By Vijai Shankar, VP Product Marketing, Uniphore
Automating after-call work — categorizing and summarizing the call, updating systems, taking follow-up actions on promises made to the customer during the interaction, and other tasks — can have a dramatic impact on your costs, staffing, wait times, customer satisfaction, and quality. For example, some companies are seeing reductions of 60-80% in the duration of after-call work, reducing their average handle time by two minutes or more.
While creating the business case for automating ACW is straightforward, figuring out how to get started with conversational artificial intelligence (AI) and automation can be daunting. It’s a challenge to sort through the buzzwords and hype to find practical information about what you need to successfully automate ACW.
Automating after-call work can have a dramatic impact on your costs, staffing, wait times, customer satisfaction, and quality.
The following easy-to-understand definitions and a capabilities checklist can help.
Understanding the Core Technologies
Achieving the level of automation and accuracy you need to cut minutes from the average time spent on ACW demands a combination of advanced technologies centered around conversational artificial intelligence (AI). Here’s a quick list and definition of the core technologies you’ll need:
- Conversational AI: A set of advanced AI technologies that recognizes and comprehends human language in multiple languages and uses this understanding to optimize and analyze conversations in and across multiple channels.
- Natural language processing and understanding (NLP/NLU): NLP and NLU are components of conversational AI that help computers understand and interpret human language.
- Robotic process automation (RPA): RPA is software that can emulate the actions of a human interacting with digital systems to automate repetitive tasks and end-to-end business processes.
- AI analytics/intelligent decision support: Intelligent decision support uses machine learning (part of AI) and reasoning to discover insights, find patterns, and uncover relationships in data, automating the steps that humans would take if they could exhaustively analyze large datasets.
- Intelligent applications: AI-powered software called intelligent applications includes rules engines, user interfaces, notifications, and alerts, and other components that handle specific use cases within the contact center, such as intelligent agent assistance, intelligent self-service, and others.
Following a Checklist of Capabilities
While superficial AI solutions and open source software can provide sandbox environments for exploring AI, they don’t provide the sophisticated capabilities seen in leading-edge conversational AI technology designed with the contact center in mind. Automating ACW also requires specific capabilities to achieve optimal outcomes. Look for a platform that combines the core technologies described above and offers the following capabilities:
✅ Industry-leading speech recognition
You need a platform that understands your agents and customers with a high degree of accuracy. To do so, the technology must be able to listen for and detect a wide range of languages, including specific dialects.
✅ Intent recognition
Beyond understanding the language, the platform must be able to identify and understand customer intent, parsing the conversation in real-time to accurately categorize the call and understand how to act on the assurances made by the agent during the call.
✅ Intelligent interaction sectioning
In addition to intent, the solution should also automatically identify and section the key classifying elements in a conversation and match them to categories to add context and facts to an intent. For example, the platform needs to automatically identify the greeting, discover key issues and intents during the call, and recognize and record the outcome/resolution with an accurate interaction sectioning capability.
✅ Customer sentiment and emotion recognition and analysis
The platform should recognize and extract customer sentiment as positive, neutral, or negative. This provides insight into the customer’s state of mind to enhance empathy and communications, and it provides data for trend analysis. Understanding customer emotions such as frustration, disappointment, and anger helps the platform inspire customer loyalty and trust.
✅ Promises management
A promise made that is not kept or tasks that are not performed correctly can quickly negate the positive effects of a good conversational experience. Promises management is an important component of ACW that directly impacts your call handling times, wait times, and customer satisfaction.
✅ Integrations with existing systems and architecture
The last thing your contact center needs is another siloed solution. You also don’t want to be on the hook for building custom integrations. That’s why it’s important to choose a platform that works with your existing architecture and interfaces to your current contact center solutions.
✅ Cloud-based services
As more companies are moving to the cloud for its agility, scalability, ease of management, and availability of new technologies, it’s important to choose a conversational AI and automation platform that is designed for use in the cloud.
Learn more about what to look for in a conversational AI and automation solution in our buyer’s guide “How to Choose a Solution for Automating After-Call Work.”