What is best for your business? 

Contact centers and customer support platforms have historically been managed as part of an organization’s telephony department. As these organizations look to move to an omni-channel cloud-based solution it is important to understand the options available when choosing a path forward. There are three types of solutions,  

• Commercial off-the-shelf (COTS) 
• Highly configurable  
• Highly customizable 

Most customers want and need a solution that meets their exact requirements. However, the more complex the requirements, the less likely it is that a COTS solution is going to come out-of-the box meeting their business needs. To meet the customer’s objectives, the software must either be configured or customized and depending on the solution, only one option may be available to meet the requirements. This brings us to a crucial crossroads: should an organization opt for customization or configuration when implementing a contact center solution? 

The difference between configuration and customization 

• Configuration. This refers to adjusting the settings and preferences of an existing contact center platform to meet the specific needs of an organization, without changing its core code. Configuration is simpler and less costly than customization since it relies on using the software’s inherent features and settings. A configurable system is a COTS solution but allows the owner to easily personalize certain aspects of the system themselves, without the help of experienced software developers.  
• Customization. This involves making changes to the contact center platform code to add new features, change its behavior, or integrate it with other applications. Customization typically requires specialized development skills and an understanding of the software’s architecture. Customization is more complex due to the need for software development efforts. A customized contact center platform is developed specifically for your organization. It is tailor-made for your organization and does everything you require in the way that works best for that team. 

Highly Configurable Contact Center Platform 

Configuration provides a way to adjust the Contact Center settings to align with specific needs, without altering the core codebase. Originations can quickly adapt functionalities based on presets and options provided by the software. This means getting your desired outcome without the intricate process of rewriting or adding new code. Some of the benefits of configuration provides include the following: 

1. Adjusting out-of-box functionality 
2. Adjustable only within certain parameters 
3. Use tools in the application to meet specific requirements without the use of code 
4. Uses software’s inherent features and settings 
5. Can add fields, changes names, or add buttons 
6. Cost & Time Efficiency 
7. Ease of Upgrading 
8. Vendor Support 
9. Can make changes the vendor has anticipated you will want to make in the software 

When to choose a highly configurable contact center platform

1. Your organization is flexible and can leverage supported workflows 
2. Your contact center platform does not need a lot of integration 

Highly Customizable Contact Center Platform 

Organizations that need technology and software solutions that meet unique business requirements and use cases that cannot be obtained out-of-the-box should opt for customization. This also implies that the business is not flexible enough to change its behavior to meet the workflows of a given solution. Some of the benefits of customization provides include the following: 

1. Enhances the software’s capabilities 
2. Make modifications that are unavailable though out-of-box functionality 
3. Changing the code of the software to meet business needs 
4. Write new code in software that meets specific requirements 
5. Customized reports can reveal new perspectives on your data 
6. Involves code changes 
7. Custom to the specific needs of your organization 
8. Software is more aligned to your processes 
9. Can add new features, change behavior, or integration with other applications 

When to choose a highly customizable contact center platform

1. Your organization needs a solution that meets its unique business requirements and use cases are not obtained by COTS alone. 
2. Your contact center platform needs lots of integrations to services such as CRMs (Customer Relationship Management), WFO, outbound campaign management, Voice Mail, SMS, WhatsApp, internal databases, external services like electronic health records, and reporting systems. 
3. Usually, large complex organizations that may be siloed or not flexible enough to change their behavior to meet the workflows of a given solution. 

How can Perficient Help

Perficient is an enterprise digital consultancy, and our Business Unit has deep experience with contact center management platforms. We can deploy COTS, Highly Configurable, and Highly Customizable contact center solutions. We can help configure and customize your contact center platform. We can also help migrate from legacy contact center platforms to any omni-channel cloud-based contact center platform.   

We have seen many originations have difficulty choosing which of these three options is best for them. This may be because COTS solutions can be demonstrated better than Highly Configurable or Highly Customizable solutions. Since COTS are off-the-self all their features are complete. In fact, Highly Configurable options may be easier to demonstrate than Highly Customizable solutions. However, for some organizations the greatest return on investment might be the customizations that lead to unique process improvements that save time across thousands of support agents. Or the custom integrations that enable a holistic digital customer experience platform.  

• Perficient is also a gold partner with most omni-channel cloud-based contact center vendors. So, we can provide unbiased suggestions on which solution is best for your organization. 
• Perficient has deployed, configured, and customized contact center platforms for many small, medium, and large originations.  
• Perficient has a robust set of software accelerators for custom features, plugins, workflows, and integrations that we can use to speed up your customization development time. 

A guiding principle of Perficient’s is to rely on configuration as much as possible and only customize when requirements cannot be met with pure configuration. 

Perficient takes pride in our personal approach to the customer journey. We help enterprise clients transform and modernize their contact center and CRM with platforms like Amazon Connect, Salesforce Service Cloud Voice, and Twilio Flex. 

For more information on how to get the most out of Amazon Connect and Service Cloud Voice, please contact us here. 

Now that the dust has settled, the team has had the chance to Re:flect on the events and announcements of AWS re:Invent 2023. Dominating the conversation was the advancement and capabilities of Generative AI across several AWS Services, while not losing sight on the importance of application modernization and cloud migration. Perficient walked away with 3 key takeaways: 1) Amazon Q 2) Serverless Innovation 3) The Zero ETL Future

1. Amazon Q

Generative AI was the talk of the conference, and no topic was discussed more than Amazon Q. The powerful, new generative AI assistant can be tailored to your business and can be used to generate content and solve problems, or if leveraged with Amazon Connect, now with generative AI capabilities that are powered through Amazon Bedrock, it can allow your agents to respond faster by assisting with suggesting actions or links to relevant articles. AI is here and it isn’t going anywhere, but what might be most important is to ensure it is being used responsibly. “What’s exciting here is that the path to responsibly enabling AI for enterprise is starting to light up…” Steve Holstad, Principal of Cloud said, “We know it’s going to be an ongoing journey for years to come, but the time for a private pilot leveraging your data, based on your unique use cases, is here.” At Perficient, we are at the forefront of the next generation of AI and ML. We’re excited about the progress we’ve made and are looking forward to creating innovative solutions with AWS Q.

Read Zachary Fischer’s, Senior Solutions Architect, blog about exploring the potential of Amazon Q and Perficient Handshake.

2. Serverless Innovation

Serverless computing isn’t new to AWS, as their wide variety of serverless data offerings have been helping customers take advantage of automated methods of setting up infrastructure, real time scaling, and dynamic pricing. Three new AWS serverless innovations for Amazon Aurora, Amazon Redshift, and Amazon ElastiCache build on the work AWS has already been doing for some time.

1. Amazon Aurora Limitless Database: A new feature supporting automated horizontal scaling to process millions of transactions at a speed unlike any before and manage an excessive amount of data in a single Aurora database.
2. Amazon Redshift Serverless: Gather insights in seconds without having to manage data warehouse infrastructure. Leverage its self-service analytics and autoscaling capabilities to better make sense of your data.
3. Amazon ElastiCache Serverless: An innovative serverless solution enabling users to create a cache within a minute and dynamically adjust capacity in real-time according to application traffic trends.

Learn more by reading Shishir Meshram’s, Senior Technical Consultant, blog about Perficient’s ability to help achieve a serverless infrastructure.

3. The Zero ETL Future

Historically, to connect all your data sources to find new insights, you’d need to “extract, transform, and load” (ETL) information in a tedious manual effort. AWS announced several new integrations as part of their continued commitment to a, “zero ETL future,” so users can access data when and where they need it. In his keynote presentation, Dr. Swami Sivasubramanian, Vice President of Data and AI at AWS, said, ““In addition to having the right tool for the job, customers need to be able to integrate the data that is spread across their organizations to unlock more value for their business and innovate faster. That is why we are investing in a zero-ETL future, where data integration is no longer a tedious, manual effort, and customers can easily get their data where they need it.”

Learn more about these integrations, and find out how like AWS, you can work your way toward a “zero ETL future.”

This was just the tip of the iceberg of what was discussed at AWS re:Invent and Perficient is excited to be in the thick of it! Join us on this journey of discovery. Let’s see what we can build together.

Amazon Connect contact flows and Twilio studio flows are designed to be user-friendly. However, clients often have trouble understanding flows because long widget text is truncated, unessential technical detail is not filtered out, and transition arrows often overlap or are obscured behind widgets.

IVR-to-Flowchart Tool

The IVR-to-Flowchart tool generates flowcharts to present a high-level overview of flows to clients.

How it all works

The tool uses JavaScript and Mermaid which is a charting language. It parses flows in JSON format, converts them to strings of Mermaid code, and writes those strings to a file in HTML and Markdown format. The tool is triggered via the command line, and it can receive JSON flow from exported files or via Connect and Twilio APIs. It also allows users to select what flows to convert and the flowcharts can be previewed and exported as PDF, PNG, or SVG, or they can be printed.

Description of Features

Each flowchart section is indicated as follows:

• Error transitions – red lines
• Long ARNs and SIDs – excluded to reduce clutter
• Handoffs to outside processes like queues and function invocations – dotted lines

Widget types are distinguished by the following node shapes:

• Terminal widget – stadium shape
• Connector – a circle
• Data storage – cylinder
• Decision – rhombus
• Setting logging or recording behavior – rectangle
• Outputting a message to the participant – a rectangle with rounded edges

Node shapes were chosen based on common flowchart symbols and their associated meanings.

How the Tool Works

Settings are configured via an .env file. Also, you can configure whether to import from a file or via API and whether the flows are from Amazon Connect or Twilio.

When reading Amazon Connect flows from a file, the JSON object must be exported from the new user interface released in September 2022 because the JSON object is formatted differently than the old user interface. To trigger the tool, run npm run ivr2f in the command line from within the project directory.

How to Use the Output

The Markdown Preview Mermaid Support VS Code extension can be used to render quick previews of the flows in Markdown format. Flows in HTML format can be served using the Live Server VS Code extension and saved in PDF or printed format. Flowcharts can be saved in the PNG or SVG format by screenshotting or pasting Mermaid code into Mermaid’s Live Editor and exporting the flow.

Design Choices and Implementation

Mermaid offered the convenience of a prebuilt charting language with a sleek appearance; however, this posed a few challenges. There is no built-in text wrapping, so text wrap had to be manually implemented by placing <br> tags in the correct positions throughout the text. Reserved characters in Mermaid like quotes and parentheses had to be removed from the widgets’ content before adding them to the flowchart. Additionally, Mermaid offers limited customizability, so text size and node positioning are not optimized for readability in every flowchart. In retrospect, I would have looked for a charting tool with more customizability; mxGraph may be a promising alternative.

Conclusion

The IVR-to-Flowchart tool is a useful tool that simplifies complicated contact flows for clients and the flowcharts can be previewed and exported in different formats. The tool uses JavaScript and Mermaid, a charting language, to parse JSON flow and convert it into a string of Mermaid code. The tool’s different node shapes and line styles make it easy to distinguish different widget types and flowchart sections.

Amazon Connect offers OpenSearch as a service that can be used in a contact center to build reporting dashboards that supervisors can use to review agent activity and availability, agent performance and status, inbound and outbound calls, SMS, chats, and much more. In this blog, I will walk through how OpenSearch integrates and how it can benefit your contact center. 

What is OpenSearch

OpenSearch is an open-source search and analytics engine for log analytics, real-time application monitoring, and clickstream analysis. Amazon OpenSearch Service is a managed service that makes it easy to deploy, operate, and scale OpenSearch clusters in the AWS (Amazon Web Services) cloud.
Read this article to learn more about AWS OpenSearch Service. 

What can you do with Amazon OpenSearch

By using the power of the OpenSearch service, users can create interactive dashboards which reflect historical and live data in a statistical, easy-to-understand format.

In a contact center application, different activities are running simultaneously – like ongoing calls, chats, integration with third-party applications, voicemails, and similar actions. So, to understand what you can do with OpenSearch, here is a list of some of the actions OpenSearch can visualize. 

1) Agent’s activities like status, availability, and performance
2) Real-time and historical interactive information like customer name, disposition, a sub–disposition
3) Average call handle time, queue time, wrap time, and similar information
4) Platform where the chat originated from – like web chat, Facebook, or Instagram. 

 Create a Visualization in OpenSearch 

To create a visualization for supervisors, the following parameters must be configured:

1. Access the OpenSearch dashboard home page.
   
2. Navigate to Stack Management to create Index patterns which will be required for creating dashboards and visualizations.
   
3. Navigate to Visualizations for creating visualizations like Metrics or Bar graphs. We are creating a visualization that will show the names of the customers and their feedback depending on the survey provided during the chat session. Click Create Visualization.
   
   
4. OpenSearch provides a list of default composers to create the visualization, and we will select a pie visualization.
   
   
5. Select Ctr_surveys* to create the pie visualization.
   
   
6. Click Add buckets and select the option Split Table.
   
   
7. Configure the following values:

– For the Aggregation drop-down select Terms
– For the Field drop-down select surveyAnswer.asnwer.keyword
– Click Update 

8. Provide a name for your visualization and then save the configuration. 

9. Create one more visualization for displaying the names of the agents along with their current status. This way, the viewer can see who can accept new tasks and what is the duration of the agent’s current status.  

10) Configure the following values:
– For the Aggregation drop-down select Terms
– For the Field drop-down select IsRoutable.keyword
– For Order by select Metric Last Updated
– For Order select Descending
– For Size select 100
– Click Update             

11) Once you save the configuration, navigate to the Dashboard link. 

12)Click Create dashboard. 

13) Click the Add an existing link. 

14) Search for the visualization you have created (example: NumberOfAgents)  

15) Search for the second visualization you have created (Example: Name & Status)

16) Save the dashboard by entering a unique name and a description. 

17) OpenSearch dashboard is now ready for use. 

18) Click Share dashboards to configure the dashboard in an application.

Now, this iFrame embed code can be used to import the dashboard into the contact center application. 

Why choose OpenSearch

In a nutshell, AWS OpenSearch service provides solutions for a contact center to manage and maintain the state of the application as well as the information necessary to make data-driven decisions for the organization.

If you are interested in OpenSearch and need some guidance on maximizing your contact center’s efficiency, we can help. At Perficient, we are an APN Advanced Consulting Partner for Amazon Connect which gives us a unique set of skills to accelerate your cloud, agent, and customer experience.  

Perficient takes pride in our personal approach to the customer journey where we help enterprise clients transform and modernize their contact center with platforms like Amazon Connect. 

For more information on how Perficient can help you get the most out of OpenSearch and Amazon Connect, please contact us here.  

Service Cloud Voice Permissions and Security Explained

If you’re familiar with using Salesforce technologies to track customer data, keep a record of client feedback, and track previous interactions with your client base, then you might also be familiar with a platform called Service Cloud Voice.

If not, Service Cloud Voice is a combination of both the Amazon Connect platform to handle phone calls, and the Salesforce platform to handle CRM duties.

Essentially what this does for a business is two things:

• It streamlines the process of creating a fully integrated contact center software capable of saving your data and re-using it at a later time.
• It helps the business improve the quality of service by making its infrastructure highly resilient and highly available at all times. For some businesses, even a couple of minutes of downtime can come at a great cost

Great, so now what? How do we use Service Cloud Voice and is there anything special we need to do?

Well, the short answer is no! All you would have to do is contact a Salesforce representative so they can set up your instance.  But, once the Salesforce environment is ready, another question appears – how does this all work? How do these two platforms communicate with each other?

In order for these platforms to communicate, AWS, or Amazon Web Services, requires that each service embedded into AWS have a set of permissions to access the services deployed into our Service Cloud Voice instance.

Service Cloud Voice Tenant Stack

So how is this all set up? Let’s start with what’s known as that Service Cloud Voice Tenant stack.

The Service Cloud Voice Tenant stack is a series of services deployed to our contact center that contain policies that give permission to those services to access other services within the same stack. This stack also gives our contact center the ability to write to databases deployed within the stack, create logs and save them for when they need to be accessed, and provide data to our contact center which is deployed in another AWS instance.

This stack consists of permission policies called roles, which are policies attached to certain serverless services to allow them to carry out their purpose as a service. A quick summary of each role:

• Contact Trace Record Role Policy: A policy that allows caller data to be synced from one platform to another. This role also gives our data sync function the ability to read and write data from Salesforce and other resources deployed in AWS, such as CloudWatch logs–a very important feature for storing data to gain insight into business trends.
• Contact Lens Consumer Role Policy: Contact lens is a resource that analyzes speech data and gathers insights based on that data. Things like customer feedback, contextualizing the sentiment behind the conversation, and categorizing contacts. This role gives Contact lens the ability to log all of this data to CloudWatch logs and trigger other functions within Service Cloud Voice.
• Contact Lens Streaming Role Policy: This role has the same permissions as the Contact lens function policy, but this role allows Contact Lens to log multiple streams of data to and from Salesforce on multiple call instances.
• Identity Provider Lambda Policy: This is a policy that allows our Identity Provider serverless function to create users that will be able to access our contact center. This policy also provides an Identity Provider ID number so that our provider can be identified when the user is created.
• Invoke Salesforce API Role: This policy can be attached to a resource in AWS which allows it to get, write, and read SSM parameters (keys that are required to access certain services, such as databases or digital storage spaces) on all services throughout AWS. This role also gives the invoke Salesforce API function write permissions to CloudWatch logs.
• Invoke Telephony Integration API Role: This allows our Amazon Connect instance to be used within our Salesforce instance. This is used to accept, reject, and queue calls as you would in a regular contact center. The policy gives the same permissions as the InvokeSalesforceRestApiFunctionRolePolicy resource, which allows the reading and writing of SSM parameters. This role also gives permission to write to CloudWatch logs.
• KVS Consumer Trigger Role Policy: This is a policy attached to the service that triggers the transcription of a call. This policy allows for the invocation of async and synchronous Lambda functions from all resources in AWS as well as creating logs in CloudWatch logs.
• KVS Transcriber Role Policy: This policy gives permission to the KVS transcriber to pull the call in Amazon Connect and transcribe it. This role gives permissions to read and write cloud logs, delete transcriptions, filter transcriptions from sensitive data, list which words are being filtered, and of course, initialize the transcription service itself. This role also holds permissions for obtaining video from Kinesis as well. This role gives permission to read and list SSM parameters and update contact information in Amazon Connect.
• Provisioning Role Resource: This role is more of an Admin role in AWS. This role is a general policy that allows the assigned user to provide any and all resources for the following services: Amazon Connect, Lambda, S3, Kinesis, CloudFormation, SSM, IAM, Secrets Manager, DS, KMS, CloudWatch Events, SNS, General CloudWatch, CloudWatch logs, and CloudTrail.
• S3 Role Policy: This role allows our S3 buckets (digital storage) to get objects, list other S3 buckets, and decrypt KMS keys. This role also comes with an assigned ID to refer to that specific bucket in which the role is being defined. This goes for all resources within AWS no matter where the data is coming from.
• SSM Lambda Execution Role Resource: This role allows our Lambdas to handle any action as it pertains to our SSM service. This role also allows Lambda to create and write CloudWatch log streams and events. This role also comes with a Sid which refers to the specific Lambda this role is assigned to. This is allowed from all resources on AWS.
• Trail Log Group Role Policy: This role consists of a CloudTrail policy that allows CloudTrail to write events, create log streams, and push those logs to the specified log group located in the N. Virginia us-east-1 Availability Zone in AWS.

In a nutshell, the Service Cloud Voice Tenant Stack is deployed before any other stack. This is because the permissions and roles need to be attached to the other contact center resources once they are deployed.

If you have an AWS account, you view these resources deployed in the IAM and S3 services section.

This is the beginning stage of when we initially activate our Service Cloud Voice instance through Salesforce. This graph may simplify the order in which our platform is deployed.

This graph only represents one part of how our Service Cloud Voice application is built and how permissions are shared between services in the application. But this piece is essentially about learning how these services can connect and share information, which might be the most meaningful part of using Service Cloud Voice as a contact center/ CRM Solution.

Service Cloud Voice is the “one-stop shop” platform for any business looking to implement a cost-effective, full-stack contact center web application capable of storing large amounts of data, transcribing and saving call information, tracking metrics for enhanced insight into customer issues, and so much more!

Our consultant team of Amazon Connect experts worked together to creatively solve an interesting business use case for one of our clients. We were presented with a scenario that required business users to dynamically update their IVR menus, while not being able to directly access the call center system. This business team also needed the ability to quickly and frequently change the order of their menu options.

This feature couldn’t be owned by one group, and it needed to support the shared responsibility of three different teams with undesirable permissions:

• A security team that had requirements that prevented the business team from directly accessing their contact center
• A business team of product owners (line of business (LOB) owners) with full control over what the IVR menus should offer, and the order that the options would appear in
• an IT team that supported the Amazon Connect contact center and had the authority to modify the IVR menus. This team desired minimal maintenance responsibilities for the menus and was unable to support the frequency of menu change requests from the business team

Our development team offered a solution that satisfied all three teams:

• We worked within the constraints requested by the security team and had a strong understanding of the Amazon Connect contact center permissions. We also knew that there was a gap that could not be addressed via configurations.
• We identified an alternative method to give the business team the control they needed – upload a document that specified the menu order and allow messaging associated with the menus.
• We provided a minimal-maintenance path to allow for frequent updates to the menus via smartly defined Amazon Connect contact flows.

Discovering and Addressing the Gaps

We worked diligently with the LOB owners to understand how their customers interacted with the contact center and how they intended to deliver information and self-service capabilities to those callers. We were quickly able to identify and recommend the following adjustments and improvements to the IVR:

• optimizing user experience
• removing customer-trapping scenarios
• streamlining the caller’s path towards an agent “self-serve to agent pathing” experience.

Furthermore, in coordination with the IT team, we established a way for the business team to upload a spreadsheet to an AWS S3 bucket related to their menu configurations. We also found a way for the IT team to maintain functional documentation through the well-defined Amazon Connect contact flows.

We made the upload of the spreadsheet as flat as possible while also containing all LOB owner inputs.  The upload consisted of the following fields:

• MenuId: Specify the contact flow / business context that menu option is related to
• MenuOrder: Specify the order/position of the option in the menu
• MenuOption: The special unique identifier of the items in the menu (Process Payment, Agent, etc.)
• Prompt: Specify the message that the customers will hear

See below for sample data:

Leveraging additional Amazon Web Services

Our development team created a trigger on the S3 bucket to activate a process that would transform the spreadsheet into multiple records that would be loaded into the DynamoDB table, with data validation checks and data cleaning processes. We provisioned Lambdas to retrieve, transform, and output these data points to TTS menu prompts.

The menu choices are then dynamically retrieved and processed in the Amazon Connect contact flow that the IT team maintained:

Contact Flow Configurations

1. The IVR system collected and populated contact attributes to establish the customers’ context and reason for calling in, thus identifying the menu that should be offered.
2. We provided a Lambda function that would fetch the menu details based on the established caller context (e.g.: paymentmenu, customerservice). This function would be a multi-step process that includes the following actions: fetch the menu details from the database, stitch the items together in an ordered fashion (0-9), and provide a message prompt that activates in
3. After receiving the customer’s input (0 through 9), the contact flow would pass the customer input, add menu context to a Lambda function to resolve the choice selected (ProcessPayment, PaymentFAQ, Agent, etc.), and send it back as an external contact attribute.
4. The contact flow block would then use this external contact attribute to route the caller to the correct path and/or flow.

Final Results

After completion of this development experience:

• The business team had control over their IVR menu choices and a way to update their menu orders without being restricted by the support team’s schedule.
• The contact center IT team, through coordination with the business team, was able to minimize their maintenance and support needs for Contact Flow Designer updates.
• The security team could uphold their goals of minimizing access, giving security roles and functionality only as required.

If you are struggling to find a solution that fits your unique and complex organizational structure and need some guidance on maximizing your contact center’s efficiency, we can help. At Perficient, we are an APN Advanced Consulting Partner for Amazon Connect which gives us a unique set of skills to accelerate your cloud, agent, and customer experience.

Perficient takes pride in our personal approach to the customer journey where we help enterprise clients transform and modernize their contact center and CRM experience with platforms like Amazon Connect, Service Cloud Voice, or our own personalized offering: Perficient Amazon Connect Experience.

For more information on how Perficient can help you get the most out of your contact center needs, please contact us here.

To kick off the largest cloud conference of the year, Amazon Web Services (AWS) CEO Adam Selipsky delivered his opening keynote at AWS re:Invent in Las Vegas. Selipsky welcomed 50,000 in-person attendees and 300,000 virtual attendees from around the globe. He encouraged enterprises to move to the cloud, citing the dramatic cost savings it offers, which is critical in today’s world of economic uncertainty.

“The cloud is more cost-effective, and many customers are saving 30% or more,“ he said. “In times of uncertainty, it actually can be tempting to cut back and slow down, but when it comes to cloud, many of our customers know that they should be leaning in precisely because of economic uncertainty…if you’re looking to tighten your belt, the cloud is the place to do it.”

Selipsky gave some examples of companies that saved money by moving to the cloud. Carrier saved 40% in costs by migrating to AWS Cloud. Gilead saved $60 million, and AGCO has 78% lower costs in the cloud. He also mentioned how Airbnb was better able to weather the downturn in demand in the hospitality industry during the COVID-19 pandemic because they were an early adopter of the cloud, which allowed them to operate more efficiently and be more agile.

Selipsky went on to talk about more of the outcomes of moving to the cloud aside from the cost savings aspect:

“We are so passionate about what we do at AWS because we see what you are doing with the cloud,” he continued. “Enterprises look to the cloud to innovate in ways they haven’t been able to do before. Entrepreneurs bring their dreams to the cloud and change the future. Governments rely on the cloud to provide critical services to constituents. Financial services and pharmaceuticals, researchers and retailers, freight carriers, phone carriers, NGOs, energy firms, entertainment studios, the list goes on and on.”

Here are some of the big announcements that CEO Adam Selipsky made in his re:Invent keynote:

1. Amazon Redshift Integration for Apache Spark

If you are using AWS analytics and machine learning (ML) services—such as Amazon EMR, AWS Glue, and Amazon SageMaker—you can now build Apache Spark applications that read from and write to your Amazon Redshift data warehouse without compromising on the performance of your applications or transactional consistency of your data.

Read more about Perficient’s Amazon Redshift offering at Amazon Redshift Delivery Services / Perficient, Inc.

2. New Healthcare and Life Science Services

Amazon Omics is a new purpose-built service that helps healthcare and life science organizations store, query, and analyze genomic, transcriptomic, and other omics data and then generate insights from that data to improve health and advance scientific discoveries.

Learn more about some of the AWS Cloud use cases and solutions that Perficient already offers healthcare and life science organizations, AWS Cloud Solutions: Use Cases in Healthcare – Perficient Blogs

3. Amazon Aurora zero-ETL integration with Amazon Redshift

Amazon Aurora now supports zero-ETL integration with Amazon Redshift, to enable near real-time analytics and machine learning (ML) using Amazon Redshift on petabytes of transactional data from Aurora. Within seconds of transactional data being written into Aurora, the data is available in Amazon Redshift, so you don’t have to build and maintain complex data pipelines to perform extract, transform, and load ETL operations.

With near real-time access to transactional data, you can leverage Amazon Redshift’s analytics and capabilities such as built-in ML, materialized views, data sharing, and federated access to multiple data stores and data lakes to derive insights from transactional and other data.

4. Announcing Amazon OpenSearch Serverless

Amazon OpenSearch Service now offers a new serverless option, Amazon OpenSearch Serverless. This option simplifies the process of running petabyte-scale search and analytics workloads without having to configure, manage, or scale OpenSearch clusters. OpenSearch Serverless automatically provisions and scales the underlying resources to deliver fast data ingestion and query responses for even the most demanding and unpredictable workloads. With OpenSearch Serverless, you pay only for the resources consumed.

5. New Amazon Connect Features

Selipsky had two announcements for Amazon Connect. First, Contact Lens for Amazon Connect now provides a set of agent performance evaluation capabilities that enable contact center managers to create evaluation forms with criteria (e.g, adherence to talk scripts or compliance with sensitive data collection practices) that can be scored using Contact Lens’ ML-powered conversational analytics. Managers can assess agent performance alongside contact details, recordings, transcripts, and summaries, without the need to switch applications. These capabilities allow managers to assess more agent/customer interactions while reducing the amount of time they spend identifying performance issues and coaching agents to perform their best.

Secondly, Amazon Connect agent workspace now provides a step-by-step experience that guides agents by identifying customer issues and recommending subsequent actions. With Amazon Connect, you can create workflows that walk agents through custom UI pages that suggest what to do at a given moment during a customer interaction. Detailed step-by-step guides increase agent productivity and decrease training time.

Interested in learning more about Perficient’s Amazon Connect capabilities, learn more at our web page Perficient Amazon Connect / Perficient, Inc.

6. AWS Sustainability

Selipsky highlighted Amazon’s sustainability efforts as well, sharing that Amazon is now the world’s largest purchaser of renewable energy with a goal of being 100% renewable by 2025. He also mentioned Amazon’s goal of being water positive by 2030 – i.e. returning more water into the system than Amazon consumes.

Perficient + AWS

Perficient is a certified Amazon Web Services partner with more than 10 years of experience delivering enterprise-level applications and expertise in cloud platform solutions, contact center, application modernization, migrations, data analytics, mobile, developer and management tools, IoT, serverless, security, and more. Paired with our industry-leading strategy and team, Perficient is equipped to help enterprises tackle the toughest challenges and get the most out of their implementations and integrations. To learn more, check out our AWS Partner Page Amazon Web Services Trusted Partner / Perficient, Inc.

The ability to provide exceptional customer experiences when interacting with service agents is already enhanced by natively integrating Salesforce with Amazon Connect in Service Cloud Voice. Why stop there? By Integrating Einstein Next Best Action into the agents’ Voice Call Console, Salesforce uses AI (Artificial Intelligence) to empower agents with the knowledge and automation you already have. The Conversation Toolkit API (Application Programming Interface) helper is a console integration for live agents. It deploys scaffolding to use voice, chat, and messaging transcripts to guide agents toward the most relevant knowledge articles and workflows in real time. Gone are the days of agents searching for information or needing to memorize complex processes. Let Einstein automate these processes for your organization and empower your agents to supply unparalleled customer service.  

Implement Einstein Next Best Action 

Update Voice Call Lightning Page 

To implement Einstein Next Best Action, you will need to update the Lightning Record Page for the Voice Call Object.  

• Navigate to the Object Manager from Setup and select Voice Call.  
• Open the Lightning Record Pages on the left utility panel.  
• Open the Voice Call – Default and select edit.  
• This will open the Lightning App Builder for the Voice Call Record Page.  
• Add the Einstein Next Best Action component if it is not currently displayed on the page 
• Search for Service – Conversation Toolkit Helper and place the customer component anywhere on the page.  
• This is a hidden component and is not visible to the agent or administrator, making placement arbitrary.  

Once these items are placed, you are ready to move on to installing the Flow Article Viewer. 

Install Knowledge Article Viewer 

To display knowledge articles in flow screens you need the flow knowledge article viewer from Salesforce AppExchange. Install the component by clicking Get it now. Follow the prompts to sign in and install the component in your Salesforce environment. After a few moments, the component will be installed, or you may receive a message that the app is taking a long time to install. This is normal, and once you have received notification that the component is installed you will be ready to use this component in a flow.  

Create Flow Screen for a Knowledge Article 

You are going to create a flow that displays knowledge articles you already have in your knowledge base. 

•  Navigate to Flows from the setup menu and create a new screen flow. 
•  Add a new screen object.  
• Insert the Knowledge Article Viewer custom component. You must only supply an API Name and either an Article ID or Article Name. Do not confuse Article ID with Article Number because they are not the same.  
• To use the article name: 
• Create a constant resource to store the article name you want the agent to see on this screen.  
• For the constant’s value, you must enter the article name exactly as it appears in the knowledge article.  
• You can leave the rest of the fields in the component as default. As with creating any other screen flow in Salesforce, label your screen and fill out the rest of the required information. Removing the header and footer from the knowledge article screen makes for a cleaner appearance but is not necessary. Activate and save your flow.  
• Once activated and saved, you can preview your flow to see what the agent will see when running this flow.  

When you have completed this process, you are ready to create a recommendation to point to this flow. 

Create a Recommendation for Flows 

The agent will see recommendations in the Einstein Next Best Action panel.  

• Create a new recommendation by opening the app launcher and searching for recommendations.  
• The name, description, acceptance, rejection labels, and image will all be visible to an agent, so make them meaningful. Salesforce recommends using an image that is 1000x380px.  
• For the Action field, select the flow you just created.  
• Categorize your recommendation.  
• Select agents who should be able to access this recommendation by configuring the targeted audience.  
• Optionally, you can select a ranking to prioritize similar recommendations.  

To recommend a workflow instead of a knowledge article, duplicate this process, and set the action to the desired workflow. Keep in mind certain workflows must be launched from specific screens.  

Note: Recommendations are active by default. However, if the action for the recommendation is a flow that has not been activated, the recommendation cannot be set to active.  

Create Conversation Helper 

The conversation helper is a custom object that is deployed by the Conversation Toolkit API, allowing Einstein Next Best Action to recommend flows to agents based on keywords in transcripts. To create a conversation helper: 

• Open the app launcher and search for the conversation helpers object.  
• Create a new conversation helper by giving it a name and value.  
• The value property is important because when the value is found in the transcript it will trigger Einstein to display the recommended action in the Next Best Action panel.  
• For value, choose if you want to monitor the agent’s and/or customer’s transcripts. 
• Finally, set the conversation helper to Active. 

Results 

The results in the image above capture just how easy it is for us to allow your agents to access knowledge and process automation in real-time. A drawback to this approach is the manual process of creating recommendations and conversation helpers; however, with article and flow versioning, no changes need to be made to recommendations or helpers. Even if you update the articles or flows they are related to, the added configuration is not necessary. At Perficient, we are an APN Advanced Consulting Partner for Amazon Connect and a Salesforce Consulting Partner, giving us a unique set of skills to accelerate your cloud agent and customer experience.  

Perficient takes pride in our personal approach to the customer journey. We help enterprise clients transform and modernize their contact center and CRM with platforms like Amazon Connect and Service Cloud Voice.  

For more information on how to get the most out of Amazon Connect and Service Cloud Voice, please contact us here. 

Written by Gerald Frilot. Published by Tony Harper.

AWS CloudWatch is a unified monitoring service for AWS services and your cloud applications. Using AWS CloudWatch, you can:

• monitor your AWS account and resources
• generate a stream of events
• trigger alarms and actions for specific conditions
• manually export CloudWatch log groups to an Amazon S3 bucket

Exporting data to an S3 bucket is an important process if your organization needs to report on CloudWatch data for a period greater than the specified retention time. After the retention time expires, log groups are permanently deleted. In this case, manual exports alleviate risks associated with data loss but one major disadvantage of manually exporting logs, as defined in AWS Docs, is that each AWS account can only support one export task at a time. This operation is feasible if you only have a few log groups to export but can become very time consuming and prone to errors if you need to manually export more than 100 log groups periodically.

Let’s use a step-by-step solution to automate the process of exporting larger log groups to an S3 bucket using a Lambda instance to direct CloudWatch event-based traffic. You can use an existing S3 bucket or create a new S3 instance.

Amazon Simple Storage Service (S3)

Log into your AWS account, search for the Amazon S3 service, and follow these steps to enable the simple storage service:

1. Select a meaningful name
2. Select an AWS Region
3. Keep all defaults
   1. ACLs disabled (Recommended)
   2. Block all public access (Disabled)
   3. Bucket Versioning (Disable)
   4. Default encryption (Disable)

• Select Create Bucket (This creates a new S3 instance for data storage)

Once the bucket is created, you will need to navigate to the Permissions Tab:

Update the Bucket Policy that allows CloudWatch to store objects to the S3 bucket. Use the following to complete this process:

AWS Lambda

The S3 bucket is now configured to allow object write-through from our CloudWatch service. Our next step is to create a Lambda instance that houses the source code for receiving CloudWatch events and storing them to our S3 instance.

Search for the Lambda service in your AWS account, navigate to functions, and select Create Function.

Follow these steps:

1. Select the Author from scratch template

2. Under Basic Information, we need to provide:
   1. Function name
   2. Runtime (Python 3.9)
   3. Instruction set Architecture (x86_64 default)

3. Keep the defaults under execution role and advanced settings dropdown, and select Create Function

Python Script (Pseudocode)

Python script imports the boto3 aws-sdk module for creating, configuring, and managing AWS services along with an os and time module. We instantiate a new instance of CloudWatch logs and a new instance of the AWS Systems Manager Parameter Store. Within the lambda handler method, we initialize an empty object and two empty arrays. The empty object may be useful if we only care to target a specific log group name prefix.

Our first array targets all log groups, and the second array is used to determine which log groups to export. We then check if the S3 bucket environment variable exists, if not we return an error. Otherwise, we enter a series of loops. The first loop will invoke the AWS DescribeLogGroups method and add them to our initial log groups array. Once all log groups are added, we begin our second loop that searches for the ExportToS3 tag in the initial log groups array. If this tag exists, we update the second array with log groups that need to be exported.

The final loop iterates over the second array and uses the log group name as a prefix for the Parameter Store search. If a match is found, we then check the time value stored and compare it to our current time. If 15 minutes have elapsed, we update the S3 bucket with our data and then update the Parameter Store value with the current time.

4. Select Deploy to save our code changes and then navigate to the Configuration tab

5. We now need to create an environment variable that references the S3 bucket where our CloudWatch events will be stored

Note: Key needs to be set to S3_BUCKET and the value set to the name of your S3 bucket. This is referenced in the lambda code and will need to be set prior to invoking this function.

6. Our next course of action is to update the lambda’s basic execution role. This allows our lambda permission to perform read/update operations on separate AWS services. Use the following to complete the process:

{

    “Version”: “2012-10-17”,

    “Statement”: [

        {

            “Sid”: “VisualEditor0”,

            “Effect”: “Allow”,

            “Action”: [

                “logs:ListTagsLogGroup”,

                “logs:DescribeLogGroups”,

                “logs:CreateLogGroup”,

                “logs:CreateExportTask”,

                “ssm:GetParameter”,

                “ssm:PutParameter”

            ],

            “Resource”: “arn:aws:logs:{your-region}:{ your aws account number}:*”

        },

        {

            “Sid”: “VisualEditor1”,

            “Effect”: “Allow”,

            “Action”: [

                “logs:ListTagsLogGroup”,

                “logs:CreateLogStream”,

                “logs:DescribeLogGroups”,

                “logs:PutLogEvents”,

                “logs:CreateExportTask”,

                “ssm:GetParameter”,

                “ssm:PutParameter”,

                “s3:PutObject”,

                “s3:PutObjectAcl”

            ],

            “Resource”: “arn:aws:logs:{your region}:{your aws account number}:log-group:/aws/lambda/{ Function Name }:*”

        },

        {

            “Sid”: “VisualEditor2”,

            “Effect”: “Allow”,

            “Action”: “ssm:DescribeParameters”,

            “Resource”: “*”

        },

        {

            “Sid”: “VisualEditor3”,

            “Effect”: “Allow”,

            “Action”: [

                “ssm:GetParameter”,

                “ssm:PutParameter”

            ],

            “Resource”: “arn:aws:ssm:{ your region }:{aws account number}:parameter/log-exporter-*”

        },

        {

            “Sid”: “VisualEditor4”,

            “Effect”: “Allow”,

            “Action”: [

                “s3:PutObject”,

                “s3:PutObjectAcl”,

                “s3:GetObject”,

                “s3:GetObjectAcl”,

                “s3:DeleteObject”

            ],

            “Resource”: [

                “arn:aws:s3:::{aws bucket name}”,

                “arn:aws:s3:::{aws bucket name}/*”

            ]

        }

    ]

}

AWS Parameter Store

Now that the S3 bucket and the Lambda are completely set up, we can turn to the AWS service called Parameter Store which provides secure, hierarchical storage for configuration data management and secrets management.  This service is for reference only as our lambda method takes care of the initial setup and naming conventions for this service. When a CloudWatch event is triggered, our code references Parameter Store to determine if 15 minutes have elapsed since we last stored data in our S3 bucket. The first invocation will set the parameter store value to 0 and then check/update that value with every recurring event on 15-minute boundaries. Data is never overwritten, and our initial setup runs flawlessly without any user intervention.

Lambda Triggers

We are going to redirect back to our Lambda instance and make one final update under the Configuration > Triggers tab

1. Select Add trigger
2. Fill in the following fields and then select Add
   1. CloudWatch Logs (click the caret to select the dropdown menu and select the right service)
   2. Log group
   3. Filter name

3. Repeat steps 1 and 2 for each log group required for S3 storage.

Note: The previous step and the one following are executed in this order to avoid writing data to the S3 bucket for an active environment.

CloudWatch Tags

Our code will only export log groups that contain a tag and this operation can only be done from a terminal. Refer to AWS CLI to learn more about how to set up command line access (CLI) for your AWS environment. Once command-line access is complete, we can set up each log group needing export via the command line. Use the following command to complete this process:

aws –region us-west-2 logs tag-log-group –log-group-name /api/aws/connect –tags ExportToS3=true

We are now automatically set up to export CloudWatch log groups to our S3 bucket!

AWS Solution Delivered

We’ve chosen AWS Services because of its flexibility and ability to drive results to the market in a timely manner. By directing our attention to AWS Cloud, we were able to effectively export data to an S3 bucket driven by CloudWatch events.

Contact Us

At Perficient, we are an APN Advanced Consulting Partner for Amazon Connect which gives us a unique set of skills to accelerate your cloud, agent, and customer experience.

Perficient takes pride in our personal approach to the customer journey where we help enterprise clients transform and modernize their contact center and CRM experience with platforms like Amazon Connect. For more information on how Perficient can help you get the most out of Amazon Lex, please contact us here.

Perficient’s Amazon Connect Experience (PACE) solution amplifies the power of Amazon Connect with several added features and managed services. As we continue to develop and customize our software to your needs, we will post blogs with new features and processes we have added, changed, or fixed. To learn more about PACE, visit our dedicated landing page or view our listing on the AWS Marketplace!

Video and Screen Sharing:

Are you looking for a solution that seamlessly enables video and screen sharing for your contact center? Look no further. PACE now supports video and screen sharing out of the box. Customers can request that agents escalate chat conversations to virtual meetings that enable video and screen sharing. In addition, agents can proactively escalate active chat conversations to virtual meetings. Regardless of who triggers the escalation, agents and customers can talk to each other, see each other, and even share their screens.

Microsoft Teams Outbound Chat:

Earlier this year, we announced the first native Microsoft Teams chatbot for Amazon Connect, enabling Teams users to seamlessly message contact center agents. With this release, contact center agents can now trigger outbound chats to Teams users while taking advantage of other PACE features such as canned responses. Best of all, supervisors can gain valuable insights into these interactions by reviewing them in the PACE real-time dashboards and historical reports.

Microsoft Teams Status Sync:

PACE already enables agents to sync their Microsoft Teams statuses to Amazon Connect. Now, we also allow agents to sync their Amazon Connect statuses to Microsoft Teams, resulting in bi-directional sync between the two platforms. This innovative approach will increase productivity and reduce unwanted interruptions for agents who are Microsoft Teams users.

To help administrators manage this new functionality, we’ve added support for configuring which Microsoft Teams activities will prevent agents from being available for contact center interactions and which PACE activities will set agents to busy in Microsoft Teams. In addition, administrators will be able to better manage the underlying integration with enhancements such as the secret expiration indicator.

And There is More!

This release improves the underlying Dashboard, Lambda, and WebSocket frameworks for enhanced stability and cost savings. It enables supervisors to configure Microsoft Teams status syncing on a per-agent basis and adds retention policies for voicemails and 3rd party links. PACE also now supports rich text for both Canned Responses and Web Chat, as well as automatic email responses configured to proactively acknowledge your customer’s messages.

But we didn’t stop there. Agents will no longer be confused by seeing the Canned Responses tab on contacts that don’t support that functionality, and they will have a better experience when handling emails. Also, administrators will better understand PACE contact flows with the newly improved descriptions. Last but not least, we made multiple enhancements to the following pages on the Administrative Console:

• Integrations (Teams)
• Integrations (SMS)
• Prompts Manager

Learn More

We’re an Amazon Connect Service Delivery Partner with more than 20 years of experience delivering customer engagement solutions. We offer unparalleled contact center experience to accelerate innovation with AWS and Amazon Connect. In addition, our cloud expertise enables us to create powerful solutions while maintaining business agility and flexibility, while our dedicated CRM and ERP practices ensure seamless integrations with legacy applications.

To learn more about what our experts are doing when it comes to customer engagement solutions and Amazon Connect and to get in touch with our team, visit our thought leadership hub!

Amazon Connect now out-of-the-box offers cases in preview. With Cases, you can create or update a customer issue like any other ticketing system. Without integration, agents can collect customer information on the interaction and save it on their Agent Desktop. Switching between multiple applications can often lead to incomplete call information and a loss of time, but this can be minimized with the ability to create a case for each customer issue natively within Amazon Connect. I am very excited about this feature since it can significantly reduce the time to resolution of each contact center inquiry. 

Numerous times I have called the support line for a specific issue, and after a lot of waiting in the queue, I was transferred to different departments where I had to repeat to the agents the reason for my call. I can assure you it was a very frustrating and time-consuming customer experience. Now, similar to other ticketing systems, Amazon Connect Cases allows agents to see the call history and contact information on their Agent Desktop. This, in turn, reduces the customer’s time on hold and speeds up the time to resolution. If you want to improve customer experience and modernize your Amazon Connect contact center, continue reading for more exciting information below. 

Enable Amazon Connect Cases 

Cases are simple to enable. On the Amazon Connect console, you need to create a domain, and your cases are ready to go. However, Cases cannot work without Customer Profiles as this feature also needs to be enabled for the contact flow associated with Cases. Customer Profiles store customer information such as first and last name, phone number, home address, email address, etc. To successfully link a customer with each specific case, the customer needs to be successfully identified by Customer Profiles. 

The next step you need to complete is providing access to Cases to your security profile in Amazon Connect. 

Once this is done, you will see a new icon named Agent applications with two tabs: Case fields and Case templates.

Case fields are essential information you want your agents to collect during the conversation with a customer, such as a title, summary, status, or any other field relevant to your business.

With Case templates, you can do the following:

• Make certain fields required
• Choose fields that are displayed on the agent’s screen
• Configure the order of the fields

Create a New Case

When the agents answer the incoming contacts, all previous cases get displayed on their screen along with their status. Not only can the agent link his current call with any of the open cases, but he can also create a new one.

Creating a new Case is easy with a clickable button. Using a single interface, you can enter all information about the call and leave the summary for other agents to use.

What happens with cases after they are open or updated? To get an experience similar to other traditional ticketing systems, cases could be sent via email directly to customers or supervisors using Amazon SQS and a custom lambda.

Are you ready to step forward and use Cases with your contact center? No custom code or integration is needed to better organize your business, increase customer satisfaction, and reduce on-hold times.

Learn More About Our Amazon Connect Capabilities

We’re an Amazon Connect Service Delivery Partner with more than 20 years of experience delivering customer engagement solutions. We offer unparalleled contact center experience to accelerate innovation with AWS and Amazon Connect. In addition, our cloud expertise enables us to create powerful solutions while maintaining business agility and flexibility, while our dedicated CRM and ERP practices ensure seamless integrations with legacy applications.

To learn more about what our experts are doing regarding customer engagement solutions and Amazon Connect and to get in touch with our team, visit our Amazon Connect page.

Amazon Connect now offers a preview of Forecasting, Capacity Planning, and Scheduling capabilities directly from the Amazon Connect console. This is a significant step forward since Amazon Connect already offers integrations with the leading WFM providers. However, with these out-of-the-box features based on machine learning, Amazon now allows its customers to safely predict their future contact center volume and average handle time without utilizing any third-party tool.

In this blog post, I will describe in more detail how I set up Forecasting, Capacity Planning, and Scheduling.

Forecasting in Amazon Connect

Predict your contact center call volume and average handling time with high accuracy

The first thing you need to ensure is that you have the proper permissions to view and access the forecasting features in case you are not set up as an Admin in your Amazon Connect instance. Under Security Profiles for your role, make sure the Scheduling and Agent Application permissions are as follows:

Once this is done, you can navigate to the Forecasting page. Testing this new feature is highly recommended in a production environment as it won’t cause any issues, and you won’t need to worry about importing any dummy data. In my case, I had to import dummy data for the past year to ensure there was enough data to successfully calculate long-term and short-term forecasts.

If you have to use dummy data, you need to upload it in .csv format using the following two intervals:

• 15-minute or 30-minute intervals to generate the short-term forecast, and
• daily intervals to generate the long-term forecast.

Along with proper data, you also need to create at least one forecast group that has at least one queue. If there isn’t enough contact volume in a single queue, selecting multiple queues is recommended, and optionally separating them into different forecast groups is recommended.

Once you click the Create Forecast button, you will need to wait between a couple of hours to a full day for the system to calculate the short-term data and about a week to generate the long-term data. This is because short-term data is generated daily, while long-term data is generated weekly.

Below is an example of the long-term forecast for our contact volume for the next 18 months. It is great that you can filter by queue or channel and select the desired time frame.

Once you are satisfied with the forecast, you need to publish it to use it for Capacity Planning and Scheduling.

Capacity Planning in Amazon Connect

With Capacity Planning, estimate how many full-time equivalent (FTE) employees are required

Using a long-term contact volume forecast, you can estimate how many agents you need to hire for the next 12 months. To start, you need to create an input scenario where you specify max occupancy, overtime, work hours, attrition rates, in-office and out-of-office shrinkage, and time off for all your FTE employees. Your input will help the tool predict more accurately your future staffing needs.

After you create a scenario, you are ready to generate your capacity plan for the desired time frame. The screenshot below tells exactly how many FTE agents my contact center requires each week based on the estimated contact volume. The system can also warn if there is a need for more FTE employees for a specific time frame, or vice versa, when you have more FTE employees than might be needed. Based on the prediction, FTE agents can plan their vacations accordingly. It’s amazing how this simple feature can help supervisors and agents with proper planning.

Scheduling in Amazon Connect

Ensure you have enough FTE agents in the shifts to support customer contacts

With Scheduling, you can also create interval schedules for your FTE employees according to the future number of contacts predicted by the ML algorithm. With Staff Rules, you enter specific information about each agent’s working hours, start date and end date, and time off.

With Staffing Groups, the agents and supervisors are grouped into shifts; this is how you ensure you have properly skilled agents available at the right time of the day. Shift Profiles help determine start and end times for your daily contact center operation based on how the system divides agents into groups and shifts.

With Shift Activities, it is simple to determine what is allowed and expected from employees during their shifts. In my example, lunch and breaks are set up at the exact time of the day for each employee, so this time can also be included in the calculation when making the schedule, as no contacts are expected for those agents at that time.

Now when all is set, we are ready to generate our schedules based on the short-term forecast and it looks similar to the below screenshot:

When supervisors are satisfied with the schedule, they can easily publish it and make it available to their agents.

And this is it, as simple as that! I am impressed by how easy it is to start planning your future contact center volume and staffing without spending additional money on custom integrations. It is flexible to set up and allows supervisors to create capacity plans and schedules with high efficiency, even when the agents have many unique schedule requirements. Considering this feature is safe to be used in production instances, I can’t wait to see when it becomes publicly available, knowing how beneficial it will be for Amazon Connect customers.

If you’re interested in maximizing your contact center’s efficiency, we can help. At Perficient, we are an APN Advanced Consulting Partner for Amazon Connect, giving us a unique set of skills to accelerate your cloud, agent, and customer experience.

Perficient takes pride in our personal approach to the customer journey, where we help enterprise clients transform and modernize their contact center and CRM experience with platforms like Amazon Connect, ServiceNow, and more.

Please contact us here for more information on how Perficient can help you get the most out of Amazon Connect.