8 ways to enhance the customer experience using AI
Self-service chatbots, interactive voice response, robotic process automation and intelligent routing are some of the ways organizations can boost satisfaction and loyalty.
Using AI to put a smile on the face of your customers
In a competitive market, businesses are focusing on enhancing customer experiences, while, at the same time, trying to minimize costs. Artificial intelligence, with its computational power, can help to accomplish that goal by understanding customers better from the data they leave behind at every touchpoint. Here are eight AI features businesses need to consider to maximize their AI initiatives.
1. Self-service and chatbots
Customers prefer to resolve their own issues. Self-service knowledgebase and chatbots are popular tools that empower customers. The power of AI in chatbot or knowledgebase provides a substantial efficiency gain over rules-driven chatbots or keyword-based (KB) search. Gradually the results are perfected as more data is fed into the system. While call center agents have limited bandwidth, these tools can scale and provide 24x7 customer support and save costs through call deflection.
2. Interactive Voice Response
Interactive Voice Response (IVR) provides a menu-based option of self-service to callers. The static decision tree approach in a traditional IVR, however, often frustrates customers. As a result, they attempt to bypass it and talk to a human agent. AI-driven IVR incorporates automated speech recognition and natural language processing throughout the call. Instead of focusing on the answer, it attempts to determine the intent of customers' requests and reorders recommendations intelligently, based on the expected flow. Thus, customers’ inquiries are effectively resolved without having to talk to an agent.
3. Robotic Process Automation
Robotic Process Automation (RPA) for back office and customer service reduces manual, error-prone and repetitive efforts of data entry, interface navigation or process fulfillment. To enhance the experience further, AI-driven RPA uses a cognitive engine to recognize the resemblances in successfully completed processes in the past and intelligently derive conclusions based on probability. This method ensures accuracy and efficiency in tasks that agents can perform using mouse clicks or keystroke hits.
4. Intelligent routing
To optimize the customer satisfaction requires assigning the right task, to the right agent at the right time. AI-driven workflow leverages the customer data, transaction history to anticipate the intent behind the call and automatically assigns the ticket to the most capable agent, based on skill, training, track record and availability.
5. Automatic language translation
For businesses operating globally, it is critical to break the language barriers and enable CSRs to communicate in customers' language. Automatic language detection and translation can help to achieve that goal. The use of AI-driven language translation helps companies stay ahead of the game and provides greater accuracy. Unlike traditional translation, AI-based translation engine uses neural network to convert the entire sentence at once to deliver greater context.
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6. Predictive analytics
AI-driven predictive analytics generates exceptional insights into customers’ future behavior. Machine learning algorithms, paired with statistical techniques, make assumptions from historical transactions. Any potential threat can be identified in a customer's journey from the past buying patterns or anomaly detection techniques. In this way, organizations can act on information ahead of time. Further, huge amounts of call center data, such as average call handle time, ticket volume etc. provide insight into agents' performance trends. Strategies like staffing, training, and system performance enhancement are outcomes of those analytics. Sentiment analysis process, on the other hand, delivers insights whether the customer's opinion reflects positive, negative or neutral sentiments. By understanding natural language, this technology identifies and scores words, phrases indicating sentiments.
7. Personalized recommendations
Personalized communications build relationships. Advanced technology enables companies to listen to customers through every activity they do. AI-powered social listening and recommendation engines leverage ‘Big Data’ to capture and digest customers' demographic data, frequent browsing history, cart item selection, media sharing, and product reviews in real-time. As a result, they recognize every customer's unique preferences. Accordingly, companies can create unique recommendations proactively for individual customers, regardless of the marketing channel they use.
8. Cross-channel contextual engagement
Customer journeys occur in multi stages. They jump from channel to channel, device to device and yet expect a connected cross channel experience at every touchpoint. AI and Machine Learning techniques gather and analyze data from the entire eco-system and determine the intent and context. The context becomes available across all the channels. It proactively guides customers’ journeys, enabling them to switch from speech to chat or chat to live agent seamlessly, without repeating any details.
The role of human agents in the future
Enterprise leaders need to identify AI opportunities in their unique customer service arena, to optimize the experience of both customers and agents. There’s no doubt that AI is revolutionizing the delivery of customer service. It is not, however, going to replace human agents anytime soon. With the aid of technology, repetitive tasks can be automated, while human agents can focus on customers by showing them empathy and handling complex queries.