Self-service enables your business to provide immediate contactless support to your customers through the use of intelligent technologies such as voice biometrics, behavior analysis, and language processing nature (NLP). Provide 24/7 support to your customers when your live agents are offline or busy with priority calls.
65% of customers prefer self-service to talking to a company representative.
Security should always be a priority for your business. Whether its a contact centre protecting valuable customer information or keeping business information confidential fraudsters like PIN and caller ID are becoming increasingly vulnerable. Your business can use voice biometrics software to identify your customers by their unique voiceprint. Instantly verifying your callers without asking for a password makes instant verification easy and secure.
Natural Language Processing (NLP) is a branch of AI that enables computer software and systems to analyze, understand, and act on requests and information through normal human language. Operated as an all-in-one self-service option for your customers, it enables a seamless end-to-end chat experience across all channels. Change the way you identify and authenticate legitimate users, and help identify potential malicious activity. The combination of biometrics and conversational IVR fully automates multi-factor user identification and authentication. Working with leading companies like Omilia, Verint, Amelia AI, Nuance, and Pindrop, we design and implement solutions that seamlessly integrate with your current and targeted technologies.
Behavioral Analytics (BA) enables you to respond to changing customer needs by collecting and analyzing data from customer and employee interactions, employee desktop usage, and contextual information. BA predicts future trends, infers customer needs and preferences, and allows you to direct your customers to targeted content. BAs also track and monitor the behavior of users, IP addresses, and devices within a company. Abnormal behaviors are automatically detected by machine learning algorithms. Such unusual behavior indicates potentially malicious activity that is alerted by analysts with relevant contextual information for further investigation and action.
