Great customer experience provides a competitive edge and helps create brand differentiation. As per the Forrester report, The State Of Customer Obsession, 2022, being customer-first can make a sizable impact on an organization’s balance sheet, as organizations embracing this methodology are surpassing their peers in revenue growth. Despite contact centers being under constant pressure to do more with less while improving customer experiences, 80% of companies plan to increase their level of investment in Customer Experience (CX) to provide a differentiated customer experience. Rapid innovation and improvement in generative AI has captured our mind and attention and as per McKinsey & Company’s estimate, applying generative AI to customer care functions could increase productivity at a value ranging from 30–45% of current function costs.
Amazon SageMaker Canvas provides business analysts with a visual point-and-click interface that allows you to build models and generate accurate machine learning (ML) predictions without requiring any ML experience or coding. In October 2023, SageMaker Canvas announced support for foundation models among its ready-to-use models, powered by Amazon Bedrock and Amazon SageMaker JumpStart. This allows you to use natural language with a conversational chat interface to perform tasks such as creating novel content including narratives, reports, and blog posts; summarizing notes and articles; and answering questions from a centralized knowledge base—all without writing a single line of code.
A call center agent’s job is to handle inbound and outbound customer calls and provide support or resolve issues while fielding dozens of calls daily. Keeping up with this volume while giving customers immediate answers is challenging without time to research between calls. Typically, call scripts guide agents through calls and outline addressing issues. Well-written scripts improve compliance, reduce errors, and increase efficiency by helping agents quickly understand problems and solutions.
In this post, we explore how generative AI in SageMaker Canvas can help solve common challenges customers may face when dealing with contact centers. We show how to use SageMaker Canvas to create a new call script or improve an existing call script, and explore how generative AI can help with reviewing existing interactions to bring insights that are difficult to obtain from traditional tools. As part of this post, we provide the prompts used to solve the tasks and discuss architectures to integrate these results in your AWS Contact Center Intelligence (CCI) workflows.
Overview of solution
Generative AI foundation models can help create powerful call scripts in contact centers and enable organizations to do the following:
Create consistent customer experiences with a unified knowledge repository to handle customer queries
Reduce call handling time
Enhance support team productivity
Enable the support team with next best actions to eliminate errors and take the next best action
With SageMaker Canvas, you can choose from a larger selection of foundation models to create compelling call scripts. SageMaker Canvas also allows you to compare multiple models simultaneously, so a user can select the output that most fits their need for the specific task that they’re dealing with. To use generative AI-powered chatbots, the user first needs to provide a prompt, which is an instruction to tell the model what you intend to do.
In this post, we address four common use cases:
Creating new call scripts
Enhancing an existing call script
Automating post-call tasks
Post-call analytics
Throughout the post, we use large language models (LLMs) available in SageMaker Canvas powered by Amazon Bedrock. Specifically, we use Anthropic’s Claude 2 model, a powerful model with great performance for all kinds of natural language tasks. The examples are in English; however, Anthropic Claude 2 supports multiple languages. Refer to Anthropic Claude 2 to learn more. Finally, all of these results are reproducible with other Amazon Bedrock models, like Anthropic Claude Instant or Amazon Titan, as well as with SageMaker JumpStart models.
Prerequisites
For this post, make sure that you have set up an AWS account with appropriate resources and permissions. In particular, complete the following prerequisite steps:
Deploy an Amazon SageMaker domain. For instructions, refer to Onboard to Amazon SageMaker Domain.
Configure the permissions to set up and deploy SageMaker Canvas. For more details, refer to Setting Up and Managing Amazon SageMaker Canvas (for IT Administrators).
Configure cross-origin resource sharing (CORS) policies for SageMaker Canvas. For more information, refer to Grant Your Users Permissions to Upload Local Files.
Add the permissions to use foundation models in SageMaker Canvas. For instructions, refer to Use generative AI with foundation models.
Note that the services that SageMaker Canvas uses to solve generative AI tasks are available in SageMaker JumpStart and Amazon Bedrock. To use Amazon Bedrock, make sure you are using SageMaker Canvas in the Region where Amazon Bedrock is supported. Refer to Supported Regions to learn more.
Create a new call script
For this use case, a contact center analyst defines a call script with the help of one of the ready-to-use models available in SageMaker Canvas, entering an appropriate prompt, such as “Create a call script for an agent that helps customers with lost credit cards.” To implement this, after the organization’s cloud administrator grants single-sign access to the contact center analyst, complete the following steps:
On the SageMaker console, choose Canvas in the navigation pane.
Choose your domain and user profile and choose Open Canvas to open the SageMaker Canvas application.
Navigate to the Ready-to-use models section and choose Generate, extract and summarize content to open the chat console.
With the Anthropic Claude 2 model selected, enter your prompt “Create a call script for an agent that helps customers with lost credit cards” and press Enter.
The script obtained through generative AI is included in a document (such as TXT, HTML, or PDF), and added to a knowledge base that will guide contact center agents in their interactions with customers.
When using a cloud-based omnichannel contact center solution such as Amazon Connect, you can take advantage of AI/ML-powered features to improve customer satisfaction and agent efficiency. Amazon Connect Wisdom reduces the time agents spend searching for answers and enables quick resolution of customer issues by providing knowledge search and real-time recommendations while agents talk with customers. In this particular example, Amazon Connect Wisdom can synchronize with Amazon Simple Storage Service (Amazon S3) as a source of content for the knowledge base, thereby incorporating the call script generated with the help of SageMaker Canvas. For more information, refer to Amazon Connect Wisdom S3 Sync.
The following diagram illustrates this architecture.
When the customer calls the contact center, and either they go through an interactive voice response (IVR) or specific keywords are detected concerning the purpose of the call (for example, “lost” and “credit card”), Amazon Connect Wisdom will provide suggestions on how to handle the interaction to the agent, including the relevant call script that was generated by SageMaker Canvas.
With SageMaker Canvas generative AI, contact center analysts save time in the creation of call scripts, and are able to quickly try new prompts to tweak the scripts creation.
Enhance an existing call script
As per the following survey, 78% of customers feel that their call center experience improves when the customer service agent doesn’t sound as though they are reading from a script. SageMaker Canvas can use generative AI help you analyze the existing call script and suggest improvements to improve the quality of call scripts. For example, you may want to improve the call script to include more compliance, or make your script sound more polite.
To do so, choose New chat and select Claude 2 as your model. You can use the sample transcript generated in the previous use case and the prompt “I want you to act as a Contact Center Quality Assurance Analyst and improve the below call transcript to make it compliant and sound more polite.”
Automate post-call tasks
You can also use SageMaker Canvas generative AI to automate post-call work in call centers. Common use cases are call summarization, assistance in call logs completion, and personalized follow-up message creation. This can improve agent productivity and reduce the risk of errors, allowing them to focus on higher-value tasks such as customer engagement and relationship-building.
Choose New chat and select Claude 2 as your model. You can use the sample transcript generated in the previous use case and the prompt “Summarize the below Call transcript to highlight Customer issue, Agent actions, Call outcome and Customer sentiment.”
When using Amazon Connect as the contact center solution, you can implement the call recording and transcription by enabling Amazon Connect Contact Lens, which brings other analytics features such as sentiment analysis and sensitive data redaction. It also has summarization by highlighting key sentences in the transcript and labeling the issues, outcomes, and action items.
Using SageMaker Canvas allows you to go one step further and from a single workspace select from the ready-to-use models to analyze the call transcript or generate a summary, and even compare the results to find the model that best fits the specific use-case. The following diagram illustrates this solution architecture.
Customer post-call analytics
Another area where contact centers can take advantage of SageMaker Canvas is to understand interactions between customer and agents. As per the 2022 NICE WEM Global Survey, 58% of call center agents say they benefit very little from company coaching sessions. Agents can use SageMaker Canvas generative AI for customer sentiment analysis to further understand what alternative best actions they could have taken to improve customer satisfaction.
We follow similar steps as in the previous use cases. Choose New chat and select Claude 2. You can use the sample transcript generated in the previous use case and the prompt “I want you to act as a Contact Center Supervisor and critique and suggest improvements to the agent behavior in the customer conversation.”
Clean up
SageMaker Canvas will automatically shut down any SageMaker JumpStart models started under it after 2 hours of inactivity. Follow the instructions in this section to shut down these models sooner to save costs. Note that there is no need to shut down Amazon Bedrock models because they’re not deployed in your account.
To shut down the SageMaker JumpStart model, you can choose from two methods:
Choose New chat, and on the model drop-down menu, choose Start up another model. Then, on the Foundation models page, under Amazon SageMaker JumpStart models, choose the model (such as Falcon-40B-Instruct) and in the right pane, choose Shut down model.
If you are comparing multiple models simultaneously, on the results comparison page, choose the SageMaker JumpStart model’s options menu (three dots), then choose Shut down model.
Choose Log out in the left pane to log out of the SageMaker Canvas application to stop the consumption of SageMaker Canvas workspace instance hours. This will release all resources used by the workspace instance.
Conclusion
In this post, we analyzed how you can use SageMaker Canvas generative AI in contact centers to create hyper-personalized customer interactions, enhance contact center analysts and agents’ productivity, and bring insights that are hard to get from traditional tools. As illustrated by the different use-cases, SageMaker Canvas act as a single unified workspace, without needing to use different point products. With SageMaker Canvas generative AI, contact centers can improve customer satisfaction, reduce costs, and increase efficiency. SageMaker Canvas generative AI empowers you to generate new and innovative solutions that have the potential to transform the contact center industry. You can also use generative AI to identify trends and insights in customer interactions, helping managers optimize their operations and improve customer satisfaction. Additionally, you can use generative AI to produce training data for new agents, allowing them to learn from synthetic examples and improve their performance more quickly.
Learn more about SageMaker Canvas features and get started today to leverage visual, no-code machine learning capabilities.
About the Authors
Davide Gallitelli is a Senior Specialist Solutions Architect for AI/ML. He is based in Brussels and works closely with customers all around the globe that are looking to adopt Low-Code/No-Code Machine Learning technologies, and Generative AI. He has been a developer since he was very young, starting to code at the age of 7. He started learning AI/ML at university, and has fallen in love with it since then.
Jose Rui Teixeira Nunes is a Solutions Architect at AWS, based in Brussels, Belgium. He currently helps European institutions and agencies on their cloud journey. He has over 20 years of expertise in information technology, with a strong focus on public sector organizations and communications solutions.
Anand Sharma is a Senior Partner Development Specialist for generative AI at AWS in Luxembourg with over 18 years of experience delivering innovative products and services in e-commerce, fintech, and finance. Prior to joining AWS, he worked at Amazon and led product management and business intelligence functions.
