As AI development accelerates, so too does contact center innovation.
Indeed, over the last few years, advanced algorithms and models have augmented and enhanced agent workflows, transforming customer experiences and unlocking valuable insights for business leaders.
But that’s not all. There’s lots more that contact center AI can do, as evident in the following top 20 use cases.
1. Capturing Customer Intent
With NLP (Natural Language Processing) models or generative AI, contact centers can automatically identify a person’s reason for contacting their team. These tools can pinpoint keywords in conversations and apply tags to service requests and tickets, streamlining the routing process.
The ability to rapidly capture customer intent can help with everything from tracking a customer’s interaction history to building more granular contact center forecasts.
2. Reinventing the IVR
With AI, the frustrations of old-fashioned clunky IVR systems are a thing of the past. AI-powered IVR systems can understand caller requests, analyze their needs, and automatically route them to the right agent or department in seconds. With AI-powered IVRs, customers can express their needs more clearly and even complete simple tasks without speaking to an agent.
3. Improving Contact Center Routing
Since AI-powered tools can more accurately identify customer intent and sentiment, they can also streamline the routing process. AI tools can pinpoint the ideal agent for an interaction based on agent expertise, availability, query complexity, and even customer value. What’s more, they can distribute conversations across a range of channels, empowering omnichannel service.
4. Automating Customer Conversations
One of the most common use cases for AI in the contact center is the automation of customer conversations. Generative AI-powered chatbots and virtual assistants can deliver personalized and engaging responses to customer questions, complete tasks without human input, and even determine when to transfer a customer over to a human agent.
5. Transcribing and Summarizing Conversations
Converting call recordings into searchable transcriptions and summaries can be a time-consuming process. AI-driven solutions can offer real-time or post-call transcriptions of every interaction. Some generative AI tools can even create intuitive call summaries, highlighting important topics and action items for customer follow-up, and training purposes.
6. Streamlining Quality Management
With AI-powered monitoring tools, companies can automate the quality management process, rapidly scoring conversations based on pre-set criteria. Some solutions can even send instant alerts to business leaders and supervisors when issues emerge to help proactively improve the customer experience.
7. Feeding Agents Live Knowledge and Data
In the standard contact center, employees often waste significant time searching for relevant data to solve a customer’s problems. An AI-powered assistant can boost agent productivity, surfacing information from databases and other applications, based on identified keywords.
These tools can even integrate with CRM systems to offer personalized insights into a customer’s purchasing history or previous conversations.
8. Mechanizing Time-Consuming Agent Desktop Tasks
AI is an important ingredient of effective workflow automation. Intelligent tools can automatically update customer records, fill out forms, schedule follow-up calls, and even manage proactive outreach on behalf of agents. This gives contact center reps more time to focus on answering customer calls that need more attention and focus wholeheartedly on addressing customer needs.
9. Suggesting Next Best Actions
For both new and existing agents, AI agent-assist can act as a valuable guide, coaching them through the process of delivering amazing customer experiences. These tools can leverage both real-time and historical data to suggest next best actions to agents. They can even personalize these suggestions based on a customer’s profile or history.
10. Identifying Coaching Opportunities
Consistent coaching is crucial to boosting the productivity and performance of your contact center team.
AI analytical tools built into contact center environments can analyze conversations, providing insights into average handling times, queue times, and changing customer sentiment. This can help business leaders identify coaching opportunities and develop training resources specific to each team member’s needs.
11. Automating Contact Center Forecasts
Since AI solutions can analyze and surface trends in handling times, contact volumes, and resource requirements, they can have a significant impact on the accuracy of your contact center forecasts.
The best-placed tools can help enhance your workforce management (WFM) strategy, enabling better staffing decisions and even reducing operational costs.
12. Pinpointing Broken Processes
By diving into the data of each conversation, AI tools can help you discover broken processes across different customer intents.
As a result, businesses can adjust the customer journey to avoid failure demand, reduce overall call volumes, and enhance customer experiences.
13. Measuring Customer and Employee Sentiment
Intelligent tools are incredibly effective at detecting sentiment in a person’s voice, or the words they use in a conversation. Sentiment analysis apps can offer real-time insights into a person’s mood, helping you to determine which factors positively or negatively affect both customer experience and employee engagement.
14. Monitoring First Contact Resolution
Short waiting times and high first contact resolution rates are often two of the most important factors influencing customer satisfaction levels. Unfortunately, monitoring FCR rates manually is a minefield – with many contact centers defaulting to measuring repeat contacts instead. AI improves the accuracy of your FCR calculations by tracking customer intent and repeat contacts at the same time.
15. Drawing Insights from Customer Feedback
Today’s companies need to listen to and understand the voice of the customer to ensure they’re living up to their expectations. AI makes sifting through vast volumes of customer feedback simple. It can examine responses to surveys and experience-focused questions across a range of channels, and surface insights that drive service enhancements and product improvements.
16. Predicting Customer Behaviours
The ability AI has to examine large amounts of contact center data and identify key trends makes it excellent at predicting future behaviours. It can help companies to deliver proactive service, by monitoring when and why customers are likely to reach out for support. It can even boost the quality of your marketing strategies, and sales initiatives.
17. Analyzing the Success of New Tech Deployments
As your contact center evolves and you introduce new channels and technologies to your teams, AI can help you track the ROI of your efforts. It can offer insights into adoption rates among team members and help you monitor how new solutions impact performance. It can also help you understand which channels and tools are most valuable to your customers.
18. Augmenting Contact Center Security
Security and compliance are critical in the contact center, and AI gives business leaders the guidance they need to protect their teams, customers, and data. It can increase visibility into potential security risks, optimize authentication methods with biometrics, and automatically alert business leaders when it detects suspicious activity.
19. Personalizing the Service Experience
Around 71% of customers now expect a personalized experience from every contact center. AI can help companies deliver the unique interactions customers are looking for. It can share data on past interactions, preferences, and behaviors with an agent during a conversation, draw data from CRM systems during self-service discussions, and even deliver personalized product recommendations.
20. Optimizing Contact Center Gamification
Finally, AI can provide companies with an intuitive way to boost employee engagement through gamification. Using historical data, you can check the potential impact a game or competition will have before you roll it out to your team. You can also consistently monitor the impact of gamification strategies on agent performance and satisfaction levels.
