Delivering positive customer experiences (CX) is critical – it’s how organisations drive loyalty, competitive differentiation and revenue growth. Now more than ever, it is essential for businesses to benchmark their present CX performance and prepare to better meet customers’ evolving expectations.

Artificial intelligence provides a golden opportunity for tailoring positive customer experiences. AI-powered tools allow contact centres to handle more complex situations that used to require human intervention, provide customer self-service options, and extend enhanced support across contact channels.

Today, AI is being layered with current conversation intelligence solutions and emerging techniques encompassing machine learning (ML) and robotic process automation (RPA). Automating aspects of contact centre operations makes it possible to serve customers in new ways, raise customer satisfaction and give businesses more insight into their customer interactions.

Read on for our list of use cases and proof points to guide your AI-powered customer experience strategy.

Use cases and proof points

AI by itself does not improve customer experience or agent productivity. Rather, this technology streamlines processes – often mundane or repetitive processes – and can uncover trends or patterns within data. It’s through AI that humans can make more informed business decisions and drive improvement. Here is a sampling of how combining AI-powered conversation intelligence solutions can benefit contact centre operations.

Improve first-call resolution (FCR): As a live call or online chat progresses, AI can predict the direction the interaction will take, accurately forecast whether the customer will make a future contact, and guide agents accordingly.
Create better customer experiences across departments: Engagement and analytics data can be used to assist throughout the customer journey, from sales to onboarding, technical assistance, billing and payments.
Increase customer satisfaction: AI can predict whether a customer will be satisfied or dissatisfied with a specific action an agent could take, thereby helping agents take approaches to deliver better outcomes and experiences.
Predict customer churn: AI is used to predict churn by analysing historical data to identify at-risk customers. These insights can help companies proactively take action to engage customers and get the chance to improve customer satisfaction.
Identify upsell opportunities: AI can rate how likely a customer is to respond to an upsell request and prompt agents to upsell likely prospects and avoid spending time on low-likelihood customers.
Improve agent training: The predictive powers of AI can be used to forecast how well agents will perform in different situations and tailor individual training and coaching strategies.
Using data to train chatbots: Call transcripts from your call centre are a data goldmine to train chatbot interactions. Customer service agents see the value of such capabilities – 64% believe AI-powered chatbots will enable them to provide a more personalised experience to customers.
Reduce agent turnover and improve job satisfaction: Approximately three quarters of organisations that use AI and ML said their employees are doing more interesting work as a result of ML-enabled processes, and 78% said ML-enabled processes will result in improvements in job satisfaction and retention.
Prevent fraud: AI can detect fraudulent activity based on when, how often and by which channels an individual contacts a company, the questions they ask, the requests they make, and the specific words, phrases and persuasive techniques used.

AI represents the next step in the evolution of CX. It gives new value to old, previously untapped data and helps organisations continuously make experiences better by learning what works and applying the results. To learn more, check out CallMiner’s white paper, How AI Improves the Customer Experience.