The Future of Intelligent Decision Support Systems in Contact Centers

Decision Support Systems (DSS) drive faster, smarter decisions based on objective data, rather than on subjective criteria or personal instinct. They offer insights and proposed courses of action to decision-makers based on problem diagnosis, previous actions taken, the outcomes of those actions and other relevant contextual info.

These systems are relevant for many verticals including healthcare, finance, weather prediction, call and chat centers, desktop apps, info kiosks and more.

This article underlines the value of DSS in customer service and focuses on the potential computer vision brings to call/chat centers, where providing a positive customer experience (CX) at scale is critical to success in the current competitive business climate.

Current state of DSS in contact centers

Call centers are highly stressed environments. Agents performance is closely monitored, and their actions have business and financial implications, such as dropping a sale, dispatching a technician unnecessarily, losing a customer, or worse, setting off a social media backlash over a bad experience.

Implementing decision support tools backs up the agent, putting the right information at the tip of their fingers, helping them work smarter and perform better in operational KPIs such as First Call Resolution (FCR) and Average Handling Time (AHT). It also improves their ability to bring customer issues to successful resolution, enhancing both customer satisfaction and inline agent satisfaction.

For example, a DSS can suggest a series of questions in order to troubleshoot an issue, and then based on the customer’s input, select the most suitable branch in the decision-tree to find the quickest path to resolution.

From DSS to IDSS — the AI Factor

Traditional DSS are decision tree driven solutions, built by humans and automated. The relatively new integration of artificial intelligence (AI) into DSS has created more sophisticated, problem-oriented intelligent decision-support systems (IDSS) that can understand a wide range of inputs and select the next best course of action.

Using machine learning, IDSS learn from previous cases and improve with time, providing a more efficient decision-making mechanism that is continuously evolving.

IDSS allow human agents to focus more on their soft skills and quality of the interaction, and less on scripts and manuals. Take, for example, one of the more complex issues handled in contact centers — technical support.

Complicated scripts are used to identify and troubleshoot issues, and guide customers through to resolution. The process requires possession of large datasets that are difficult for even the most highly trained agent to remember.

Vision — The Missing Link

While effective, the IDSS flow has a major weakness — it relies heavily on input generated by the consumer, which is highly subjective and often error-prone.

For example, when troubleshooting, the IDSS has to analyze device status, connections or cables, or even evaluate damage for insurance Relying on inputs generated by an untrained consumer and conveyed to an agent in spoken or written language often results in errors. Wrong inputs undermine the ability of the IDSS to identify the problem. For IDSS to provide additional value, new technologies must be exploited to support customers on a higher level.

The solution: Employ computer vision and deep learning to act as the “eyes” of the IDSS, adding a visual pillar to drive performance to a whole new level.

Computer Vision and IDSS: Stronger Together

Visual-based artificial intelligence is the ability to harness computer vision and deep learning to analyze images and video streams in order to cognitively recognize and understand objects, situations, statuses, issues etc. Computer vision is already being utilized in a wide range of applications. It recognizes faces and emotions in cameras; it helps self-driving cars read traffic signs and avoid pedestrians; it allows factory robots to monitor problems in the production line. In customer service, it helps the IDSS see the problem, as a virtual agent.

With computer vision powered IDSS, the computer identifies devices, and points to the most relevant next question based on consumer input. The agent’s cognitive focus shifts from the mechanics of each step of the process to the big picture — satisfying the customer.

Computer vision minimizes the need for customer input, by enabling the IDSS to automatically collect data within a visual session, including images of equipment, physical environment, documents etc. It increases the accuracy of recognition, identification and decision making, and empowers agents to provide better support based on objective inputs.

Using deep learning and computer vision technologies, agents automatically and instantly identify devices within visual sessions, correlate them with past cases involving either the same device or similar technical issue, and suggest concrete Next-Best Action measures for resolving the issue based on the crowdsourced knowledge generated from previous sessions .

Visual IDSS — Transforming Contact Centers of the Future

Successful enterprises are proactively shaping the CX by investing in innovative technologies that improve operational efficiencies and empower their human agents.

Computer vision-based IDSS are ideal tools that provide a wide range of benefits for contact centers. They represent a powerful solution that removes the dependency on human input, and allows human agents to focus on the big picture, using their soft skills to deliver an enhanced customer experience. By combining cognitive understanding and cognitive vision, IDSS deliver significantly enhanced decision-making capabilities and Next-Best Action suggestions, and drive improvement in KPIs and business results.

This post was originally published on the TechSee Blog