A 2022 Gartner study highlighted three “important” ways in which AI can benefit customer service operations. These were:
- Obtaining insights
- Ensuring optimal user experiences
- Process improvement
Many forms of AI enable customer service functions to achieve these outcomes. Indeed, technology vendors are embedding robotic process automation (RPA), natural language processing (NLP), and machine learning (ML) capabilities into traditional contact center software. Meanwhile, conversational AI, predictive routing, and speech analytics are becoming stalwart AI-led customer service technologies.
Yet, the success of any such AI-powered customer service software hinges on the data available. In the previously referenced research, Vice President Analyst at Gartner Bern Elliot states: “Customer service and support leaders seeking to use AI to improve digital and self-service customer service should ensure they have sufficient, accurate and relevant data to support customer service insights and predictions use cases.”
Breaking down the three ways that AI benefits customer service above, here are seven such use cases that can supercharge customer service in 2022.
1. Understanding Customer Pain Points
Speech analytics is a longstanding AI-driven contact center technology, which is improving thanks to advances in NLP. One use case for this technology is to tag every contact center conversation with a disposition code, enabling operations to understand why customers reach out to them. With this capability, contact centers can accurately quantify failure demand and – again with speech analytics – pinpoint the issues which result in customer conversations. Contact centers can lower failure demand, cut costs, and remove effort from CX by addressing these.
2. Upgrading Proactive Customer Communication
Companies may obtain a far more precise picture of their consumers’ preferences and behavior patterns by using AI and ML to assess social, historical, and behavioral data.
A top-notch proactive customer service strategy either informs, adjusts, or optimizes customer behavior based on rules within the customer journey. However, for such a strategy to pay dividends, companies must use customer data to assess where the customer is on their journey. Customer journey analytics paves the way for this, while other AI-powered tools can automate the delivery of proactive alerts.
3. Building 360-Degree Customer Profiles
By using AI and ML to assess social, historical, and behavioral data, companies may obtain a far more precise picture of their consumers’ preferences and behavior patterns.
Typically hosted within a CRM platform, the AI learns and improves from the data it examines. In doing so, it creates new insights for customer profiles – including sentiment predictions – to optimize customer service.
4. Automating Customer Communication
Automated AI assistants can now respond to customers across all contact center channels to offer advanced self-service. If the bot interaction becomes too complicated, many models will seamlessly pivot to a human agent, who receives a transcript of the conversation so far.
Augmented with machine learning, these bots may also learn from previous interactions and patterns in customer behavior. In doing so, they grow increasingly sophisticated.
5. Enhancing the IVR
The IVR is a stalwart contact center technology. However, it is often a source of customer frustration. Many callers get confused by the options it presents, angry at the unclear navigation, and impatient when it takes a long time to navigate.
Yet, AI is supercharging the IVR. Indeed, as McKinsey & Company writes: “IVR systems are evolving from dumb menu systems into smart “voicebots” capable of handling complex customer queries.” These “voicebots” use natural language understanding to route customers seamlessly, alongside NLP and RPA to enable next-generation self-service.
6. Streamlining the Agent Experience
AI technologies can automate many basic desktop processes to streamline the agent experience. With these tools, agents no longer need to copy and paste information, launch new applications, and manually fill in forms. Instead, desktop automation takes care of it all.
Furthermore, advances in agent-assist technologies enable bots to track conversations in real-time and proactively present agents with information from various systems – such as the knowledge base, CRM, and delivery tracking software. As such, agents can respond to customer queries more seamlessly.
7. Tracking Customer Emotion
AI can assess the language and tone in a conversation – across numerous channels – and determine if a caller is pleased or upset. By monitoring sentiment, agents can adapt their approach to provide better, more empathetic service. Yet, perhaps more pressingly, companies may measure customer emotion across the customer experience.
Indeed, this is an evolving CX trend. As a Deloitte 2019 study states: “Emotional and contextual data can foster deeper emotional connections across all key moments with customers to increase their lifetime value while also decreasing their likelihood of switching brands.”
Discover more trends in customer service AI by reading our article: 5 Trends in Contact Centre AI for 2022
