for hundreds to millions of your customers, and you will increase your agents’ productivity and lower your operational costs.

Amazon Connect outbound campaigns delivers a predictive phone dialer. The dialer includes an answering machine detection system powered by machine learning. It allows the automatic detection of answering machines for voice calls and passes calls to agents only when the call is answered by a human. The dialer also adjusts the call rate depending on factors such as percentage of human-answered the calls, call duration, and agent availability. There is no integration required to get the benefit of existing Amazon Connect features, such as automated workflows, routing, and machine learning capabilities like Contact Lens. You now have a single system for inbound and outbound communications.

To further refine the customer experience or use multiple channels in your campaigns, for example, to send an SMS or email message to your customers when they do not answer calls, you have the option to use Amazon Pinpoint. Amazon Pinpoint is a flexible and scalable outbound and inbound marketing communications service. It allows you to define customer segments, define the customer journey, define the contact strategy, and more. Amazon Pinpoint is the system handling high-volume SMS and email campaigns.

To better understand how Amazon Connect, Amazon Pinpoint, and other AWS services work together, you can refer to this very detailed blog post.

Let’s show you how it works
Imagine I am a contact center manager, and I want to create an outbound call campaign to target a selected list of customers.

I first import my customer contact list from a spreadsheet on Amazon S3. I may also import it from popular customer relationship management (CRM) and marketing automation applications, such as Marketo, Salesforce, Twilio’s Segment, ServiceNow, Shopify, Zendesk, and Amazon Pinpoint itself.

Amazon Connect outbound campaigns - import contact 2

Then I create a campaign and define some journey parameters: the communication channel, the start time, and the corresponding content, such as a call script, email template, or SMS message. At the scheduled start time, the journey is executed using Amazon Connect for calls or Amazon Pinpoint for SMS or emails, as specified.

Amazon Connect outbound campaigns - create campaign

When I configure the campaign to run in Predictive dial mode, as I mentioned before, the dialer automatically adjusts the dial rate based on the duration of calls and the real-time availability of agents. Once a call is answered, Amazon Connect distinguishes whether it is a live voice or a recorded message and routes the live customer to an available agent in the Amazon Connect agent application, where the agent can see the call script that I specified during setup, along with relevant customer information.

As explained earlier, I may use Amazon Pinpoint to define the customer journey. By doing so, I can combine voice, email, and SMS channels in the same outbound communication campaign to improve the efficiency of my agents and my customer’s experience. For example, a financial institution can use Amazon Connect to send an SMS notification to remind a customer of a missed payment and include a link to request a call back from an agent. When a call is requested, Amazon Connect automatically queues the call, dials the customer’s number, detects their voice, and connects an available agent to the customer.

Amazon Connect outbound campaigns - journey workflow

Amazon Pinpoint allows you to define the details of the customer journey.

Amazon Connect outbound campaigns - setup quiet times

As usual with AWS services, I can analyze contact events sent via Amazon EventBridge. EventBridge is a serverless event bus that makes it easier to build event-driven applications at scale using events generated from your applications, integrated software-as-a-service (SaaS) applications, and AWS service. When filtering or analyzing events posted to EventBridge, I can create metrics such as time to connect to an agent, duration of the contact, and call abandonment rate

These metrics help me understand the status of my campaign and ensure compliance with applicable regulations, such as maximum call abandonment rates. I also can use historical reports of these metrics to understand the effectiveness of all my communications campaigns over time.

Amazon Connect outbound campaigns - jounrey metrics

Speaking of compliance, we do not want anyone to abuse the system, intentionally or not, or to break any local compliance rules.

Access and Compliance
Using automated services to drive outbound communication campaigns is strictly regulated in several countries and territories. For example, the US adopted the Telephone Consumer Protection Act (TCPA) in 1991, and the United Kingdom’s Office of Communications has similar rules.

Amazon Connect outbound campaigns gives you the tools to stay compliant with these regulations and many others. However, just like with traditional IT security, it is a shared responsibility. It is your responsibility to use the service in a compliant manner. We are happy to assist you in addressing specific use cases.

Let’s share two examples to illustrate how Amazon Connect outbound campaigns can help you meet your compliance status: respect quiet time and monitor call abandonment rate.

The use of quiet times allows contact center managers to configure a schedule for channel communications based on the day of the week and the hours of the day. More precise delivery times means your customers are most likely to engage with the communication and increase metrics such as open rates for SMS and email, as well as pick-up rates for voice calls. It also allows contact center managers to follow country and state-level voice dialing legislation. The following screenshot shows how you can configure quiet times using Amazon Pinpoint.

Amazon Connect outbound campaigns - quiet times

According to TCPA, call abandonment rate is the percentage of calls picked up by a live customer but not connected to a live agent within two seconds after the customer greeting. I found it interesting that in the UK, the time is measured from the start of your customer greetings, while in the US, it is measured from the end of the greeting. Amazon Connect outbound campaigns provides you with metrics, such as customerGreetingStart, customerGreetingStop, andconnectedToAgent for each outbound communication. Contact center managers can use these to compute the abandonment rate and dial up or down the outgoing communication channel accordingly.

Other metrics, configuration parameters, and AWS Lambda API integration allow contact center managers to consult a Do-Not-Call (DNC) registry or list scrubbing and verify your customer’s local time zone or bank holiday calendars, just to name a few.

Pricing and Availability
Amazon Connect outbound campaigns is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (London) AWS Regions. This allows you to start your outbound campaigns for customers in the USA, UK, Australia, and New Zealand.

As usual, pricing is based on your usage; you only pay for what you use with no upfront or minimum engagement. The key metrics we are using for pricing are the minutes of outbound calls. The pricing page has all the details.

And now, go build your contact centers.

— seb

Automate Amazon Connect Data Streaming using AWS CDK

Post Syndicated from Tarik Makota original https://aws.amazon.com/blogs/architecture/automate-amazon-connect-data-streaming-using-aws-cdk/

Many customers want to provision Amazon Web Services (AWS) cloud resources quickly and consistently with lifecycle management, by treating infrastructure as code (IaC). Commonly used services are AWS CloudFormation and HashiCorp Terraform. Currently, customers set up Amazon Connect data streaming manually, as the service is not available under CloudFormation resource types. Customers may want to extend it to retrieve real-time contact and agent data. Integration is done manually and can result in issues with IaC.

Amazon Connect contact trace records (CTRs) capture the events associated with a contact in the contact center. Amazon Connect agent event streams are Amazon Kinesis Data Streams that provide near real-time reporting of agent activity within the Amazon Connect instance. The events published to the stream include these contact control panel (CCP) events:

  • Agent login
  • Agent logout
  • Agent connects with a contact
  • Agent status change, such as to available to handle contacts, or on break, or at training.

In this blog post, we will show you how to automate Amazon Connect data streaming using AWS Cloud Development Kit (AWS CDK). AWS CDK is an open source software development framework to define your cloud application resources using familiar programming languages. We will create a custom CDK resource, which in turn uses Amazon Connect API. This can be used as a template to automate other parts of Amazon Connect, or for other AWS services that don’t expose its full functionality through CloudFormation.

Overview of Amazon Connect automation solution

Amazon Connect is an omnichannel cloud contact center that helps you provide superior customer service. We will stream Amazon Connect agent activity and contact trace records to Amazon Kinesis. We will assume that data will then be used by other services or third-party integrations for processing. Here are the high-level steps and AWS services that we are going use, see Figure 1:

  1. Amazon Connect: We will create an instance and enable data streaming
  2. Cloud Deployment Toolkit: We will create custom resource and orchestrate automation
  3. Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose: To stream data out of Connect
  4. AWS Identity and Access Management (IAM): To govern access and permissible actions across all AWS services
  5. Third-party tool or Amazon S3: Used as a destination of Connect data via Amazon Kinesis data
Figure 1. Connect data streaming automation workflow

Figure 1. Connect data streaming automation workflow

Walkthrough and deployment tasks

Sample code for this solution is provided in this GitHub repo. The code is packaged as a CDK application, so the solution can be deployed in minutes. The deployment tasks are as follows:

  • Deploy the CDK app
  • Update Amazon Connect instance settings
  • Import the demo flow and data

Custom Resources enables you to write custom logic in your CloudFormation deployment. You implement the creation, update, and deletion logic to define the custom resource deployment.

CDK implements the AWSCustomResource, which is an AWS Lambda backed custom resource that uses the AWS SDK to provision your resources. This means that the CDK stack deploys a provisioning Lambda. Upon deployment, it calls the AWS SDK API operations that you defined for the resource lifecycle (create, update, and delete).

Prerequisites

For this walkthrough, you need the following prerequisites:

Deploy and verify

1. Deploy the CDK application.

The resources required for this demo are packaged as a CDK app. Before proceeding, confirm you have command line interface (CLI) access to the AWS account where you would like to deploy your solution.

  • Open a terminal window and clone the GitHub repository in a directory of your choice:
    git clone [email protected]:aws-samples/connect-cdk-blog
  • Navigate to the cdk-app directory and follow the deployment instructions. The default Region is usually us-east-1. If you would like to deploy in another Region, you can run:
    export AWS_DEFAULT_REGION=eu-central-1

2. Create the CloudFormation stack by initiating the following commands.

source .env/bin/activate
pip install -r requirements.txt
cdk synth
cdk bootstrap
cdk deploy  --parametersinstanceId={YOUR-AMAZON-CONNECT-INSTANCE-ID}

--parameters ctrStreamName={CTRStream}

--parameters agentStreamName={AgentStream}

Note: By default, the stack will create contact trace records stream [ctrStreamName] as a Kinesis Data Stream. If you want to use an Amazon Kinesis Data Firehose delivery stream instead, you can modify this behavior by going to cdk.json and adding “ctr_stream_type”: “KINESIS_FIREHOSE” as a parameter under “context.”

Once the status of CloudFormation stack is updated to CREATE_COMPLETE, the following resources are created:

  • Kinesis Data Stream
  • IAM roles
  • Lambda

3. Verify the integration.

  • Kinesis Data Streams are added to the Amazon Connect instance
Figure 2. Screenshot of Amazon Connect with Data Streaming enabled

Figure 2. Screenshot of Amazon Connect with Data Streaming enabled

Cleaning up

You can remove all resources provisioned for the CDK app by running the following command under connect-app directory:

cdk destroy

This will not remove your Amazon Connect instance. You can remove it by navigating to the AWS Management Console -> Services -> Amazon Connect. Find your Connect instance and click Delete.

Conclusion

In this blog, we demonstrated how to maintain Amazon Connect as Infrastructure as Code (IaC). Using a custom resource of AWS CDK, we have shown how to automate setting Amazon Kinesis Data Streams to Data Streaming in Amazon Connect. The same approach can be extended to automate setting other Amazon Connect properties such as Amazon Lex, AWS Lambda, Amazon Polly, and Customer Profiles. This approach will help you to integrate Amazon Connect with your Workflow Management Application in a faster and consistent manner, and reduce manual configuration.

For more information, refer to Enable Data Streaming for your instance.

Enhance Your Contact Center Solution with Automated Voice Authentication and Visual IVR

Post Syndicated from Soonam Jose original https://aws.amazon.com/blogs/architecture/enhance-your-contact-center-solution-with-automated-voice-authentication-and-visual-ivr/

Recently, the Accenture AWS Business Group (AABG) assisted a customer in developing a secure and personalized Interactive Voice Response (IVR) contact center experience that receives and processes payments and responds to customer inquiries.

Our solution uses Amazon Connect at its core to help customers efficiently engage with customer service agents. To ensure transactions are completed securely and to prevent fraud, the architecture provides voice authentication using Amazon Connect Voice ID and a visual portal to submit payments. The visual IVR feature allows customers to easily provide the required information online while the IVR is on standby. The solution also provides agents the information they need to effectively and efficiently understand and resolve callers’ inquiries, which helps improve the quality of their service.

Overview of solution

Our IVR is designed using Contact Flows on Amazon Connect and uses the following services:

  • Amazon Lex provides the voice-based intent analysis. Intent analysis is the process of determining the underlying intention behind customer interactions.
  • Amazon Connect integrates with other AWS services using AWS Lambda.
  • Amazon DynamoDB stores customer data.
  • Amazon Pinpoint notifies customers via text and email.
  • AWS Amplify provides the customized agent dashboard and generates the visual IVR portal.

Figure 1 shows how this architecture routes customer calls:

  1. Callers dial the main line to interact with the IVR in Amazon Connect.
  2. Amazon Connect Voice ID sets up a voiceprint for first time callers or performs voice authentication for repeat callers for added security.
  3. Upon successful voice authentication, callers can proceed to IVR self-service functions, such as checking their account balance or making a payment. Amazon Lex handles the voice intent analysis.
  4. When callers make a payment request, they are given the option to be handed off securely to a visual IVR portal to process their payment.
  5. If a caller requests to be connected to an agent, the agent will be presented with the customer’s information and IVR interaction details on their agent dashboard.
Architecture diagram

Figure 1. Architecture diagram

Customer IVR experience

Figure 2 describes how callers navigate through the IVR:

  1. The IVR asks the caller the purpose of the call.
  2. The caller’s answer is sent for voice intent analysis. The IVR also attempts to authenticate the caller’s voice using Amazon Connect Voice ID. If authenticated, the caller is automatically routed to the correct flow based on the analyzed intent.
    • For the “Account Balance” flow, the caller is provided the account balance information.
    • For the “Make a Payment” flow, the caller can use the IVR or a visual IVR portal to process the payment. Upon payment completion, the caller is immediately notified their transaction has completed via SMS or email. Both flows allow the caller to be transferred to an agent. The caller also has the option to be called back when an agent becomes available or choose a specific date and time for the callback.
Customer IVR experience diagram

Figure 2. Customer IVR experience diagram

The intelligent self-service IVR solution includes the following features:

  • The IVR can redirect callers to a payment portal for scenarios like making a payment while the IVR remains on standby.
  • IVR transaction tracking helps agents understand the current status of the caller’s transaction and quickly determines the caller’s situation.
  • Callers have the option to receive a call as soon as the next agent becomes available or they can schedule a time that works for them to receive a callback.
  • IVR activity logging gives agents a detailed summary of the caller’s actions within the IVR.
  • Transaction confirmation which notifies callers of successful transactions via SMS or email.

Solution walkthrough

Amazon Connect Voice ID authenticates a caller’s voice as an added level of security. It requires 30 seconds to create the initial enrollment voiceprint and 10 seconds of a caller’s voice to authenticate. If there is not enough net speech to perform the voice authentication, the IVR asks the caller more questions, such as their first name and last name, until it has collected enough net speech.

The IVR falls back to dual tone multi-frequency (DTMF) input for the caller’s credentials in case the system cannot successfully authenticate. This can include information like the last four digits of their national identification number or postal code.

In contact flows, you will enable voice authentication by adding the “Set security behavior” contact block and specifying the authentication threshold, as shown in Figure 3.

Set security behavior contact block

Figure 3. Set security behavior contact block

Figure 4 shows the “Check security status” contact block, which determines if the user has been successfully authenticated or not. It also shows results that it may return if the caller is not successfully authenticated, including, “Not authenticated,” “Inconclusive,” “Not enrolled,” “Opted out,” and “Error.”

Check security status contact block

Figure 4. Check security status contact block

Providing a personalized experience for callers

To provide a personalized experience for callers, sample customer data is stored in a DynamoDB table. A Lambda function queries this table when callers call the contact center. The query returns information about the caller, such as their name, so the IVR can offer a customized greeting.

Transaction tracking

The table can also query if a customer previously called and attempted to make a payment but didn’t complete it successfully. This feature is called “transaction tracking.” Here’s how it works:

  • When the caller progresses through the “make a payment” flow, a field in the table is updated to reflect their transaction’s status.
  • If the payment is abandoned, the status in the table remains open, and the IVR prompts the caller to pick up where they left off the next time they call.
  • Once they have successfully completed their payment, we update the status in the table to “complete.”
  • When the IVR confirms that the caller’s payment has gone through, they will receive a confirmation via SMS and email. A Lambda function in the contact flow receives the caller’s phone number and email address. Then it distributes the confirmation messages via Amazon Pinpoint.

If a call is escalated to an agent, the “Check contact attributes” contact block in Figure 5 helps to check the caller’s intent and provide the agent with a customized whisper.

Agent whisper sample contact flow

Figure 5. Agent whisper sample contact flow

Making payments via the payment portal

To make a payment, an Amazon Lex bot presents the caller with the option to provide payment details over the phone or through a visual IVR portal.

If they choose to use the visual IVR portal (Figure 6), they can enter their payment details while maintaining an open phone connection with the contact center, in case they need additional assistance. Here’s how it works:

  • When callers select to use the payment portal, it prompts a Lambda function that generates a universally unique identifier (UUID) and provides the caller a unique PIN.
  • The UUID and PIN are stored in the DynamoDB table along with the caller’s information.
  • Another Lambda function generates a secure link using the UUID. It then uses Amazon Pinpoint to send the link to the caller over text message to their phone number on record. When they open the link, they are prompted to enter their unique PIN.
  • Then, the webpage makes an API call that validates the payment request by comparing the entered PIN to the PIN stored in the DynamoDB table.
  • Once validated, the caller can enter their payment information.
Visual IVR portal

Figure 6. Visual IVR portal

Figure 7 illustrates visual IVR portal contact flow:

  • Every 10 seconds, a Lambda function checks the caller’s payment status. It provides the caller the option to escalate to an agent if they have questions.
  • If the caller does not fill out all the information when they hit “Submit Payment,” an IVR prompt will ask them to provide all payment details before proceeding.
  • The IVR phone call stays active until the user’s payment status is updated to “complete” in the DynamoDB table. This generates an IVR prompt stating that their payment was successful.
Visual IVR portal sample contact flow

Figure 7. Visual IVR portal sample contact flow

Generating a chat transcript for agents

When the customer’s call is escalated to an agent, the agent receives a chat transcript. Here’s how it works:

  • After the caller’s intent is captured at the start of the call, the IVR logs activity using a “Set contact attribute” contact block, which prompts the $.Lex.SessionAttributes.transcript.
  • This transcript is used in a Lambda function to build a chat interface.
  • This transcript is shown on the agent’s dashboard, along with the Contact Control Panel (CCP) and a few key pieces of caller information.
IVR transcript

Figure 8. IVR transcript

The agent’s customized dashboard and the visual IVR portal are deployed and hosted on Amplify. This allows us to seamlessly connect to our code repository and automate deployments after changes are committed. It removed the need to configure Amazon Simple Storage Service (Amazon S3) buckets, an Amazon CloudFront distribution, and Amazon Route 53 DNS to host our front-end components.

This solution also offers callers the ability to opt-in for a callback or to schedule a callback. A “Check queue status” contact block checks the current time in queue, and if it reaches a certain threshold, the IVR will offer a callback. The caller has the option to receive a call as soon as the next agent becomes available or to schedule a time to receive a callback. A Lex bot gathers the date and time slots, which are then passed to a Lambda function that will validate the proposed callback option.

Once confirmed, the scheduled callback is placed into a DynamoDB table along with the caller’s phone number. Another Lambda function scans the table every 5 minutes to see if there are any callbacks scheduled within that 5-minute time period. You’ll add an Amazon EventBridge prompt to the Lambda function that specifies a schedule expression like cron(0/5 8-17 ? * MON-FRI *), which means the Lambda function will execute every 5 minutes, Monday through Friday from 8:00 AM to 4:55 PM.

Conclusion

This solution helps you increase customer satisfaction by making it easier for callers to complete transactions over the phone. The visual IVR provides added web-based support experience to submit payments. It also improves the quality of service of your customer service agents by making relevant information available to agents during the call.

This solution also allows you to scale out the resources to handle increasing demand. Custom features can easily be added using serverless technology, such as Lambda functions or other cloud-native services on AWS.

Ready to get started? The AABG helps customers accelerate their pace of digital innovation and realize incremental business value from cloud adoption and transformation. Connect with our team at [email protected] to learn how to use machine learning in your products and services.

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Machine Learning-Powered Amazon Connect, Now With Call Summarization

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/machine-learning-powered-amazon-connect-now-with-call-summarization/

At AWS our mission is to make machine learning (ML) accessible to data scientists, developers, and business users. To help businesses easily leverage the power of ML, we create purpose-built solutions that embed ML and deep learning technologies directly into a business process to address real customer needs, rather than leaving companies to sort it out on their own.

One place where we have seen ML have an impact is within the contact center—the place you receive and respond to customer inquiries and issues. Because of the growing role of customer experience (CX) and the increase in contact less commerce via phone or email, contact centers are essentials to maintaining the human connections that businesses depend on. However, analog or outdated methods make it difficult to address every customer need in an effective way that delivers timely resolutions, delivers great experiences, and fosters customer loyalty.

Embedding AWS ML technologies into a cloud contact center solution helps decrease the friction of calls, chats, and other engagements. It also makes it possible to automate outdated processes.

Amazon Connect is an easy-to-use, cloud-based, ML-powered contact center service that helps companies of any size deliver superior customer service at a lower cost.

Let me take three examples with Voice ID, Wisdom, and Contact Lens.

Amazon Connect Voice ID
ML capabilities might help streamline customer experience for authentication. Instead of asking customers to repeat their email address and their mother’s maiden name several times, ML-powered voice identification can establish a digital voice print associated with each customer’s unique voice. Then, it can recognize it at the beginning of each subsequent call. Voice identification provides a confidence score that may be used to automate authentication workflows.

Amazon Connect Wisdom
ML might also help search the vast documentation and knowledge base to find the most relevant answers to the questions raised by the customer. ML helps resolve customer issues faster and better.

Contact Lens for Amazon Connect
ML technologies also shine at analyzing the tone and content of a conversation, capturing customer sentiment in the moment, and learning from it. ML can help transcribe calls, track customer sentiment, detect common issues and customer trends, or even pinpoint discrepancies.

At just about the same time last year, I announced the addition of real-time capabilities for Contact Lens. This lets supervisors identify when to assist an agent on live calls so that they can provide guidance via chat or have the agent transfer the call. Last September, we added support for eight new languages, ending up with a total of 21 languages for post-call analytics and 12 languages for both post-call and real-time analytics.

Contact Lens Adds Call Summarization
But we didn’t stop there. Today, I am pleased to announce the addition of a new capability that helps you improve customer experience and agent and supervisor productivity by automatically summarizing the important aspects of each customer call.

You told us that keeping notes of customer conversations is time consuming, especially, for agents that must take notes during the call and import them manually in your CRM tool afterward. In the end, this is more time for us, the customers, waiting in queue for an agent to become available. Likewise, using automatically generated call transcripts doesn’t save time for supervisors. It is time consuming for supervisors to read these full call transcripts to understand what happened during customer conversations.

How it Works
Starting today, Contact Lens has added a summary of the key moments in a conversation. It is enabled by default, and there is no additional configuration step. You may toggle the Show transcript summary button to show or hide the summary when you don’t need it.

Contac Lens - Show Transcript Summary - Toggle button

Once a call is analyzed, the summary is available on the contact detail page.

Contact Lens identifies and summarizes the sections corresponding to Issue (e.g., lost package), Outcome (e.g., customer refund), and Action item (e.g., send a follow-up mail confirming the refund was processed). A manager can quickly see where there’s an action to send a customer a follow-up email and take action to ensure it happens.

Contact Lens Call Summary Example

The call summary is also available in JSON format. Contact Lens uploads these in the S3 bucket of your choice. Having access to the JSON file lets you import the summaries programmatically in your CRM or other tools.

... redacted for brevity ...

"IssuesDetected": [
{
   "CharacterOffsets": {
      "BeginOffsetChar": 31,
      "EndOffsetChar": 73
   },
   "Text": "I would like to cancel my subscription"
}
]
...
"ActionItemsDetected": [
 {
   "CharacterOffsets": {
      "BeginOffsetChar": 32,
      "EndOffsetChar": 116
   },
   "Text": "I will send you an email with details"
 }
 ]

Availability and Pricing
Call summarization by Contact Lens is available in all AWS Regions where Contact Lens is available today. We support post-call analytics in the US West (Oregon), US East (N. Virginia), Canada (Central), Europe (London), Europe (Frankfurt), Asia Pacific (Singapore), Asia Pacific (Seoul), Asia Pacific (Tokyo), and Asia Pacific (Sydney) regions. We support real-time analytics in the US West (Oregon), US East (N. Virginia), Canada (Central), Europe (London), Europe (Frankfurt), Asia Pacific (Seoul), Asia Pacific (Tokyo), and Asia Pacific (Sydney) regions.

Call summary comes at no additional cost on top of the usual charges for Contact Lens. This is why we choose to enable it by default. Contact Lens is charged $0.015 per minute of voice conversation analyzed. Most of our Contact Lens customers analyze millions of conversation minutes per month. The price is $0.0125 per minute when you analyze more than 5 millions minutes per month.

If you do not have Contact Lens enabled on your call center, go ahead and start using it today.

— seb

New for Amazon Connect: Voice ID, Wisdom, and Outbound Communications

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/three-new-capabilities-for-amazon-connect/

During the AWS re:Invent conference last year, I wrote about new capabilities added to Amazon Connect. Today, I am happy to announce the general availability of two of these capabilities, Voice ID and Wisdom, and the launch of a new one. High-volume outbound communications allows, as the name implies, the initiation and management of outbound communications over voice, SMS, or email.

Amazon Connect is an easy-to-use omnichannel cloud contact center that helps you provide customer service at a lower cost. In just a few clicks, you can set up and make changes to your contact center, so agents can begin helping customers right away.

Amazon Connect Wisdom
Wisdom reduces the time agents spend searching for answers. Today, when agents require access to information to help a customer, they lose time trying to navigate different data sources in siloes: FAQ, files, wiki pages, customer call history, knowledge bases, etc.

When using Wisdom, agents simply enter a question or phrase in their agent desktop application, such as “what is the pet policy in hotel rooms”, and Wisdom searches connected repositories and returns the most relevant information and best answer to handle the customer issue.

Amazon Connect Wisdom launch screenshot

Wisdom also uses real-time call transcripts from Contact Lens for Amazon Connect to automatically detect customer issues during calls and to recommend relevant content stored across connected knowledge repositories, without requiring agents to even enter a question.

Wisdom connects to knowledge repositories with built-in connectors for third-party applications including Salesforce and ServiceNow. You can also ingest content from other knowledge stores using the Wisdom ingestion APIs.

Amazon Connect Wisom configure connectors

Amazon Connect Voice ID
How many times have you been through an authentication procedure when calling a contact center? Voice ID simplifies this to make voice interactions faster and more secure. It uses machine learning to provide real-time caller authentication based on the caller’s voice.

To effectively recognize me as “Sébastien”, Voice ID must learn how I talk. This is the enrollment phase. It only requires 30 seconds of voice recording to enroll a caller.

When I call the same contact center again, Voice ID compares the sound of my voice with the one enrolled earlier. This is the verification phase. It only requires between 5 and 10 seconds of my voice to authenticate me. The verification phase generates a confidence score and a status displayed in the agent desktop app.

Amazon Connect Voice ID agent desktop view

Contact Center administrators can use this result to configure different flows depending on the verification outcome. The routing is configured with a simple configuration panel such as this one:Amazon Connect Voice ID Configuration panelTo meet with personal data protection laws, contact center agents capture my consent to use Voice ID.

High-Volume Outbound Communications
Typical contact centers are designed to receive customer calls. However, there is a growing set of use cases where contact centers send outbound communications as well. For example, to call customers back,to inform them about the progress of a case, to confirm an appointment, to renew a subscription, or for telemarketing, just to name a few.

The majority of these outbound communications are phone calls. When doing so, traditional contact center agents dial the number provided by a customer management system and wait for someone to answer. Typically, only 10% of the calls are answered. This process is highly inefficient.

In the Amazon Connect administration console, I select High volume outbound communication, then I select Create Campaign.

Connect High Volume Outbout Communication - Create Campaign

I then configure the details. I give the campaign a name, then I select one of my outbound contact flow and a contact queue associated with an outbound phone number.

Connect High Volume Outbout Communication - Campaign details

The predictive dialer makes more calls than available agents. It uses metrics such as campaign performance, expected pick-up rates, and the number of available agents to adjust the number of calls. When a call is answered, it detects when a human is on the line (vs. an automatic machine, a fax line, etc.). Only calls answered by humans are routed to an available agent. The Amazon Connect agent application shows the call script that was specified during setup, along with relevant customer information.

The progressive dialer is more conservative, it uses a 1:1 ratio between calls and available agents.

Amazon Connect not only adds high-volume outbound communication capabilities for voice, but also for SMS, and email. Amazon Connect comes with pre-built connectors for importing customer contact lists from external systems, such as Salesforce, Zendesk, Marketo, and Amazon Pinpoint. No coding required.

Contact center managers have access to real-time metrics such as contact volume, abandonment rates, average connection times, and minimum ring times to optimize agent efficiency. These metrics help to understand the status of their campaigns and ensure compliance with applicable regulations, such as maximum call abandonment rates. Contact center managers use historical reports of these metrics to understand the effectiveness of all their communications campaigns over time.

To ensure a fair usage of the high-volume outbound communication capability, you must apply for production access to use the predictive dialer as well as SMS and email. You may submit a service request detailing your use cases and business context, which will be used to validate your legitimacy as a sender. Once access is granted, Amazon Connect continuously monitors your usage and the team might revoke access when fraud is suspected.

If you want to try it out yourself, you may apply to the preview by filling out this form.

Pricing and Availability
As usual, there are no upfront costs or minimum usage fees. You pay only what you use: a price per minute of outbound calls and per email or SMS message. The details are up-to-date on the Amazon Connect pricing page.

Regional availability slightly differ for each of these three new capabilities, here is a the list of AWS Regions where they are available:

  • Wisdom: US West (Oregon), US East (N. Virginia), Europe (London), Europe (Frankfurt), Asia Pacific (Tokyo), and Asia Pacific (Sydney).
  • Voice ID: US West (Oregon), US East (N. Virginia), Europe (London), Europe (Frankfurt), Asia Pacific (Tokyo), Asia Pacific (Singapore), and Asia Pacific (Sydney)
  • High volume outbound communication (preview): US East (N. Virginia), Europe (London), and US West (Oregon). More Regions will be added when it will be generally available.

As usual, let us know what you think about these new capabilities and how you use them. Go build your own contact center in the cloud today.

— seb

Disaster Recovery (DR) for a Third-party Interactive Voice Response on AWS

Post Syndicated from Priyanka Kulkarni original https://aws.amazon.com/blogs/architecture/disaster-recovery-dr-for-a-third-party-interactive-voice-response-on-aws/

Voice calling systems are prevalent and necessary to many businesses today. They are usually designed to provide a 24×7 helpline support across multiple domains and use cases. Reliability and availability of such systems are important for a good customer experience. The thoughtful design of a cost-optimized solution will allow your business to sustain the system into the future.

We address a scenario in which you are mandated to host the workload on a corporate data center (DC), and configure the backup site on Amazon Web Services (AWS). Since the primary objective of a backup site is disaster recovery (DR) management, this site is often referred to as a DR site.

Disaster Recovery on AWS

DR strategy defines the recovery objectives for downtime and data loss. The workload has a recovery time objective (RTO) and a recovery point objective (RPO). RTO is the maximum acceptable delay between the interruption of service and the restoration of service. RPO is the maximum acceptable amount of time since the last data recovery point. AWS defines four DR strategies in increasing order of complexity, and decreasing order of RTO and RPO. These are backup and restore, active/passive (pilot light or warm standby), or active/active.

Figure 1. Disaster recovery (DR) options

Figure 1. Disaster recovery (DR) options

In our use case, the DR site on AWS must serve the user traffic with RPO and RTO in seconds. Warm standby is the optimal choice in this case. It is a scaled-down version of a fully functional environment, and is always running in the cloud.

Amazon Connect is an omnichannel cloud contact center that helps you provide great customer service at a lower cost. But in some situations, Amazon Connect may not be available. In other cases, the customer may want to use their home developed or third-party contact center application. Our solution is designed to help in both these scenarios.

This architecture enables customers facing challenges of cost overhead with redundant Session Initiation Protocol (SIP) trunks for the DC and DR sites. It allows you to optimize your spend and yet retain a reliable workflow.

SIP trunk communication on AWS

Let’s see how the SIP trunk termination on the AWS network handles the failover scenario of a third-party IVR application installed on Amazon EC2 at the DR site.

There will be two connections made from the AWS Direct Connect location (DX). The first will be for a point-to-point connectivity between the corporate DC and the AWS DR site. The second connection will be originating from the multiplexer (MUX) of the telecom provider who is providing you the SIP trunk.

The telecom provider will lay the SIP trunk from its MUX to the customer router at the DX location. At this point, the mode of communication becomes IP-based. The telecom provider will send the call to the IP address attached to the Network Load Balancer (NLB) in Amazon Virtual Private Cloud (VPC).

Figure 2. Communication circuitry at telecom side

Figure 2. Communication circuitry at telecom side

AWS Network Load Balancers can now distribute traffic to AWS resources using their IP addresses and instance IDs as targets. You can also distribute the traffic with on-premises resources over AWS Direct Connect. Load balancing across AWS and on-premises resources using the same load balancer streamlines migrate-to-cloud, burst-to-cloud, or failover-to-cloud.

In the backup site, the NLB will point to the Session Border Controller (SBC). This is a special-purpose device that protects and regulates IP communications flows. You can bring your own SBC, or you can use an SBC offered in the AWS Marketplace.

Best practices for high availability of IVR solution on AWS

  • Configure the multiple Availability Zone (Multi-AZ) SBC setup
  • Make sure that the telecom provider for the SIP trunk is different from the internet service provider (ISP). This is for last mile connectivity for the DC from Direct Connect
  • Consider redundancy for Direct Connect by using a Site-to-Site VPN tunnel
Figure 3. Solution architecture of DR on AWS for a third-party IVR solution

Figure 3. Solution architecture of DR on AWS for a third-party IVR solution

Communication flow for an IVR solution deployed on a corporate DC and its DR on AWS

  1. The callers are received on the telecom providers SIP line, which terminates on the AWS Direct Connect location.
  2. At the DX location, you will configure a route in the AWS router to send the traffic to the IP address of the NLB. The NLB should be configured to perform health checks on the virtual machine in your on-premises DC. Based on these health checks, the NLB will do the routing and the failover.
  3. In a live scenario with successful health checks at the DC, the NLB will forward the call to the IP of the on-premises virtual machine. This is where the IVR application will be installed.
  4. The communication between the NLB in Amazon VPC and the virtual machine in DC, will happen over Direct Connect.
  5. In a DR scenario, the NLB will failover the communication to SBCs in Amazon VPC.

Conclusion

This solution is useful when a third-party IVR system is deployed in a corporate data center, and the passive DR site is hosted on AWS. Cost optimization on telecom components is an important aspect of this design. AWS Direct Connect provides dedicated connectivity to the AWS environment, from 50 Mbps up to 10 Gbps. This gives you managed and controlled latency. It also provides provisioned bandwidth, so your workload can connect to AWS resources in a reliable, scalable, and cost-effective way.

The solution in this blog explains the end-to-end flow of communication, from the user to the IVR agents. It also provides insights into managing failover and failback between DR and the DR site.

Further Reading:

How AWS is Supporting Nonprofits, Governments, and Communities Impacted by Hurricane Ida

Post Syndicated from AWS News Blog Team original https://aws.amazon.com/blogs/aws/how-aws-is-supporting-nonprofits-governments-and-communities-impacted-by-hurricane-ida/

Image of a man in a blue shirt running cable through a wall.

AWS Disaster Response Team volunteer Paul Fries runs cable to provide connectivity in a police station that has been converted into a supply distribution center and housing for National Guard troops and FEMA.

During the crisis that has resulted in the aftermath of Hurricane Ida, the AWS Disaster Response Team is providing personnel and resources to support our customers, including the Information Technology Disaster Resource Center (ITDRC), St. Bernard Parish government, and Crisis Cleanup.

In 2018, Amazon Web Services launched the AWS Disaster Response Program to support governments and nonprofits actively responding to natural disasters. Guided by the belief that technology has the power to solve the world’s most pressing issues, the AWS Disaster Response Team has been working around the clock to provide pro bono assistance to public sector organizations responding to Hurricane Ida’s widespread damage.

The team brings in technical expertise in the form of solutions architects, machine learning practitioners, experts in edge computing, and others across AWS. This allows AWS to support organizations responding to disasters and help them overcome the unique challenges they face.

Establishing Connectivity

When Hurricane Ida hit the United States on August 29th, 2021, it caused power outages and major disruptions to infrastructures for water, power, cellular, and commercial communications. In the South, many were left homeless or without electricity, gas, or water by Ida’s winds and flooding. In New York and New Jersey, hundreds of thousands of people lost power. The AWS Disaster Response team activated its response protocol, including technical volunteer support on the ground and financial support to disaster relief nonprofit ITDRC for its efforts to establish local internet connectivity and cell phone charging stations. This critical support – including almost 100 sites in New Orleans, Houma, and other heavily impacted areas of Louisiana – is enabling public safety agencies, responding relief organizations, social services, and community members to communicate and coordinate as they respond to and recover from Ida.

Increasing Capacity

AWS volunteers supported disaster relief nonprofit Crisis Cleanup by providing technical AWS service architecture guidance, enabling them to increase their capacity to provide call-backs to community members requesting assistance with tasks like cutting fallen trees and tarping roofs that were damaged by the storm. AWS volunteers are also helping Crisis Cleanup by conducting call-backs, bolstering their volunteer base to reach a greater number of people more quickly than otherwise would have been possible.

Building Resiliency

To support the St. Bernard Parish government, located just outside of New Orleans, AWS assisted with the deployment of Amazon WorkMail to facilitate more resilient communication across teams when their email server was knocked offline when the power went out. Using Amazon WorkMail helped enable government personnel to more effectively communicate by utilizing the cloud instead of traditional infrastructures.

The AWS Disaster Response Team continues to field requests for support following Hurricane Ida, and is also working with other organizations to support their resilience planning and disaster preparedness efforts.

Learn More

Check out the AWS Disaster Response page for more information. To donate cash and supplies to organizations such as Feeding America and Save the Children, visit the Amazon page on Hurricane Ida or just say, “Alexa, I want to donate to Hurricane Ida relief.”

Field Notes: Build Dynamic IVR Menus with Amazon Connect and AWS Lambda

Post Syndicated from Marius Cealera original https://aws.amazon.com/blogs/architecture/field-notes-build-dynamic-ivr-menus-with-amazon-connect-and-aws-lambda/

This post was co-written by Marius Cealera, Senior Partner Solutions Architect at AWS, and Zdenko Estok, Cloud Architect and DevOps Engineer at Accenture. 

Modern interactive voice response (IVR) systems help customers find answers to their questions through a series of menus, usually relying on the customer to filter and select the right options. Adding more options in these IVR menus and hoping to increase the rate of self-serviced calls can be tempting, but it can also overwhelm customers and lead them to ‘zeroing out’. That is, they select to be transferred to a human agent, defeating the purpose of a self-service solution.

This post provides a technical overview of one of Accenture’s Advanced Customer Engagement (ACE+) solutions, explaining how to build a dynamic IVR menu in Amazon Connect, in combination with AWS Lambda and Amazon DynamoDB. The solution can help the zeroing out problem by providing each customer with a personalized list of menu options. Solutions architects, developers, and contact center administrators will learn how to use Lambda and DynamoDB to build Amazon Connect flows where menu options are customized for every known customer. We have provided code examples to deploy a similar solution.

Overview of solution

“Imagine a situation where a customer navigating a call center IVR needs to choose from many menu options – for example, an insurance company providing a self-service line for customers to check their policies. For dozens of insurance policy variations, the IVR would need a long and complex menu. Chances are that after the third or fourth menu choice the customers will be confused, irritated or may even forget what they are looking for,” says Zdenko Estok, Cloud Architect and Amazon Connect Specialist at Accenture.

“Even splitting the menu in submenus does not completely solve the problem. It is a step in the right direction as it can reduce the total time spent listening to menu options, but it has the potential to grow into a huge tree of choices.”

Figure 1. A static one-layer menu on the left, a three-layer menu on the right

Figure 1. A static one-layer menu on the left, a three-layer menu on the right

One way to solve this issue is by presenting only relevant menu options to customers. This approach significantly reduces the time spent in the IVR menu, leading to a better customer experience. This also minimizes the chance the customer will require transfer to a human agent.

Figure 2. Selectively changing the IVR menu structure based on customer profile

Figure 2. Selectively changing the IVR menu structure based on customer profile

For use cases with a limited number of menu options, the solution can be achieved directly in the Amazon Connect IVR designer through the use of the Check Contact Attributes block. However, this approach can lead to complex and hard to maintain flows for situations where dozens of menu variations are possible. A more scalable solution is to store customer information and menu options in DynamoDB and build the menu dynamically by using a series of Lambda functions (Figure 3).

Consider a customer with the following information stored in a database: phone number, name, and active insurance policies. A dynamic menu implementation will authenticate the user based on the phone number, retrieve the policy information from the database, and build the menu options.

The first Lambda function retrieves the customer’s active policies and builds the personalized greeting and menu selection prompt. The second Lambda function maps the customer’s menu selection to the correct menu path. This is required since the menu is dynamic and the items and ordering are different for different customers. This approach also allows administrators to add or change insurance types and their details, directly in the database, without the need to update the IVR structure. This can be useful when maintaining IVR flows for dozens of products or services.

Figure 3 - IVR flow leveraging dynamically generated menu options.

Figure 3 – IVR flow leveraging dynamically generated menu options.

Walkthrough

Sample code for this solution is provided in this GitHub repo. The code is packaged as a CDK application, allowing the solution to be deployed in minutes. The deployment tasks are as follows:

  1. Deploy the CDK app.
  2. Update Amazon Connect instance settings.
  3. Import the demo flow and data.

Prerequisites

For this walkthrough, you need the following prerequisites:

  • An AWS account.
  • AWS CLI access to the AWS account where you would like to deploy your solution.
  • An Amazon Connect instance. If you do not have an Amazon Connect instance, you can deploy one and claim a phone number with Set up your Amazon Connect instance.

Deploy the CDK application

The resources required for this demo are packaged as a CDK app. Before proceeding, confirm you have CLI access to the AWS account where you would like to deploy your solution.

  1. Open a terminal window and clone the GitHub repository in a directory of your choice:

git clone [email protected]:aws-samples/amazon-connect-dynamic-ivr-menus.git

Navigate to the cdk-app directory and follow the deployment instructions. The default region is usually us-east-1. If you would like to deploy in another Region, you can run:

export AWS_DEFAULT_REGION=eu-central-1

Update Amazon Connect instance settings

You need to update your Amazon Connect instance settings to implement the Lambda functions created by the CDK app.

  1. Log into the AWS console.
  2. Navigate to Services > Amazon Connect. Select your Amazon Connect instance.
  3. Select Contact Flows.
  4. Scroll down to the Lambda section and add getCustomerDetails* and selectionFulfi lment* functions. If the Lambda functions are not listed, return to the Deploy the CDK application section and verify there are no deployment errors.
  5. Select +Add Lambda function.

Import the demo flow

  1. Download the DemoMenu Amazon Connect flow from the flow_archive section of the sample code repository.
  2. Log in to the Amazon Connect console. You can find the Amazon Connect access url for your instance in the AWS Console, under Services > Amazon Connect > (Your Instance Name). The access url will have the following format: https://<your_instance_name>.awsapps.com/connect/login
  3.  Create a new contact flow by selecting ‘Contact Flows’ from the left side menu and then select Create New Contact Flow.
  4.  Select ‘Import Flow(beta)’ from the upper right corner menu and select the DemoMenu file downloaded at step 1.
  5.  Click on the first ‘Invoke Lambda’ block, and verify the getCustomerDetails* Lambda is selected.
  6.  Select the second Invoke Lambda block, and verify the selectionFulfilment*’Lambda is selected.
  7.  Select Publish.
  8.  Associate the new flow with your claimed phone number (phone numbers are listed in the left side menu).

Update the demo data and test

  1. For the demo to work and recognize your phone number, you will need to enter your phone number into the demo customers table.
  2. Navigate to the AWS console and select DynamoDB.
  3. From the left hand side menu select Tables, open the CdkAppStack-policiesDb*table, and navigate to the Items tab. If the table is empty, verify you started the populateDBLamba, as mentioned in the CDK deployment instructions.
  4. Select one of the customers in the table, then select Actions > Duplicate. In the new item, enter your phone number (in international format).
  5. Select Save.
  6. Dial your claimed Connect number. You should hear the menu options based on your database table entry.

Clean up

You can remove all resources provisioned for the CDK app by navigating to the cdk-app directory and running the following command:

cdk destroy

This will not remove your Amazon Connect instance. You can remove it by navigating to the AWS console > Services > Amazon Connect. Find your Connect instance and select Remove.

Conclusion

In this post we showed you how a dynamic IVR menu can be implemented in Amazon Connect. Using a dynamic menu can significantly reduce call durations by helping customers reach relevant content faster in the IVR system, which often leads to improved customer satisfaction. Furthermore, this approach to building IVR menus provides call center administrators with a way to manage menus with dozens or hundreds of branches directly in a backend database, as well as add or update menu options.

Field Notes provides hands-on technical guidance from AWS Solutions Architects, consultants, and technical account managers, based on their experiences in the field solving real-world business problems for customers.

Accelerating Innovation with the Accenture AWS Business Group (AABG)

By working with the Accenture AWS Business Group (AABG), you can learn from the resources, technical expertise, and industry knowledge of two leading innovators, helping you accelerate the pace of innovation to deliver disruptive products and services. The AABG helps customers ideate and innovate cloud solutions with customers through rapid prototype development.

Connect with our team at [email protected]zon.com to learn how to use machine learning in your products and services.

 

Zdenko Estok

Zdenko Estok

Zdenko Estok works as a Cloud Architect and DevOps engineer at Accenture. He works with AABG to develop and implement innovative cloud solutions, and specializes in Infrastructure as Code and Cloud Security. Zdenko likes to bike to the office and enjoys pleasant walks in nature.

AWS Contact Center Day – July 2021

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/aws-contact-center-day-july-2021/

AWS Contact Center Days

Earlier this week, I ordered from Amazon.fr a box of four toothpaste tubes, but only one was in the box. I called Amazon’s customer center. The agent immediately found my order without me having to share the long order number. She issued a refund and told me I even can keep the one tube I received, no return was needed.  As a customer, I can’t ask for better customer service.

Amazon strives to be the earth’s most customer-centric company, not only because it is the right thing to do for customers, but because over the long term, it’s good for the business. According to a Salesforce study, 80% of customers believe the experience a company provides is as important as its product or services. Over 90% of customers believe a positive customer service experience makes them more likely to make another purchase.

Just like me, you might have been delighted by Amazon customer service already. We know that you want fast, convenient support and it’s what makes you loyal.

This is why we created the AWS Contact Center Day conference. To learn from industry experts how to create your contact center of the future in a free on-demand video conference.

Amazon’s Contact Centers
Amazon’s contact centers are critical to our mission to be focused on customer experience. Our contact centers have more than 100,000 customer-service associates in 32 countries who support millions of customers in dozens of languages. Given the scale and our strict requirements for security, resiliency, flexibility, agility, or automation, we couldn’t purchase an off-the-shelf solution. We decided to build our own.

Everything that Amazon learned from our customer service organization, while looking to the future, has helped us bring to market Amazon Connect, an easy-to-use omnichannel cloud contact center that helps businesses provide superior customer service at a lower cost. As the notion of the contact center has evolved, so have the expectations of customers. The contact center of the future isn’t a collection of disparate point solutions for taking a call or a chat, and it isn’t just an application that consolidates those technologies. It’s a platform that makes it easy to integrate with your enterprise applications or system of record. The contact center of the future makes it easy to use customer data in real time to personalize and contextualize all customer experiences.

Contact Centers Best Practices
To further support business looking to improve their contact centers, Amazon designed our first contact center focused event, AWS Contact Contact Center Day, a way to share best practices, customer experience, and contact center technology, and to learn how to use Amazon Connect to accelerate the modernization of your contact centers.

The one-day conference took place on July 13, 2021 and brought together some of the most influential people invested in the future of contact centers including: Becky Ploeger, Global Head of Hilton Reservations and Customer Care, and member of the Customer Contact Week advisory board; Matt Dixon, Chief Research and Innovation Officer at Tethr, and author of multiple bestsellers, including The Challenger Sale; Customer service expert; author Shep Hyken, author of I’ll Be Back: How to Get Customers to Come Back Again and Again; Brian Solis, Global Innovation Evangelist, Salesforce, and best-selling author; and Mark Honeycutt, Director of Consumer Operations, Amazon.

At Amazon, we have gone through years of trial and error to get to where our customer experience stands today. This is why we wanted to share our experiences with you so that you can learn from our progress:

  • Customers want super-human service. You can now automate routine customer experience and agent tasks. When I call my airline for a rebooking after a delayed flight, I expect to be greeted by name. I expect the system to know my flight was delayed and to offer rebooking suggestions. This can happen automatically today, without involving a customer agent. These automatic chatbot systems are personalized per customer. They are dynamic because they answer customer questions before they ask, and they are natural because they are based on voice interactions, like conversations between humans.
  • Customers expect personalized engagement. Amazon Connect allows for fast and secure interactions with real-time voice biometric authentication. There is no need to go through a lengthy authentication questionnaire anymore. After the customer is authenticated, the customer service agent has a 360-degree view of the customer’s profile, integrating and displaying data from across the enterprise and using machine learning to provide the right information at the right moment.
  • Contact centers must evolve quickly to answer changing needs. Contact center interactions must take action based on real-time data or customer sentiment. Leaders want to experiment, learn, and improve using customer analytics and data.

Learn more
If you’re interested in learning more about contact center excellence, the entire Contact Center Day conference is now available on demand.

Check out the full agenda and watch a session now or learn more about Amazon Connect.

I am looking forward my next delightful customer experience using your contact centers.

— seb

Integrating Amazon Connect and Amazon Lex with Third-party Systems

Post Syndicated from Steven Warwick original https://aws.amazon.com/blogs/architecture/integrating-amazon-connect-and-amazon-lex-with-third-party-systems/

AWS customers who provide software solutions that integrate with AWS often require design patterns that offer some flexibility. They must build, support, and expand products and solutions to meet their end user business requirements. These design patterns must use the underlying services and infrastructure through API operations. As we will show, third-party solutions can integrate with Amazon Connect to initiate customer-specific workflows. You don’t need specific utterances when using Amazon Lex to convert speech to text.

Introduction to Amazon Connect workflows

Platform as a service (PaaS) systems are built to handle a variety of use cases with various inputs. These inputs are provided by upstream systems and sometimes result in complex integrations. For example, when creating a call center management solution, these workflows may require opaque transcription data to be sent to downstream third-party system. This pattern allows the caller to interact with a third-party system with an Amazon Connect flow. The solution allows for communication to occur multiple times. Opaque transcription data is transferred between the Amazon Connect contact flow, through Amazon Lex, and then to the third-party system. The third-party system can modify and update workflows without affecting the Amazon Connect or Amazon Lex systems.

API workflow use case

AnyCompany Tech (our hypothetical company) is a PaaS company that allows other companies to quickly build workflows with their tools. It provides the ability to take customer calls with a request-response style interaction. AnyCompany built an API into the Amazon Connect flow to allow their end users to return various response types. Examples of the response types are “disconnect,” “speak,” and “failed.”

Using Amazon Connect, AnyCompany allows their end users to build complex workflows using a basic question-response API. Each customer of AnyCompany can build workflows that respond to a voice input. It is processed via the high-quality speech recognition and natural language understanding capabilities of Amazon Lex. The caller is prompted by “What is your question?” Amazon Lex processes the audio input then invokes a Lambda function that connects to AnyCompany Tech. They in turn initiate their customer’s unique workflow. The customer’s workflow may change over time without requiring any further effort from AnyCompany Tech.

Questions graph database use case

AnyCompany Storage is a company that has a graph database that stores documents and information correlating the business to its inventory, sales, marketing, and employees. Accessing this database will be done via a question-response API. For example, such questions might be: “What are our third quarter earnings?”, “Do we have product X in stock?”, or “When was Jane Doe hired?” The company wants the ability to have their employees call in and after proper authentication, ask any question of the system and receive a response. Using the architecture in Figure 1, the company can link their Amazon Connect implementation up to this API. The output from Amazon Lex is passed into the API, a response is received, and it is then passed to Amazon Connect.

Amazon Connect third-party system architecture

Figure 1. End-customer call flow

Figure 1. End-customer call flow

  1. User calls Amazon Connect using the telephone number for the connect instance.
  2. Amazon Connect receives the incoming call and starts an Amazon Connect contact flow. This Amazon Connect flow captures the caller’s utterance and forwards it to Amazon Lex.
  3. Amazon Lex starts the requested bot. The Amazon Lex bot translates the caller’s utterance into text and sends it to AWS Lambda via an event.
  4. AWS Lambda accepts the incoming data, transforms or enhances it as needed, and calls out to the external API via some transport
  5. The external API processes the content sent to it from AWS Lambda.
  6. The external API returns a response back to AWS Lambda.
  7. AWS Lambda accepts the response from the external API, then forwards this response to Amazon Lex.
  8. Amazon Lex returns the response content to Amazon Connect.
  9. Amazon Connect processes the response.

Solution components

Amazon Connect

Amazon Connect allows customer calls to get information from the third-party system. An Amazon Connect contact flow is required to get the callers input, which is then sent to Amazon Lex. The Amazon Lex bot must be granted permission to interact with an Amazon Connect contact flow. As shown in Figure 2, the Get customer input block must call the fallback intent of the Amazon Lex bot. Figure 2 demonstrates a basic flow used to get input, check the response, and perform an action.

Figure 2. Basic Amazon Connect contact flow to integrate with Amazon Lex

Figure 2. Basic Amazon Connect contact flow to integrate with Amazon Lex

Amazon Lex

Amazon Lex converts the voice given by a caller into text, which is then processed by a Lambda function. When setting up the Amazon Lex bot you will require one fallback intent (Figure 3), one unused intent (Figure 4), and one clarification prompts disabled (Figure 5).

Figure 3. Amazon Lex fallback intent

Figure 3. Amazon Lex fallback intent

The fallback intent is created by using the pre-defined AMAZON.FallbackIntent and must be set up to call a Lambda function in the Fulfillment section. Using the fallback intent and Lambda fulfillment causes the system to ignore any utterance pattern and pass any translation directly to the Lambda function.

Figure 4. Amazon Lex unused intent

Figure 4. Amazon Lex unused intent

The unused intent is only created to satisfy Amazon Lex’s requirement for a bot to have at least one custom intent with a valid utterance.

Figure 5. Amazon Lex error handling clarification prompts

Figure 5. Amazon Lex error handling clarification prompts

In the error handling section of the Amazon Lex bot, the clarification prompts must be disabled. Disabling the clarification prompts stops the Amazon Lex bot from asking the caller to clarify the input.

AWS Lambda

AWS Lambda is called by Amazon Lex, which is used to interact with the third-party system. The third-party system will return values, which the Lambda will add to its session attributes resulting object. The resulting object from AWS Lambda requires the dialog action to be set up with the type set to “Close,” fulfillmentState set to “Fulfilled,” and the message contentType set to “CustomPayload”. This will allow Amazon Lex to pass the values to Amazon Connect without speaking the results to the caller.

Conclusion

In this blog we showed how Amazon Connect, Amazon Lex, and AWS Lambda functions can be used together to create common interactions with third-party systems. This is a flexible architecture and requires few changes to the Amazon Connect contact flow. You don’t have to set up pre-defined utterances that limit the allowed inputs. Using this solution, AWS customers can provide flexible solutions that interact with third-party systems.

Related information

Field Notes: Improving Call Center Experiences with Iterative Bot Training Using Amazon Connect and Amazon Lex

Post Syndicated from Marius Cealera original https://aws.amazon.com/blogs/architecture/field-notes-improving-call-center-experiences-with-iterative-bot-training-using-amazon-connect-and-amazon-lex/

This post was co-written by Abdullah Sahin, senior technology architect at Accenture, and Muhammad Qasim, software engineer at Accenture. 

Organizations deploying call-center chat bots are interested in evolving their solutions continuously, in response to changing customer demands. When developing a smart chat bot, some requests can be predicted (for example following a new product launch or a new marketing campaign). There are however instances where this is not possible (following market shifts, natural disasters, etc.)

While voice and chat bots are becoming more and more ubiquitous, keeping the bots up-to-date with the ever-changing demands remains a challenge.  It is clear that a build>deploy>forget approach quickly leads to outdated AI that lacks the ability to adapt to dynamic customer requirements.

Call-center solutions which create ongoing feedback mechanisms between incoming calls or chat messages and the chatbot’s AI, allow for a programmatic approach to predicting and catering to a customer’s intent.

This is achieved by doing the following:

  • applying natural language processing (NLP) on conversation data
  • extracting relevant missed intents,
  • automating the bot update process
  • inserting human feedback at key stages in the process.

This post provides a technical overview of one of Accenture’s Advanced Customer Engagement (ACE+) solutions, explaining how it integrates multiple AWS services to continuously and quickly improve chatbots and stay ahead of customer demands.

Call center solution architects and administrators can use this architecture as a starting point for an iterative bot improvement solution. The goal is to lead to an increase in call deflection and drive better customer experiences.

Overview of Solution

The goal of the solution is to extract missed intents and utterances from a conversation and present them to the call center agent at the end of a conversation, as part of the after-work flow. A simple UI interface was designed for the agent to select the most relevant missed phrases and forward them to an Analytics/Operations Team for final approval.

Figure 1 – Architecture Diagram

Amazon Connect serves as the contact center platform and handles incoming calls, manages the IVR flows and the escalations to the human agent. Amazon Connect is also used to gather call metadata, call analytics and handle call center user management. It is the platform from which other AWS services are called: Amazon Lex, Amazon DynamoDB and AWS Lambda.

Lex is the AI service used to build the bot. Lambda serves as the main integration tool and is used to push bot transcripts to DynamoDB, deploy updates to Lex and to populate the agent dashboard which is used to flag relevant intents missed by the bot. A generic CRM app is used to integrate the agent client and provide a single, integrated, dashboard. For example, this addition to the agent’s UI, used to review intents, could be implemented as a custom page in Salesforce (Figure 2).

Figure 2 – Agent feedback dashboard in Salesforce. The section allows the agent to select parts of the conversation that should be captured as intents by the bot.

A separate, stand-alone, dashboard is used by an Analytics and Operations Team to approve the new intents, which triggers the bot update process.

Walkthrough

The typical use case for this solution (Figure 4) shows how missing intents in the bot configuration are captured from customer conversations. These intents are then validated and used to automatically build and deploy an updated version of a chatbot. During the process, the following steps are performed:

  1. Customer intents that were missed by the chatbot are automatically highlighted in the conversation
  2. The agent performs a review of the transcript and selects the missed intents that are relevant.
  3. The selected intents are sent to an Analytics/Ops Team for final approval.
  4. The operations team validates the new intents and starts the chatbot rebuild process.

Figure 3 – Use case: the bot is unable to resolve the first call (bottom flow). Post-call analysis results in a new version of the bot being built and deployed. The new bot is able to handle the issue in subsequent calls (top flow)

During the first call (bottom flow) the bot fails to fulfil the request and the customer is escalated to a Live Agent. The agent resolves the query and, post call, analyzes the transcript between the chatbot and the customer, identifies conversation parts that the chatbot should have understood and sends a ‘missed intent/utterance’ report to the Analytics/Ops Team. The team approves and triggers the process that updates the bot.

For the second call, the customer asks the same question. This time, the (trained) bot is able to answer the query and end the conversation.

Ideally, the post-call analysis should be performed, at least in part, by the agent handling the call. Involving the agent in the process is critical for delivering quality results. Any given conversation can have multiple missed intents, some of them irrelevant when looking to generalize a customer’s question.

A call center agent is in the best position to judge what is or is not useful and mark the missed intents to be used for bot training. This is the important logical triage step. Of course, this will result in the occasional increase in the average handling time (AHT). This should be seen as a time investment with the potential to reduce future call times and increase deflection rates.

One alternative to this setup would be to have a dedicated analytics team review the conversations, offloading this work from the agent. This approach avoids the increase in AHT, but also introduces delay and, possibly, inaccuracies in the feedback loop.

The approval from the Analytics/Ops Team is a sign off on the agent’s work and trigger for the bot building process.

Prerequisites

The following section focuses on the sequence required to programmatically update intents in existing Lex bots. It assumes a Connect instance is configured and a Lex bot is already integrated with it. Navigate to this page for more information on adding Lex to your Connect flows.

It also does not cover the CRM application, where the conversation transcript is displayed and presented to the agent for intent selection.  The implementation details can vary significantly depending on the CRM solution used. Conceptually, most solutions will follow the architecture presented in Figure1: store the conversation data in a database (DynamoDB here) and expose it through an (API Gateway here) to be consumed by the CRM application.

Lex bot update sequence

The core logic for updating the bot is contained in a Lambda function that triggers the Lex update. This adds new utterances to an existing bot, builds it and then publishes a new version of the bot. The Lambda function is associated with an API Gateway endpoint which is called with the following body:

{
	“intent”: “INTENT_NAME”,
	“utterances”: [“UTTERANCE_TO_ADD_1”, “UTTERANCE_TO_ADD_2” …]
}

Steps to follow:

  1. The intent information is fetched from Lex using the getIntent API
  2. The existing utterances are combined with the new utterances and deduplicated.
  3. The intent information is updated with the new utterances
  4. The updated intent information is passed to the putIntent API to update the Lex Intent
  5. The bot information is fetched from Lex using the getBot API
  6. The intent version present within the bot information is updated with the new intent

Figure 4 – Representation of Lex Update Sequence

 

7. The update bot information is passed to the putBot API to update Lex and the processBehavior is set to “BUILD” to trigger a build. The following code snippet shows how this would be done in JavaScript:

const updateBot = await lexModel
    .putBot({
        ...bot,
        processBehavior: "BUILD"
    })
    .promise()

9. The last step is to publish the bot, for this we fetch the bot alias information and then call the putBotAlias API.

const oldBotAlias = await lexModel
    .getBotAlias({
        name: config.botAlias,
        botName: updatedBot.name
    })
    .promise()

return lexModel
    .putBotAlias({
        name: config.botAlias,
        botName: updatedBot.name,
        botVersion: updatedBot.version,
        checksum: oldBotAlias.checksum,
}) 

Conclusion

In this post, we showed how a programmatic bot improvement process can be implemented around Amazon Lex and Amazon Connect.  Continuously improving call center bots is a fundamental requirement for increased customer satisfaction. The feedback loop, agent validation and automated bot deployment pipeline should be considered integral parts to any a chatbot implementation.

Finally, the concept of a feedback-loop is not specific to call-center chatbots. The idea of adding an iterative improvement process in the bot lifecycle can also be applied in other areas where chatbots are used.

Accelerating Innovation with the Accenture AWS Business Group (AABG)

By working with the Accenture AWS Business Group (AABG), you can learn from the resources, technical expertise, and industry knowledge of two leading innovators, helping you accelerate the pace of innovation to deliver disruptive products and services. The AABG helps customers ideate and innovate cloud solutions with customers through rapid prototype development.

Connect with our team at [email protected] to learn and accelerate how to use machine learning in your products and services.


Field Notes provides hands-on technical guidance from AWS Solutions Architects, consultants, and technical account managers, based on their experiences in the field solving real-world business problems for customers.

 

Abdullah Sahin

Abdullah Sahin

Abdullah Sahin is a senior technology architect at Accenture. He is leading a rapid prototyping team bringing the power of innovation on AWS to Accenture customers. He is a fan of CI/CD, containerization technologies and IoT.

Muhammad Qasim

Muhammad Qasin

Muhammad Qasim is a software engineer at Accenture and excels in development of voice bots using services such as Amazon Connect. In his spare time, he plays badminton and loves to go for a run.

Amazon Connect – Now Smarter and More Integrated With Third-Party Tools

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/amazon-connect-smarter-and-more-integrated/

We launched Amazon Connect in 2017 and, since then, thousands of customers have created their own contact centers in the cloud. Amazon Connect makes it easy for non-technical customers to design interaction flows, manage agents, and track performance metrics.

For example, when I book a Best Western hotel room in Europe by phone, the call is managed by Amazon Connect. In the UK, the Post Office went from ideation to production rollout in just three weeks. In France, WebHelp, a global leader in Business Process Outsourcing, activated thousands of workstations and remote agents in just 72 hours.

Since I last blogged about Amazon Connect, the team has been continuously listening for your feedback and, today, I am happy to announce a new set of capabilities to make Amazon Connect smarter and more integrated with third-party tools.

We are using Machine Learning (ML) to make Amazon Connect smarter at analyzing conversations in real time, finding relevant information needed by contact center agents, and authenticating customers by the sound of their voice. The second set of capabilities makes Amazon Connect easier to integrate with third party tools or services to present unified customer profile information to contact center agents, and to make it easier to manage their tasks.

Let’s go into the details, one by one.

Contact Lens Real Time
Contact Lens for Amazon Connect is a set of machine learning (ML) capabilities allowing contact center supervisors to better understand the sentiment, trends, and compliance of customer conversations. It was first announced during re:Invent 2019 and available since July 2020. It allows to effectively train agents, replicate successful interactions, and to identify crucial company and product feedback.

Starting today, you can get real-time insights into customer experience during the live calls, such as a customer expressing dissatisfaction. Customer experience analytics and alerts for live calls are delivered in Amazon Connect’s real-time metrics dashboard. It makes it easy for supervisors to identify when to listen-in on a critical call, and to provide guidance to the agent via chat, or have the agent transfer the call to them for assistance.

You, as the contact center manager, can define rules using specific terms such as “not happy,” “poor quality product,” and “cancel my subscription.” Contact Lens uses natural language processing (NLP) to perform intelligent matching to automatically detect variations of the spoken words even when the example phrases are limited.

Create Rules for real time analytics

Contact Lens analyzes in-progress calls in real time to detect when the rule criteria for a customer experience issue is met, and immediately creates an alert next to the live call in the Amazon Connect dashboard to notify supervisors of the situation.

Real Time alert based on rules

With this launch, we are adding 13 language variants to post-call analytics, in addition to the 5 already supported :English (United States), English (Great Britain), English (Australia), English (India), and Spanish (United States).

The new language variants for post-call analytics are: English (Ireland), English (Scotland), English (Wales), Spanish (Spain), French (Canada), French (France), Portuguese (Portugal), Portuguese (Brazil), German (Germany), German (Switzerland), Italian (Italy), Arabic (Gulf), and Hindi (India).

Contact Lens for Amazon Connect real-time is available in 4 language variants: English (United States), English (Great Britain), English (Australia), and Spanish (United States). More language variants will be added at later stage.

For more details visit this launch page.

Amazon Connect Wisdom (Preview)
Wisdom provides built-in agent assistance capabilities in Amazon Connect, including machine learning (ML) powered search and real-time recommendations, to quickly enable agents with relevant information for resolving customer issues.

As an agent, I can type questions or phrases in the Wisdom search box, without guessing what keyword I should use. Wisdom understands what information I am searching for. It surfaces results in the agent’s preferred Amazon Connect application, the web-based one we do provide, or the ones you built.

Amazon Connect Wisdom search results

Wisdom comes with pre-built connectors to third-party knowledge repositories to provide most relevant results to agents. Wisdom includes connectors for Salesforce and ServiceNow during the preview, with more to come at launch.

Wisdom may use Contact Lens Real Time analytics to analyse the conversation in real-time. It detects the customer issue, finds related content in the connected repositories, and provides proactive recommendations to help the agent resolve it. For example, Wisdom can detect that a customer is talking about a problem with the handbag they bought last week, recommend an article that describes similar products defect, and provide instructions with a link to the order management application needed to initiate an exchange.

Wisdom is available in preview, you can signup today or visit the launch page.

Amazon Connect Voice ID (Preview)
Amazon Connect Voice ID provides real-time caller authentication which makes voice interactions in contact centers more secure and efficient.

To effectively recognise me as “Sébastien”, Voice ID must learn how I am talking. This is the enrolment phase. Then it compares the sound of my voice with the one enrolled earlier. This is the verification phase.

To meet with personal data protection laws, contact center agents capture my consent to use Voice ID.

During the enrolment phase, Voice ID listens to the call until it has captured 30 seconds of my voice. Then it creates my voiceprint, which uniquely authenticates me. A voiceprint is a mathematical representation that captures unique aspects of an individual’s voice such as speech rhythm, pitch, intonation, and loudness. I do not need to say or repeat any specific phrases to let Voice ID create my voiceprint. Voice ID provides an API that can be used to opt-out a customer.

When I call back in, Voice ID just needs 10 seconds of my voice to authenticate me. My voice can be captured as part of a typical interaction with the Interactive Voice Response (IVR) happening at the start of the call, or when I first start to talk with the agent. For example when I am answering questions, such as “what’s your first and last name?” and “what are you calling about?”, Voice ID uses this audio to generate my voiceprint again. It compares it with the one enrolled earlier. Voice ID then generates an authentication score depending on the confidence of the match. Contact center managers can use this score to create policies in Amazon Connect to let agents see a real-time result (“authenticated” or “not authenticated”) in their web-based application. Agents can then decide to proceed with the call or ask for additional authentication credentials.

Amazon Connect Voice ID is available in preview. You can signup today or visit this launch page.

Amazon Connect Customer Profiles
Customer Profiles is a unified profile for Amazon Connect that brings together customer information from disparate sources without having to build integrations or wrangle data.

Providing agents (or automatic IVR systems) with accurate and unified customer profile information at the right moment helps them to deliver better service to customers, and to resolve calls faster. Using Customer Profiles, agents must not navigate out of Amazon Connect, or switch between different applications to get the customer insights they need.

With just a few clicks, System Administrators can integrate customer profile data from applications like Salesforce, ServiceNow, Zendesk, and Marketo to build your own homegrown integration. Setting up connectors for Customer Profiles requires no programming or data integration expertise.

Once enabled, Customer Profiles automatically detects customer records from the applications. It matches and deduplicates them. This results in accurate and up-to-date profiles displayed to agents within their Connect web-based experience.

Amazon Connect Customer Profile

Learn more about Amazon Connect Customer Profile by visiting the launch page.

Amazon Connect Tasks
Amazon Connect Tasks makes it easy to automate, track, and manage contact center agent tasks. It provides a single place for contact center managers to prioritize, assign, and track customer service tasks across the disparate applications used by agents, so that they are focused on the highest priority work of any type.

Tasks can be sourced from third-party applications, such as a CRM solution, or to update a business-specific system. For example, you can programmatically create tasks for agents to follow-up on a customer case in a third party application like Salesforce, or complete an action item in a business-specific application, such as processing a claim in an insurance system. You can also automate tasks that dont require agent interaction, to ensure your agents spend more time focused on customers.

Using Amazon Connect tasks, agents no longer need to switch between applications to know what work should be completed, and with what priority. Agents can see all their assigned tasks right from the Amazon Connect contact control panel, the same web-based application they use to interact with customers over calls and chat. When a task is assigned, the agent receives a notification with the description of the task, and when required, links to any external applications needed to complete the action. Agents can also create tasks so that follow-up work is not forgotten, for example calling a customer back to provide a status update.

Amazon Connect Accept Tasks Amazon Connect View Task Create a task uisng Amazon Connect Tasks
Incoming Tasks Task Details Create a new Task

Amazon Connect Tasks provides pre-built connectors fo Salesforce and Zendesk. With just a few clicks, you can easily set up rules to automatically create tasks based on pre-defined conditions, as sown on the screenshot hereunder. It also provides an API to create tasks from any other application.

Amazon Connect Task Rules

Learn more about how to configure and to get started with Tasks by visiting the launch page.

Available Today
Three of these new capabilities are available today: Contact Lens Real Time, Customer Profiles, and Tasks. You must register to the preview program to test Wisdom and Voice ID.

Customer Profile and Tasks are available in all AWS Regions where Amazon Connect is available : US East (N. Virginia), US East (Ohio), US West (N. California), US West (Oregon), Asia Pacific (Sydney), Asia Pacific (Singapore), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (London). Contact Lens Real Time is available in US West (Oregon), US East (N. Virginia), and Asia Pacific (Sydney) at the moment. Wisdom is available in US East (N. Virginia) and US West (Oregon) during the preview, while Voice ID is available only in US West (Oregon) during the preview.

With Amazon Connect, you only pay for what you use. There are no required up-front payments, long-term commitments, or minimum monthly fees. The price metrics for these new capabilities are detailed on the Amazon Connect pricing page.

Should you need help adding Amazon Connect any of these capabilities to contact flows, please reach out to one of the dozens of Amazon Connect partners available worldwide.

— seb

Send voice appointment reminders using Amazon Pinpoint custom channels and Amazon Connect

Post Syndicated from Ryan Lowe original https://aws.amazon.com/blogs/messaging-and-targeting/send-voice-appointment-reminders-using-amazon-pinpoint-custom-channels-and-amazon-connect/

Introduction

In this post, we will walk through setting up an always-on appointment reminder campaign in Amazon Pinpoint. No-show rates are a constant challenge for service providers. Industries such as hospitality estimate 20% of diners miss reservations in big cities,1 while salons average five missed appointments per week.2 Professional services such as financial institutions and sales teams have similar challenges to ensure clients do not miss meetings. To these businesses, an appointment missed represents lost revenue. As a result, the no-show rate is a key metric to improve. An outbound voice message provides another way to reach customers versus emails or SMS, and voice reminders give customers the choice of channels based on personal preferences.

Overview

Amazon Pinpoint is a multichannel communications service enabling customers to send both promotional and transactional messages across email, SMS, push notifications, voice, and custom channels. Amazon Connect is an easy to use omnichannel cloud contact center that helps companies provide superior customer service at a lower cost.

There are benefits of using these services together. Amazon Pinpoint allows you to build a segment of users which can be used within a campaign. Amazon Connect can enable customers to send outbound voice messages at scale should your user audience be large and require a high number of transactions per second (TPS).

To use these services together, you setup custom channels in Amazon Pinpoint, which can be created via an AWS Lambda function. These functions enable you to call APIs to trigger message sends as part of Amazon Pinpoint campaigns. Amazon Pinpoint has developed a new AWS Lambda function which can be used to send outbound voice messages via Amazon Connect. This configuration allows you to define the voice message to be sent, define the segment of users you would like to target, and send voice messages at scale through Amazon Connect via the Amazon Pinpoint custom channel.

The audience for this solution are technical customers who are used to working with multiple AWS services and are familiar with AWS Lambda functions. The solution built relies on the Amazon Pinpoint custom channel feature and targeting, along with the Amazon Connect outbound voice API called via a prepared AWS Lambda function. Once completed, you will be able to create an evergreen campaign which will send outbound voice messages to your patients who have an appointment the following day.

The costs associated with this solution will be:

  1. Amazon Connect outbound voice calls per minute
  2. Amazon Connect claimed phone number(s)
  3. Amazon Pinpoint Monthly Targeted Audience (MTA) costs.

The costs for a outbound voice reminder system that sends 10k messages per day, with an average length of 20 seconds per call, to an total monthly audience of 300k, in the US are as follows. Note that prices with vary for other countries. Complete Amazon Connect outbound call pricing can be found here.

Solution

Prerequisites:

For this walkthrough the article assumes:

  • An AWS account
  • Basic understanding of IAM and privileges required to create the following; IAM identity provider, roles, policies, and users
  • Basic understanding of Amazon Pinpoint and how to create a project
  • Basic understanding of Amazon Connect and experience in creating contact flows. More information on setup of Amazon Connect can be found here.

Step 1: Create an Appointment Reminder custom event

The first step in setting up this solution is to create and report a custom event to Amazon Pinpoint. There are multiple ways to report events in your application. Ffor demonstration purposes, below are two example event calls using the AWS SDK for Python (Boto3) from inside an AWS Lambda Function.

It is important to note that the Amazon Pinpoint events API can also be used to update endpoints when the event gets registered. In the below example, the first API call will update the endpoint attributes AppointmentDate and AppointmentTime with the details of the upcoming appointment. These attributes will be used in the outgoing message to the end-user

Sample Event: Appointment Coming Up

import boto3

client = boto3.client('pinpoint')
app_id = '[PINPOINT_PROJECT_ID]'
endpoint_id = '[ENDPOINT_ID]'
address = '[PHONE_NUMBER]'

def lambda_handler(event, context):

client.put_events(
ApplicationId = applicationId,
EventsRequest={
'BatchItem': {
endpoint_id: {
'Endpoint': {
'ChannelType': 'CUSTOM',
'Address': address,
'Attributes': {
'AppointmentDate': ['December 15th, 2020'],
'AppointmentTime': ['2:15pm']
}
},
'Events':{
'appointment-event': {
'Attributes':{},
'EventType': 'AppointmentReminder',
'Timestamp': datetime.datetime.fromtimestamp(time.time()).isoformat()
}
}
}
}
}
)

NOTE: The following steps assume that the AppointmentReminder event is being reported to Amazon Pinpoint. If you are unable to integrate the above API call into your application, you can manually create an AWS Lambda function using a Python runtime with the above code to trigger sample events.

Step 2: Create an Amazon Connect contact flow for outbound calls

This article assumes that you have an Amazon Connect contact center already setup and working. In this step, we will set up our Amazon Connect contact flow to dial our recipients and play read the message before hanging up.

  1. Log in to your Amazon Connect instance using your access URL (https://<alias>.awsapps.com/connect/login).
    Note: Replace alias with your instance’s alias.
  2. In the left navigation bar, pause on Routing, and then choose Contact flows.
  3. Under Contact flows, choose a template, or choose Create contact flow to design a contact flow from scratch. For more information, see Create a New Contact Flow.
  4. Download the sample JSON contact flow configuration file Outbound_calling.json.
  5. Choose the dropdown menu under Save and choose Import flow (beta).
  6. Select the Outbound_calling.json file in the Import flow (beta) dialog and choose Save.
  7. Choose Save to open the Save flow dialog. Then choose Save to close the dialog.
  8. Choose Publish to open the Publish dialog. Then choose Publish to close the dialog.
  9. In the contact flow designer, expand Show additional flow information.
  10. Under ARN, copy the Amazon Resource Name (ARN) contact flow. It looks like the following:
    arn:aws:connect:region:123456789012:instance/[ConnectInstanceId]/contact-flow/[ConnectContactFlowId]Note the ConnectInstanceId and ConnectContactFlowId from the ARN, they will be used in the next step.
  11. In the left navigation bar, pause on Routing and then choose Queues.
  12. Choose the queue you wish to use for the outbound calls.
  13. In the Edit queue screen, expand Show additional queue information.
  14. Under ARN, copy the Amazon Resource Name (ARN) for the queue. It looks like the following:
    arn:aws:connect:region:123456789012:instance/[ConnectInstanceId]/contact-flow/[ConnectQueueId]Note the ConnectQueueId from the ARN. It will be used in the next step.

Step 3: Deploy and modify the Amazon Pinpoint to the Amazon Connect custom channel with AWS Lambda function

Next, we will need to deploy an Amazon Pinpoint custom channel. Custom channels in Amazon Pinpoint allow you to send messages through any service with an API, including Amazon Connect. The AWS Serverless Application Repository contains an open-sourced AWS Lambda function that we will use for our custom channel. After deploying the AWS Lambda function, we will customize it to match our requirements.

  1. Navigate to the AWS Lambda Console, then choose Create function.
  2. Under Create function, Choose Browser serverless app repository.
  3. Under Public applications, choose the checkbox next to Show apps that create custom IAM roles or resource policies and enter amazon-pinpoint-connect-channel in the search box.
  4. Choose the amazon-pinpoint-connect-channel card from the list and review the Application details.
  5. Under Application settings enter the details for ConnectContactFlowId, ConnectInstanceId, and ConnectQueueId from the previous step.
  6. After reviewing all the details, choose the checkbox next to I acknowledge that this app creates custom IAM roles and resource policies and choose Deploy.
  7. Wait a couple minutes for the application to deploy two AWS Lambda functions and an AWS Simple Queue Service queue.
  8. Under Resources, choose the PinpointConnectQueuerFunction resource to open the AWS Lambda function configuration. This is the AWS Lambda function that Amazon Pinpoint will call when the message is crafted.
  9. Under Function code, scroll down to line 31 and replace
    message = "Hello World! -Pinpoint Connect Channel"
    with
    message = "This is a reminder of your upcoming appointment on {0} at {1}".format(endpoint_profile["Attributes"]["AppointmentDate"][0], endpoint_profile["Attributes"]["AppointmentTime"][0])
  10. Choose Deploy.

Step 4: (Optional) Modify the custom channel AWS Lambda function to meet change the rate of outgoing calls

By default, the custom channel we deployed in the previous step will place outbound calls through Amazon Connect at a rate of 1 call every 3 seconds. This allows you to configure how many active outbound calls to avoid running into service limits. Review your current service limits in Amazon Connect for more details.

  1. Navigate to the AWS Lambda Console, then choose AmazonPinpointConnectChannel-backgroundprocessor function.
  2. Under Function code, scroll down to line 73 and replace the sleep timer, currently set with 3 seconds, with your requirements.
  3. Choose Deploy.

Step 5: Create a Pinpoint custom campaign with your lambda function and segment

  1. Create a CSV file to import endpoints with the attributes of AppointmentDate and AppointmentTime.
    Example:
    Id,Address,ChannelType,Attributes.AppointmentDate,Attributes.AppointmentTime
    1,+1[PHONE_NUMBER],SMS,November 30 2020,9:00am
    2,+1[PHONE_NUMBER2],SMS,November 30 2020,10:00am
  2. Navigate to the Amazon Pinpoint console.
  3. In the All Projects list, select your project.
  4. In the navigation pane, choose Segments.
  5. Choose Create a Segment.
  6. Choose Import a segment and upload your CSV file and choose Create segment.
  7. In the navigation pane, choose Campaigns.
  8. Choose Create campaign.
  9. In the Create a campaign wizard, enter a name for campaign name.
  10. Under Channel choose Custom.
  11. Choose Next.
  12. On the Choose a segment screen, choose the segment created above, and choose Next.
  13. On the Create your message screen, do the following:
    a) For Lambda function choose AmazonPinpointConnectChannel that we deployed in Step 3 above.
    b) For endpoint Options choose SMS.
    c) Choose Next.
  14. On the Choose when to send the campaign screen, do the following:
    a) Choose When an event occurs.
    b) Under Events, choose the AppointmentReminder event.
    c) Under campaign dates, choose a Start date and time and an End date and time to be used as the campaign’s duration.
  15. Choose Next.
  16. Review the campaign details and choose Launch campaign.

Cleanup:

To remove the two AWS Lambda functions and the Amazon Simple Queue Service queue provisioned in the steps above in order not to incur further charges, please follow these steps below.

  1. Navigate to the Amazon CloudFormation Console.
  2. Choose severlessrepo-amazon-pinpoint-connect-channel and choose Delete.
  3. Choose Delete stack in the delete confirmation window.

 

Next Steps:

You can continue to iterate on this experience using Amazon Pinpoint and Amazon Connect to create a custom user experience.

To learn more about these services, please visit the Amazon Pinpoint or Amazon Connect web pages.

(1) https://www.scisolutions.com/uploads/news/Missed-Appts-Cost-HMT-Article-042617.pdf

(2) https://blog.carbonfreedining.org/the-ultimate-guide-to-restaurant-no-shows

Creating a Seamless Handoff Between Amazon Pinpoint and Amazon Connect

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/creating-a-seamless-handoff-between-amazon-pinpoint-and-amazon-connect/

Note: This post was written by Ilya Pupko, Senior Consultant for the AWS Digital User Engagement team.


Time to read 5 minutes
Learning level Intermediate (200)
Services used Amazon Pinpoint, Amazon SNS, AWS Lambda, Amazon Lex, Amazon Connect

Your customers deserve to have helpful communications with your brand, regardless of the channel that you use to interact with them. There are many situations in which you might have to move customers from one channel to another—for example, when a customer is interacting with a chatbot over SMS, but their needs suddenly change to require voice assistance. To create a great customer experience, your communications with your customers should be seamless across all communication channels.

Welcome aboard Customer Obsessed Airlines

In this post, we look at a scenario that involves our fictitious airline, Customer Obsessed Airlines. Severe storms in one area of the country have caused Customer Obsessed Airlines to cancel a large number of flights. Customer Obsessed Airlines has to notify all of the affected customers of the cancellations right away. But most importantly, to keep customers as happy as possible in this unfortunate and unavoidable situation, Customer Obsessed Airlines has to make it easy for customers to rebook their flights.

Fortunately, Customer Obsessed Airlines has implemented the solution that’s outlined later in this post. This solution uses Amazon Pinpoint to send messages to a targeted segment of customers—in this case, the specific customers who were booked on the affected flights. Some of these customers might have straightforward travel itineraries that can simply be rebooked through interactions with a chatbot. Other customers who have more complex itineraries, or those who simply prefer to interact with a human over the phone, can be handed off to an agent in your call center.

About the solution

The solution that we’ll build to handle this scenario can be deployed in under an hour. The following diagram illustrates the interactions in this solution.

At a high level, this solution uses the following workflow:

  1. An event occurs. Automated impact analysis systems trigger the creation of custom segments—in this case, all passengers whose flights were cancelled.
  2. Amazon Pinpoint sends a message to the affected passengers through their preferred channels. Amazon Pinpoint supports the email, SMS, push, and voice channels, but in this example, we focus exclusively on SMS.
  3. Passengers who receive the message can respond. When they do, they interact with a chatbot that helps them book a different flight.
  4. If a passenger requests a live agent, or if their situation can’t be handled by a chatbot, then Amazon Pinpoint passes information about the customer’s situation and communication history to Amazon Connect. The passenger is entered into a queue. When the passenger reaches the front of the queue, they receive a phone call from an agent.
  5. After being re-booked, the passenger receives a written confirmation of the changes to their itinerary through their preferred channel. Passengers are also given the option of providing feedback on their interaction when the process is complete.

To build this solution, we use Amazon Pinpoint to segment our customers based on their attributes (such as which flight they’ve booked), and to deliver messages to those segments.

We also use Amazon Connect to manage the voice calling part of the solution, and Amazon Lex to power the chatbot. Finally, we connect these services using logic that’s defined in AWS Lambda functions.

Setting up the solution

Step 1: Set up Amazon Pinpoint and link it with Amazon Lex

The first step in setting up this solution is to create a new Amazon Pinpoint project and configure the SMS channel. When that’s done, you can create an Amazon Lex chatbot and link it to the Amazon Pinpoint project.

We described this process in detail in an earlier blog post. Complete the procedures in Create an SMS Chatbot with Amazon Pinpoint and Amazon Lex, and then proceed to step 2.

Step 2: Set up Amazon Connect and link it with your Amazon Lex chatbot

By completing step 1, we’ve created a system that can send messages to our passengers and receive messages from them. The next step is to create a way for passengers to communicate with our call center.

The Amazon Connect Administrator Guide provides instructions for linking an Amazon Lex bot to an Amazon Connect instance. For complete procedures, see Add an Amazon Lex Bot.

When you complete these procedures, link your Amazon Connect instance to the same Amazon Lex bot that you created in step 1. This step is intended to provide customers with a consistent, cohesive experience across channels.

Step 3: Set up an Amazon Connect callback queue and use Amazon Pinpoint keyword logic to trigger it

Now that we’ve configured Amazon Pinpoint and Amazon Connect, we can connect them.

Linking the two services makes it possible for passengers to request additional assistance. Traditionally, passengers in this situation would have to call a call center themselves and then wait on hold for an agent to become available. However, in this solution, our call center calls the passenger directly as soon as an agent is available. When the agent calls the passenger, the agent has all of the information about the passenger’s issue, as well as a transcript of the passenger’s interactions with your chatbot.

To implement an automatic callback mechanism, use the Amazon Pinpoint Connect Callback Requestor, which is available on the AWS GitHub page.

Next steps

By completing the preceding three steps, you can send messages to a subset of your users based on the criteria you choose and the type of message you want to send. Your customers can interact with your message by replying with questions. When they do, a chatbot responds intelligently and appropriately.

You can add to this solution by expanding it to cover other communication channels, such as push notifications. You can also automate the initial communication by integrating the solution with your systems of record.

We’re excited to see what you build using the solution that we outlined in this post. Let us know of your ideas and your successes in the comments.

Amazon Connect Introduces Web & Mobile Chat for a True Omnichannel Contact Center Experience

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/new-omnichannel-contact-center-web-and-mobile-chat-for-amazon-connect/

When we started Amazon in 1995, it was with the mission to be the earth’s most customer-centric company. It obviously requires many talented individuals and technologies to deliver on that vision, including contact centers. As Amazon’s retail business scaled, we first shopped for third-party contact center solutions, but we could not find one that fit our needs, so we decided to build our own. After we built an initial version, we listened to our contact center team feedback and iterated for several years to meet our strict requirements of security, elasticity, flexibility, reliability, and high customer experience standards. Many AWS customers told us they have the same challenges to procure, install, configure, and operate their contact centers. They asked us to make our solution available to all businesses.

Since we launched Amazon Connect, thousands of customers have created their own contact centers in the cloud. Amazon Connect makes it easy for non-technical customers to design contact flows, manage agents, and track performance metrics. It is easy to integrate Amazon Connect to other systems, such as customer relationship management (CRM) or to integrate Amazon Lex intelligent conversational bots into contact flows. For example, Intuit integrates Amazon Connect with Salesforce to build contact flow experiences that adapt to their customer needs in real-time. In the United Kingdom, the National Health Service (NHS) is using Amazon Connect and Amazon Lex to automatically answer most frequently asked questions about European Health Insurance Card (EHIC). During the first four weeks of operation, 42 percent of EHIC calls were resolved via the integrated Amazon Connect and Amazon Lex solution, and did not have to be passed back to a human agent. There was a 26 percent reduction in EHIC contact center calls handled by human agents.

But voice-based contact centers are only one part of the story. Today, we communicate more and more with messaging, and customers use multiple channels to communicate with businesses. Often, simple questions can be answered by a short chat message and do not involve a voice conversation with an agent. This is why we are announcing web and mobile chat for Amazon Connect. Your customers can now choose between using chat or making a phone call to get their questions or concerns addressed. When they choose to chat with a contact center agent, they can do it at their own pace, making it as familiar as messaging a friend. Conversation context is maintained across both chat and voice, giving customers freedom to move between channels without forcing them to start all over again or to wait for an agent.

Amazon Connect chat gives businesses a single unified contact center service for voice and chat. Amazon Connect provides a single routing engine which increases efficient distribution amongst agents and reduces end-customer wait times. Agents have a single user interface to help end-customers using both voice and chat, reducing the number of tools they have to learn and the number of screens they have to interact with. Chat activities nicely integrate into your existing contact center flows and the automation you built for voice. You build your flows once and you reuse them across multiple channels. Likewise, for metrics collection and the dashboards you built, they automatically benefit from the unified metrics across multiple channels.

Your customers can start chatting with contact center agents from any of your business applications, web or mobile. Let’s see what it looks like. In the below example, the chat is integrated in your company web site.

Contact center agents receive chat requests in the same web-based Contact Control Panel (CCP) they use for voice engagements. Since it is web-based, agents can work from virtually anywhere. Customers can integrate the CCP directly into the applications their contact center agents use, such as their customer relationship management (CRM) system, using the CCP SDK.

To add chat capabilities to your Amazon Connect contact center, open the console and enable your agents to take chats by enabling Chat in their Routing Profile, no code is required. Once this is done, they can begin accepting chats through the updated agent experience.

Should you need help adding Amazon Connect chat capabilities to your website or applications, please reach out to one of the dozens of Amazon Connect partners available worldwide.

Amazon Connect chat is charged on a per use basis. There are no required up-front payments, long-term commitments or minimum monthly fees. You pay per chat message, independently of the number of agents or customers using it. Regional pricing may vary, read the pricing page for the details.

Amazon Connect chat will be generally available this week in all AWS regions where Amazon Connect is offered: US East (Northern Virginia), US West (Oregon), EU (Frankfurt), Asia Pacific (Sydney), Asia Pacific (Tokyo).

As usual, we’re willing to hear your feedback, do not hesitate to share your thoughts with us.

— seb

Intuit: Serving Millions of Global Customers with Amazon Connect

Post Syndicated from Annik Stahl original https://aws.amazon.com/blogs/architecture/intuit-serving-millions-of-global-customers-with-amazon-connect/

Recently, Bill Schuller, Intuit Contact Center Domain Architect met with AWS’s Simon Elisha to discuss how Intuit manages its customer contact centers with AWS Connect.

As a 35-year-old company with an international customer base, Intuit is widely known as the maker of Quick Books and Turbo Tax, among other software products. Its 50 million customers can access its global contact centers not just for password resets and feature explanations, but for detailed tax interpretation and advice. As you can imagine, this presents a challenge of scale.

Using Amazon Connect, a self-service, cloud-based contact center service, Intuit has been able to provide a seamless call-in experience to Intuit customers from around the globe. When a customer calls in to Amazon Connect, Intuit is able to do a “data dip” through AWS Lambda out to the company’s CRM system (in this case, SalesForce) in order to get more information from the customer. At this point, Intuit can leverage other services like Amazon Lex for national language feedback and then get the customer to the right person who can help. When the call is over, instead of having that important recording of the call locked up in a proprietary system, the audio is moved into an S3 bucket, where Intuit can do some post-call processing. It can also be sent it out to third parties for analysis, or Intuit can use Amazon Transcribe or Amazon Comprehend to get a transcription or sentiment analysis to understand more about what happened during that particular call.

Watch the video below to understand the reasons why Intuit decided on this set of AWS services (hint: it has to do with the ability to experiment with speed and scale but without the cost overhead).

*Check out more This Is My Architecture video series.

About the author

Annik StahlAnnik Stahl is a Senior Program Manager in AWS, specializing in blog and magazine content as well as customer ratings and satisfaction. Having been the face of Microsoft Office for 10 years as the Crabby Office Lady columnist, she loves getting to know her customers and wants to hear from you.

Configuring user creation workflows with AWS Step Functions and AWS Managed Microsoft AD logs

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/configuring-user-creation-workflows-with-aws-step-functions-and-aws-managed-microsoft-ad-logs/

This post is contributed by Taka Matsumoto, Cloud Support Engineer

AWS Directory Service lets you run Microsoft Active Directory as a managed service. Directory Service for Microsoft Active Directory, also referred to as AWS Managed Microsoft AD, is powered by Microsoft Windows Server 2012 R2. It manages users and makes it easy to integrate with compatible AWS services and other applications. Using the log forwarding feature, you can stay aware of all security events in Amazon CloudWatch Logs. This helps monitor events like the addition of a new user.

When new users are created in your AWS Managed Microsoft AD, you might go through the initial setup workflow manually. However, AWS Step Functions can coordinate new user creation activities into serverless workflows that automate the process. With Step Functions, AWS Lambda can be also used to run code for the automation workflows without provisioning or managing servers.

In this post, I show how to create and trigger a new user creation workflow in Step Functions. This workflow creates a WorkSpace in Amazon WorkSpaces and a user in Amazon Connect using AWS Managed Microsoft AD, Step Functions, Lambda, and Amazon CloudWatch Logs.

Overview

The following diagram shows the solution graphically.

Configuring user creation workflows with AWS Step Functions and AWS Managed Microsoft AD logs

Walkthrough

Using the following procedures, create an automated user creation workflow with AWS Managed Microsoft AD. The solution requires the creation of new resources in CloudWatch, Lambda, and Step Functions, and a new user in Amazon WorkSpaces and Amazon Connect. Here’s the list of steps:

  1. Enable log forwarding.
  2. Create the Lambda functions.
  3. Set up log streaming.
  4. Create a state machine in Step Functions.
  5. Test the solution.

Requirements

To follow along, you need the following resources:

  • AWS Managed Microsoft AD
    • Must be registered with Amazon WorkSpaces
    • Must be registered with Amazon Connect

In this example, you use an Amazon Connect instance with SAML 2.0-based authentication as identity management. For more information, see Configure SAML for Identity Management in Amazon Connect.

Enable log forwarding

Enable log forwarding for your AWS Managed Microsoft AD.  Use /aws/directoryservice/<directory id> for the CloudWatch log group name. You will use this log group name when creating a Log Streaming in Step 3.

Create Lambda functions

Create two Lambda functions. The first starts a Step Functions execution with CloudWatch Logs. The second performs a user registration process with Amazon WorkSpaces and Amazon Connect within a Step Functions execution.

Create the first function with the following settings:

  • Name: DS-Log-Stream-Function
  • Runtime: Python 3.7
  • Memory: 128 MB
  • Timeout: 3 seconds
  • Environment variables:
    • Key: stateMachineArn
    • Value: arn:aws:states:<Region>:<AccountId>:stateMachine:NewUserWorkFlow
  • IAM role with the following permissions:
    • AWSLambdaBasicExecutionRole
    • The following permissions policy
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "states:StartExecution",
            "Resource": "*"
        }
    ]
}
import base64
import boto3
import gzip
import json
import re
import os
def lambda_handler(event, context):
    logEvents = DecodeCWPayload(event)
    print('Event payload:', logEvents)
    returnResultDict = []
    
    # Because there can be more than one message pushed in a single payload, use a for loop to start a workflow for every user
    for logevent in logEvents:
        logMessage = logevent['message']
        upnMessage =  re.search("(<Data Name='UserPrincipalName'>)(.*?)(<\/Data>)",logMessage)
        if upnMessage != None:
            upn = upnMessage.group(2).lower()
            userNameAndDomain = upn.split('@')
            userName = userNameAndDomain[0].lower()
            userNameAndDomain = upn.split('@')
            domainName = userNameAndDomain[1].lower()
            sfnInputDict = {'Username': userName, 'UPN': upn, 'DomainName': domainName}
            sfnResponse = StartSFNExecution(json.dumps(sfnInputDict))
            print('Username:',upn)
            print('Execution ARN:', sfnResponse['executionArn'])
            print('Execution start time:', sfnResponse['startDate'])
            returnResultDict.append({'Username': upn, 'ExectionArn': sfnResponse['executionArn'], 'Time': str(sfnResponse['startDate'])})

    returnObject = {'Result':returnResultDict}
    return {
        'statusCode': 200,
        'body': json.dumps(returnObject)
    }

# Helper function decode the payload
def DecodeCWPayload(payload):
    # CloudWatch Log Stream event 
    cloudWatchLog = payload['awslogs']['data']
    # Base 64 decode the log 
    base64DecodedValue = base64.b64decode(cloudWatchLog)
    # Uncompress the gzipped decoded value
    gunzipValue = gzip.decompress(base64DecodedValue)
    dictPayload = json.loads(gunzipValue)
    decodedLogEvents = dictPayload['logEvents']
    return decodedLogEvents

# Step Functions state machine execution function
def StartSFNExecution(sfnInput):
    sfnClient = boto3.client('stepfunctions')
    try:
        response = sfnClient.start_execution(
            stateMachineArn=os.environ['stateMachineArn'],
            input=sfnInput
        )
        return response
    except Exception as e:
        return e

For the other function used to perform a user creation task, use the following settings:

  • Name: SFN-New-User-Flow
  • Runtime: Python 3.7
  • Memory: 128 MB
  • Timeout: 3 seconds
  • Environment variables:
    • Key: nameDelimiter
    • Value: . [period]

This delimiter is used to split the username into a first name and last name, as Amazon Connect instances with SAML-based authentication require both a first name and last name for users. For more information, see CreateUser API and UserIdentity Info.

  • Key: bundleId
  • Value: <WorkSpaces bundle ID>

Run the following AWS CLI command to return Amazon-owned WorkSpaces bundles. Use one of the bundle IDs for the key-value pair.

aws workspaces describe-workspace-bundles –owner AMAZON

  • Key: directoryId
  • Value: <WorkSpaces directory ID>

Run the following AWS CLI command to return Amazon WorkSpaces directories. Use your directory ID for the key-value pair.

aws workspaces describe-workspace-directories

  • Key: instanceId
  • Value: <Amazon Connect instance ID>

Find the Amazon Connect instance ID the Amazon Connect instance ID.

  • Key: routingProfile
  • Value: <Amazon Connect routing profile>

Run the following AWS CLI command to list routing profiles with their IDs. For this walkthrough, use the ID for the basic routing profile.

aws connect list-routing-profiles –instance-id <instance id>

  • Key: securityProfile
  • Value: <Amazon Connect security profile>

Run the following AWS CLI command to list security profiles with their IDs. For this walkthrough, use the ID for an agent security profile.

aws connect list-security-profiles –instance-id  <instance id>

  • IAM role permissions:
    • AWSLambdaBasicExecutionRole

The following permissions policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "connect:CreateUser",
                "workspaces:CreateWorkspaces"
            ],
            "Resource": "*"
        }
    ]
}
import json
import os
import boto3

def lambda_handler(event, context):
    userName = event['input']['User']
    nameDelimiter = os.environ['nameDelimiter']
    if nameDelimiter in userName:
        firstName = userName.split(nameDelimiter)[0]
        lastName = userName.split(nameDelimiter)[1]
    else:
        firstName = userName
        lastName = userName
    domainName = event['input']['Domain']
    upn = event['input']['UPN']
    serviceName = event['input']['Service']
    if serviceName == 'WorkSpaces':
        # Setting WorkSpaces variables
        workspacesDirectoryId = os.environ['directoryId']
        workspacesUsername = upn
        workspacesBundleId = os.environ['bundleId']
        createNewWorkSpace = create_new_workspace(
            directoryId=workspacesDirectoryId,
            username=workspacesUsername,
            bundleId=workspacesBundleId
        )
        return createNewWorkSpace
    elif serviceName == 'Connect':
        createConnectUser = create_connect_user(
            connectUsername=upn,
            connectFirstName=firstName,
            connectLastName=lastName,
            securityProfile=os.environ['securityProfile'], 
            routingProfile=os.environ['routingProfile'], 
            instanceId=os.environ['instanceId']
        )
        return createConnectUser
    else:
        print(serviceName, 'is not recognized...')
        print('Available service names are WorkSpaces and Connect')
        unknownServiceException = {
            'statusCode': 500,
            'body': json.dumps(f'Service name, {serviceName}, is not recognized')}
        raise Exception(unknownServiceException)

class FailedWorkSpaceCreationException(Exception):
    pass

class WorkSpaceResourceExists(Exception):
    pass

def create_new_workspace(directoryId, username, bundleId):
    workspacesClient = boto3.client('workspaces')
    response = workspacesClient.create_workspaces(
        Workspaces=[{
                'DirectoryId': directoryId,
                'UserName': username,
                'BundleId': bundleId,
                'WorkspaceProperties': {
                    'RunningMode': 'AUTO_STOP',
                    'RunningModeAutoStopTimeoutInMinutes': 60,
                    'RootVolumeSizeGib': 80,
                    'UserVolumeSizeGib': 100,
                    'ComputeTypeName': 'VALUE'
                    }}]
                    )
    print('create_workspaces response:',response)
    for pendingRequest in response['PendingRequests']:
        if pendingRequest['UserName'] == username:
            workspacesResultObject = {'UserName':username, 'ServiceName':'WorkSpaces', 'Status': 'Success'}
            return {
                'statusCode': 200,
                'body': json.dumps(workspacesResultObject)
                }
    for failedRequest in response['FailedRequests']:
        if failedRequest['WorkspaceRequest']['UserName'] == username:
            errorCode = failedRequest['ErrorCode']
            errorMessage = failedRequest['ErrorMessage']
            errorResponse = {'Error Code:', errorCode, 'Error Message:', errorMessage}
            if errorCode == "ResourceExists.WorkSpace": 
                raise WorkSpaceResourceExists(str(errorResponse))
            else:
                raise FailedWorkSpaceCreationException(str(errorResponse))
                
def create_connect_user(connectUsername, connectFirstName,connectLastName,securityProfile,routingProfile,instanceId):
    connectClient = boto3.client('connect')
    response = connectClient.create_user(
                    Username=connectUsername,
                    IdentityInfo={
                        'FirstName': connectFirstName,
                        'LastName': connectLastName
                        },
                    PhoneConfig={
                        'PhoneType': 'SOFT_PHONE',
                        'AutoAccept': False,
                        },
                    SecurityProfileIds=[
                        securityProfile,
                        ],
                    RoutingProfileId=routingProfile,
                    InstanceId = instanceId
                    )
    connectSuccessResultObject = {'UserName':connectUsername,'ServiceName':'Connect','FirstName': connectFirstName, 'LastName': connectLastName,'Status': 'Success'}
    return {
        'statusCode': 200,
        'body': json.dumps(connectSuccessResultObject)
        }

Set up log streaming

Create a new CloudWatch Logs subscription filter that sends log data to the Lambda function DS-Log-Stream-Function created in Step 2.

  1. In the CloudWatch console, choose Logs, Log Groups, and select the log group, /aws/directoryservice/<directory id>, for the directory set up in Step 1.
  2. Choose Actions, Stream to AWS Lambda.
  3. Choose Destination, and select the Lambda function DS-Log-Stream-Function.
  4. For Log format, choose Other as the log format and enter “<EventID>4720</EventID>” (include the double quotes).
  5. Choose Start streaming.

If there is an existing subscription filter for the log group, run the following AWS CLI command to create a subscription filter for the Lambda function, DS-Log-Stream-Function.

aws logs put-subscription-filter \

--log-group-name /aws/directoryservice/<directoryid> \

--filter-name NewUser \

--filter-pattern "<EventID>4720</EventID>" \

--destination-arn arn:aws:lambda:<Region>:<ACCOUNT_NUMBER>:function:DS-Log-Stream-Function

For more information, see Using CloudWatch Logs Subscription Filters.

Create a state machine in Step Functions

The next step is to create a state machine in Step Functions. This state machine runs the Lambda function, SFN-New-User-Flow, to create a user in Amazon WorkSpaces and Amazon Connect.

Define the state machine, using the following settings:

  • Name: NewUserWorkFlow
  • State machine definition: Copy the following state machine definition:
{
    "Comment": "An example state machine for a new user creation workflow",
    "StartAt": "Parallel",
    "States": {
        "Parallel": {
            "Type": "Parallel",
            "End": true,
            "Branches": [
                {
                    "StartAt": "CreateWorkSpace",
                    "States": {
                        "CreateWorkSpace": {
                            "Type": "Task",
                            "Parameters": {
                                "input": {
                                    "User.$": "$.Username",
                                    "UPN.$": "$.UPN",
                                    "Domain.$": "$.DomainName",
                                    "Service": "WorkSpaces"
                                }
                            },
                            "Resource": "arn:aws:lambda:{region}:{account id}:function:SFN-New-User-Flow",
                            "Retry": [
                                {
                                    "ErrorEquals": [
                                        "WorkSpaceResourceExists"
                                    ],
                                    "IntervalSeconds": 1,
                                    "MaxAttempts": 0,
                                    "BackoffRate": 1
                                },
                                {
                                    "ErrorEquals": [
                                        "States.ALL"
                                    ],
                                    "IntervalSeconds": 10,
                                    "MaxAttempts": 2,
                                    "BackoffRate": 2
                                }
                            ],
                            "Catch": [
                                {
                                    "ErrorEquals": [
                                        "WorkSpaceResourceExists"
                                    ],
                                    "ResultPath": "$.workspacesResult",
                                    "Next": "WorkSpacesPassState"
                                },
                                {
                                    "ErrorEquals": [
                                        "States.ALL"
                                    ],
                                    "ResultPath": "$.workspacesResult",
                                    "Next": "WorkSpacesPassState"
                                }
                            ],
                            "End": true
                        },
                        "WorkSpacesPassState": {
                            "Type": "Pass",
                            "Parameters": {
                                "Result.$": "$.workspacesResult"
                            },
                            "End": true
                        }
                    }
                },
                {
                    "StartAt": "CreateConnectUser",
                    "States": {
                        "CreateConnectUser": {
                            "Type": "Task",
                            "Parameters": {
                                "input": {
                                    "User.$": "$.Username",
                                    "UPN.$": "$.UPN",
                                    "Domain.$": "$.DomainName",
                                    "Service": "Connect"
                                }
                            },
                            "Resource": "arn:aws:lambda:{region}:{account id}:function:SFN-New-User-Flow",
                            "Retry": [
                                {
                                    "ErrorEquals": [
                                        "DuplicateResourceException"
                                    ],
                                    "IntervalSeconds": 1,
                                    "MaxAttempts": 0,
                                    "BackoffRate": 1
                                },
                                {
                                    "ErrorEquals": [
                                        "States.ALL"
                                    ],
                                    "IntervalSeconds": 10,
                                    "MaxAttempts": 2,
                                    "BackoffRate": 2
                                }
                            ],
                            "Catch": [
                                {
                                    "ErrorEquals": [
                                        "DuplicateResourceException"
                                    ],
                                    "ResultPath": "$.connectResult",
                                    "Next": "ConnectPassState"
                                },
                                {
                                    "ErrorEquals": [
                                        "States.ALL"
                                    ],
                                    "ResultPath": "$.connectResult",
                                    "Next": "ConnectPassState"
                                }
                            ],
                            "End": true,
                            "ResultPath": "$.connectResult"
                        },
                        "ConnectPassState": {
                            "Type": "Pass",
                            "Parameters": {
                                "Result.$": "$.connectResult"
                            },
                            "End": true
                        }
                    }
                }
            ]
        }
    }
}

After entering the name and state machine definition, choose Next.

Configure the settings by choosing Create an IAM role for me. This creates an IAM role for the state machine to run the Lambda function SFN-New-User-Flow.

Here’s the list of states in the NewUserWorkFlow state machine definition:

  • Start—When the state machine starts, it creates a parallel state to start both the CreateWorkSpace and CreateConnectUser states.
  • CreateWorkSpace—This task state runs the SFN-New-User-Flow Lambda function to create a new WorkSpace for the user. If this is successful, it goes to the End state.
  • WorkSpacesPassState—This pass state returns the result from the CreateWorkSpace state.
  • CreateConnectUse — This task state runs the SFN-New-User-Flow Lambda function to create a user in Amazon Connect. If this is successful, it goes to the End state.
  • ConnectPassState—This pass state returns the result from the CreateWorkSpace state.
  • End

The following diagram shows how these states relate to each other.

Step Functions State Machine

Test the solution

It’s time to test the solution. Create a user in AWS Managed Microsoft AD. The new user should have the following attributes:

  • First name: SFNFirst
  • Last name: SFNLast
  • Email: [email protected]
  • Username: SFNFirst.SFNLast

This starts a new state machine execution in Step Functions. Here’s the flow:

  1. When there is a user creation event (Event ID: 4720) in the AWS Managed Microsoft AD security log, CloudWatch invokes the Lambda function, DS-Log-Stream-Function, to start a new state machine execution in Step Functions.
  2. To create a new WorkSpace and create a user in the Amazon Connect instance, the state machine execution runs tasks to invoke the other Lambda function, SFN-New-User-Flow.

Conclusion

This solution automates the initial user registration workflow. Step Functions provides the flexibility to customize the workflow to meet your needs. This walkthrough included Amazon WorkSpaces and Amazon Connect; both services are used to register the new user. For organizations that create a number of new users on a regular basis, this new user automation workflow can save time when configuring resources for a new user.

The event source of the automation workflow can be any event that triggers the new user workflow, so the event source isn’t limited to CloudWatch Logs. Also, the integrated service used for new user registration can be any AWS service that offers API and works with AWS Managed Microsoft AD. Other programmatically accessible services within or outside AWS can also fill that role.

In this post, I showed you how serverless workflows can streamline and coordinate user creation activities. Step Functions provides this functionality, with the help of Lambda, Amazon WorkSpaces, AWS Managed Microsoft AD, and Amazon Connect. Together, these services offer increased power and functionality when managing users, monitoring security, and integrating with compatible AWS services.

Send text messages in Amazon Connect by integrating Amazon Pinpoint

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/send-text-messages-in-amazon-connect-by-integrating-amazon-pinpoint/

Because Amazon Pinpoint is a member of the AWS family, you can integrate it seamlessly with other AWS services. In the past, this blog has looked at the process of integrating Amazon Pinpoint with Amazon Comprehend and Amazon Redshift.

Earlier this week, Michael Woodward, a Solution Architect here at AWS, published a blog post about integrating Amazon Pinpoint with Amazon Connect, our cloud-based contact center service.

Integrating Amazon Pinpoint into Amazon Connect lets you expand the capabilities of your call center systems in several interesting ways. For example, you can use Amazon Pinpoint to send more information after a call ends, or to send a link to an after-call survey.

To learn more about this solution, see Michael’s post on the AWS Contact Center blog.