Visual Engagement in Customer Service
January 21st, 12:00pm ET


Computer Vision in the Call Center – The New CX Frontier

Computer vision in call center

Companies are increasingly turning to artificial intelligence (AI)-powered contact center solutions to meet consumers’ growing demand for better CX, reduce costs and alleviate pressure on agents. With AI, call centers are equipped with a wide range of voice analytics, enabling recognition of customer, accent, gender, and emotion, as well as powering conversational IVRs and voice based virtual assistants. Natural Language Processing (NLP) algorithms have enabled AI-powered tools to grasp context, power smart classification, routing of customer inquiries, and create conversational chatbots. With structured data analysis, predictive analytics can now be performed by extracting information from mass amounts of data and using it to predict trends and future behavior patterns, such as customer churn.

But there is still one missing element that has barred AI from radically transforming the customer experience.

What is the secret sauce in the AI mix that stands at the core of this customer support transformation?

Computer vision.

Can a computer see?

Computer vision is the science that attempts to give visual capabilities to a machine. Via automatic extraction and analysis, computer vision enables the machine to extract meaningful information directly from an image, and then utilize learned algorithms to achieve automatic visual understanding.

Computer vision is being utilized in a wide range of applications. It recognizes faces and smiles in cameras; it helps self-driving cars read traffic signs and avoid pedestrians; it allows factor robots to monitor problems in the production line. In customer service, it helps the computer see the problem, as a true virtual technician.

Object recognition in a technical support model

Deep learning-based object recognition offers incredible accuracy that makes object recognition a core technology for the future virtual technician as the ability to see the problem is essential to finding a rapid resolution. Object recognition enables the computer to identify technical devices or parts – including the exact device model, and each device part, such as ports, cables or display panel, the device color, and be able to distinguish the particular device from others. In addition, the computer can recognize objects found in a live customer environment; for example, in a variety of backgrounds, positions, angles or lighting.

Computer vision can be utilized to perform as a Virtual Assistant for customer service agents, delivering effective decision support during the agent-customer interaction. Considered a hybrid model, the agent’s performance is enhanced by the computer’s ability to quickly identify devices and technical issues, as well as to provide faster resolutions. This has been proven to reduce agent training time and streamline the entire support process.

Computer vision also enables gradual automation towards full self service with device recognition and augmentation. Via a smartphone, the customer indicates the faulty device, and the virtual assistant can recognize devices, detect motions, and interact in real time with the customer. The virtual assistant uses augmented reality to guide the customer to resolution via a step-by-step process and is also able to correct the customer in case of errors, ensuring that the resolution is successful.

Powered by advances in Deep Learning

Computer vision is becoming increasingly effective in remote customer care and has been supported by unprecedented advances in AI over the past few years.  The most advanced form of AI – Deep Learning – enables independent learning of massive data sets. Unlike classic methods in which a human expert needs to define features (rules and attributes), deep learning can learn directly from data without human intervention, whether supervised or unsupervised. In some fields, deep learning achieves far greater results than classic machine learning methods. These technologies have driven significant improvements in computer vision accuracy and performance and have enabled the virtual technicians of the future.

For an enterprise to successfully implement a computer vision solution within its contact center, specific Deep Learning challenges must be overcome, and an effective strategy must be designed that takes into account the business case, available data, resources and desired output.

Click here to download Smart Vision – The White Paper and discover the challenges involved with the AI transformation, and the specific steps that businesses must take to successfully execute a deeper implementation of AI – computer vision – within their customer care operations.


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