Welcome to our discussion on call center evolution using AI-powered chat and voice agents. Our intent today is to share our experiences and observations as one of the leading Conversational AI companies about how chat and voice assistants can take call center automation experiences to the next level, providing value to both customers and call center agents, regardless of the communication channel. Then in part 2, we’ll discuss Call Center experience with voice agent: Challenges, Use Cases, and Case Study.
How to choose the right technology for a Conversational AI solution for Call Centers?
There is no single platform or technology that’s a golden ticket to a successful Conversational AI experience for call center automation. In general, for every industry and field, it takes multiple technologies and multiple systems to create an effective solution. Bringing those systems together is something that Master of Code has experience in delivering, which lets us be recognized as a trusted partner by some significant providers of Conversational AI solutions in the market, including Amazon and Microsoft.
We work with many platforms based on customer needs and selected solutions, have the knowledge and skill to create Conversational AI experiences within an existing platform to optimize it, not just for a cloud deliverable. This allows us to understand what works and what doesn’t to provide recommendations and guidance throughout the lifecycle of the engagement.
Implementing a Conversational AI experience within a call center
There are a few fundamental components that must exist, beginning with call center tools that are implemented for an organization. There is no one right or wrong tool, simply what works best for your organization. All of the major solutions, Cisco — RingCentral, Zendesk, and many more — bring value to creating that call center automation experience. And these solutions enable customers to enter into the queue to engage with live agents.
Opportunities for call center automation with Conversational AI
- Reduce repetitive requests to agents, by answering easy questions.
- Reducing wait times for users, resulting in much more favorable agent stats due to lessened waiting times.
- Bring a high level of conversational automation into the equation.
Working with a conversational platform allows marrying the live agent component to the automation piece in a much more simple fashion. In many cases, these call center solutions have a conversational element, either pre-built or with partnerships that can be leveraged. Otherwise, two systems could talk to one another via existing APIs or through custom integrations that can be developed.
Top Conversational AI channels and types for customer engagement
When making the decision of how you want to engage customers, identification of the most applicable channels and conversational types needs to be determined. It can be implemented by adding a chatbot to your website, or maybe through existing digital support channels, such as Apple Business Chat, Facebook Messenger, or Microsoft Teams. Or replacement of a phone system such as a tone-based IVR system with a Conversational AI-based one.
- Channels and communication methodologies drive the use case priority and provide a foundation for measuring success. Since each channel will offer different ways of user engagement, strong knowledge of the channel and what is available within it is key to creating that optimal experience. This selection, which can grow as your needs change, is one of the fundamental pieces that can drive digital engagement for your brand.
- Workforce management tools. Allows performing some agent planning, but also understanding how to accurately route a customer to the appropriate live agent, person or department. The faster and more readily any solution can give an answer to the user, the more positive of an experience it is.
- Agent assist. Useful in determining the customer’s need and finding the right agent or workflow to execute the request.
- Menu-driven navigating systems. Can be low-cost to implement, but also creates a much more linear experience as well as provide limited metrics. As a result, you might know how many people follow a certain path, but you don’t necessarily get insight into what other types of things they’re looking to do within your chatbot.
- Analytics. By converting the experience to a Conversational AI flow, the amount of data you get increases dramatically. You will see what people want to do, identify new flows and user experiences, and have data-centric metrics to support your growth decisions.
Download a Conversational Flow Chart Diagram with the Scenario of Building Dialogues for your Chatbot
In addition, you can add in an NLP solution, either a cloud-based one like Microsoft LUIS or an on-prem solution such as RASA. Based on organizational needs, you can fine-tune that experience to flow in a way that is virtually seamless to the end-user.
It is important to select a tool for call-center automation based on your business needs, such as supported languages, what type of PII concerns exist, and technological constraints caused by each provider’s limitation.
Finding the right NLP to manage, understand and train for your call center automation is key. And this can extend further into other AI automation components such as sentiment analysis, document analysis, visual recognition and other cognitive services. Having a long term strategy helps with the right selection, and can save time and money down the road.
Additionally, we cannot forget the line of business tools that house the detailed data that is needed to make the conversation useful to end-users. This is where users can authenticate themselves, perform appropriate tasks, and access CRM, ERP, or other operational services to allow a live agent to engage and answer user questions. As a bot obtains more access to information, its value continues to increase, resulting in customers who can obtain assistance much more quickly.
Value of integrating Conversational AI solutions for call center automation
Whether it is the channel itself, a workforce management tool, NLU or other cognitive systems, line of business tools, or an analytics platform, we cannot deny the importance of integrations. No-code systems may have challenges in obtaining the information needed for an effective exchange, and a low code approach will, at minimum, allow for the development of these custom solutions to provide value.
Omnichannel support allows the bot to work alongside any channel and over multiple communication methods. With the release of a new channel, businesses will just need to create the experience for that channel based on existing flows. But if the Conversational platform does not support it, then an investigation of optimal experience and implementation in that channel will be required.
Integration provides more performant conversations because information can be presented in a more conversational manner. Providing information from a Conversational AI solution, in the same way as from a live agent, makes for a more performant and pleasant experience. We’re not limited to just assisting a customer directly, but rather providing the right solution to solve a problem and implementing a right hand for live agents may be that solution.
API data unification allows us to bring all of the data points together into a coherent message. We may get some data from CRM, an inventory system authentication, SSO platform or directly from a database. Knowing where we can get the data from means that we can unify the experience and merge the data in a way that makes sense for the user.
Integration provides flexibility like adding data sources without impacting existing ones, such as a CRM upgrading to a new version with new APIs. We update the connectivity library and ensure to get the same information with no changes to the Conversational AI flow. The system can also be configured to fall back to the previous iteration, allowing for it to remain operational, even when downstream services are challenged.
Also read: Three Secrets Behind Impactful Troubleshooting Chatbot Conversation Flows
User request translation is a key value proposition for both a bot and a human agent. Letting a bot handle the interpretation allows for a more probabilistic understanding of the request, which could lead to routing to the appropriate person or simply pulling down the right information. Integrating with NLP and other business services to extract that data and make it available to respond to the user’s request effectively which is a huge value statement.
The Conversational AI approach is much more natural, and the more we make it human-like, the more users will engage and perform actions without the need for a live agent. Building that trust that Conversational AI solutions can answer those questions is key. It can provide a 24/7 support model in languages that perhaps your office can’t do directly through live agents.
Many enterprises have older systems that require a more hands-on approach to obtaining data. It can be a mainframe system, something written in a language and platform that is no longer viable, or a subject matter expert who has the knowledge of how to support it but has left the organization. Accessing legacy APIs and the ability to provide the data into a modern system provides significant value to the customer and to the business, which a no-code solution may not be able to provide.
Having built many extensive Conversational AI solutions, we at Master of Code are well versed in finding the right efficiencies and use cases, bringing information at the right time to create an optimal experience. With each of our partners, we work with stakeholders to best understand the ability to implement Conversational AI solutions. It includes choosing the right technology for the task at hand, data sources, and integrations to generate the best experience for users. The objective is to create efficiency and address customer concerns quickly and correctly.
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Call Center Automation using AI-Powered Chatbot was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.