For 81% of customers, self-service is the first choice for solving a problem. These customers want low-friction, fast resolution services. The speed & efficiency of your self-service determines their customer experience.
Say goodbye to forms and FAQ pages and website support. Intelligent Agents, Voicebots, and Chatbots will now deliver your self-service. As per the expert projections, by 2020, 85% of the customer interactions will be handled without human, so agents can focus on what they are best at “Doing intelligent conversations” and leaving self-service to AI-driven intelligent bots. The pathway to real-time customer service brings in huge opportunities for businesses today. 5 reasons why AI-powered customer service is the future of call centers.
1. Customer want resolutions not interfaces
Busy, information-saturated customers need clear directions. They don’t want unnecessary choices and clutter. And they don’t want to understand your interface, no matter how well it is designed. They want to resolve their problems without delays.
AI-driven self-service does just that. Customers just state their problem. AI does the task of figuring out what it means, browsing options, selecting and furnishing appropriate answers or steps. It stays by their side, guiding them through every step. It also makes the interface invisible.
How to implement this:
Create voicebots and chatbots on your webpage/ mobile application or IVR. They will greet your customer, hear their query and give replies. Use it to provide ticket status, order status or delivery status; to answer FAQ’s and give technical support. You can even implement security measures like ensuring your bot verifies customer date of birth or other such security questions, before giving information.
2. Real-Time analysis will make self-service more responsive
AI listens. A passive webpage doesn’t realize if your customer is dissatisfied. Your AI does. With speech analysis, it analyses what the customer says or types. With sentiment analysis, it determines mood. It uses this to take the customer to the next step thereby improving its performance next time.
How to implement this:
Implement Speech Analysis & Sentiment Analysis to give real-time feedback to your voicebots & chatbots.
3. Seamless escalation lead to higher FTR’s
All questions aren’t the frequently asked ones. Anyone managing a contact center knows there are exceptions, problems, and complicated answers that need human interaction. If your customer has one of these, then fumbling about self-service can leave them frustrated. But AI-powered self-service —whether it is a voice bot or a chatbot—determines when human interaction is needed. The customer is seamlessly transferred from self-service to a personal interaction that solves their problem in the first contact.
How to implement this:
Connect the voicebots and chatbots on your webpage/ mobile application or IVR with your call routing.
4. Voice recognition will transform ease-of-interaction
Chatbots can provide customers answers when they need to be discreet or are multitasking on their systems. But it’s inconvenient when they are on the move, lounging on the couch or have their hands occupied with something else.
Now people don’t need to have their hands on a keypad and eyes on a screen to opt for self-service. AI-driven voicebots will respond to their natural language, effectively understanding what they say and responding accordingly.
How to implement this:
Use voicebots to complement your chatbots. Use Smart IVR with voice recognition features turned on. People can interact with your call center through their app, your site, or your number—completely hands-free.
5. Your AI keeps learning, keeps your self-service dynamic
Analysis of problem areas isn’t complicated anymore. AI can keep a tab of when problems occur and can identify problem clusters in real time. It can work with you to create solutions, faster than ever before.
How to implement this:
Get voice-enabled analysis. You don’t need a querying language to interact with your analysis. And you can determine problems and give AI instructions easily using natural language.
The article was originally published on www.analyticsinsight.net and can be viewed in full
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