Understanding Contact Center AI: Improving Customer Experiences
Contact Centers have leveraged advanced technologies over the decades – starting with basic routing to IVR systems and it remains one of the key areas for businesses to interact and meet the customer’s needs. However, customers are still unhappy, and their expectation of the quality of service continues to increase. The demand for quality services has its operational challenges and organizations have been leveraging cloud and AI tools to provide better customer experience. We are not just talking about ubiquitous chatbots, but AI can benefit contact centers in a much larger way. In this article, we shall explore Google’s Contact Center AI solutions and how you could bring AI to support your customer services. The need for a Contact Center AI solution Today, traditional interactive voice response (IVR) systems can’t provide the quality experiences customer expect today. This is because they are expensive and require IVR experts to deploy. In most scenarios, they fail to serve the end customer, which means they fail to serve your purpose. What happens in a typical contact center is that, during peak hours the agents wouldn’t be able to reply or answer to every customer at the same time. From a customer perspective, they feel that they are ignored due to long wait/hold time, or the business doesn’t want to talk to them, and engaging with a contact center is frustrating. IVRs can be frustrating during the navigation and it doesn’t always address the need of the customer. For business, it’s a tradeoff between operational efficiency and customer experience. The typical concerns for a typical contact center include the high cost operating costs – agent cost associated with hiring, training, and managing quality agents as well as for infrastructure software and IT staff for addressing customer needs. A sizable telephony infrastructure is needed to be able to handle signaling messaging and media. Services that process media streams must be stateful. There are peaks and low in volumes and your utilization won’t be effective and risk customer churn. Also, typically agents spend a lot of time doing repetitive tasks. The ideal experience for a contact center is to provide 24×7 uninterrupted support to all its customers. For a business, it’s lower operational costs with higher customer service. What is Contact Center AI? Announced by Google in July 2018, Contact Center AI (CCAI) allows enterprises to use AI to augment and improve their contact centers without the need for deep AI expertise. So, let’s look at what exactly it does. Captures what customer said using Speech to Text (STT) with telephony optimized models.Natural Language Understanding (NLU) powered by Google’s Machine Learning.Talk to Customers by converting text into natural-sounding speech.Create conversational experiences across devices and platforms with DialogFlow.Provides one-click integration with top telephony providers and comes with other best in class technology. The Three Pillars of CCAI 1. DialogFlow: Provides Natural Language Understanding (NLU) platform and virtual agent builder interface for chat and voice thus providing 24×7 self-service conversational user interfaces. These conversational solutions can assist in complex scenarios with agent handoff and real-time call transcription. In September, Google introduced a new version of DialogFlow called DialogFlow CX – a new way to design a chatbot that is suitable for large or very complex virtual agents. This new version of DialogFlow is optimized for building conversations with complex scenarios ideal for large contact center requirements. 2. Agent Assist: Agent Assist transcribes calls in real-time, provides step by step assistance (recommended articles, workflows, etc.), and identifies customer intent thus providing continuous support during the calls. Live Transcription transcribes all interactions between customer and agent automatically. This is useful for call analytics and QA purpose posts call. It also suggests useful articles and FAQ answers to help human agents serve customers quickly – reduce time to search and useful for new agents who are less familiar with the taxonomy of the business. 3. Insights: Insight provides the tools Contact Center management needs to analyze historical conversations to see what’s working well and what needs improvement. The data from voice Contact Center voice calls can provide insights to businesses. These include Customer SentimentCall Topics (Why users are calling)Call quality including Silence Scores, Call Duration and Call Escalation PathsDemographicsReduce call center volume Here is a video of how contact center AI can be useful for financial services Future Roadmap According to Gartner, by 2023 customers will prefer to use speech interfaces to initiate 70% of self-service customer interactions, rising from 40% in 2019. While Google’s CCAI is not the only solution in the market, it’s one of the most popular and widely adopted ones. Companies like Verizon, Marks & Spencer, GoDaddy have deployed CCAI and have already seen benefits. However, there is a potential for more companies to embrace it in their call center operations. The adoption rates are expected to increase rapidly in the coming years – many contact centers are expected to shift to the remote-work model due to the COVID-19 pandemic and CCAI can provide an effortless solution.
Contact Centers have leveraged advanced technologies over the decades – starting with basic routing to IVR systems and it remains one of the key areas for businesses to interact and meet the customer’s needs. However, customers are still unhappy, and their expectation of the quality of service continues to increase. The demand for quality services […]
Contact Centers have leveraged advanced technologies over the decades – starting with basic routing to IVR systems and it remains one of the key areas for
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Bharat is a technology consultant, data science enthusiast, and blogger from Kochi, India. He currently works as a Manager at Sutherland, a process transformation organization. His expertise is in the domain of NLP with a focus on developing Conversational AI solutions. He pursued his MBA from Great Lakes Institute of Management, Chennai.