How AI can help contact centres

The Covid-19 pandemic has created intense challenges in contact centres today. The archaic systems of yesterday cannot handle the demands of rapidly evolving needs of customer service and support of today. Customer service operations are unable to effectively scale environments and processes to deliver quality experiences and speedy resolutions.

Contact centres had been struggling to deliver excellent customer experiences efficiently, even before the pandemic. One area that does not get nearly the attention it must in terms of improving agent efficiency and productivity, is automating after-call work and call dispositions.

After-call work in contact centres is when summaries are written, calls categorised, actions taken or scheduled, and various other steps followed. These tasks ensure that the history of each customer’s conversation is accurately documented for future interactions. After-call work is a crucial step in customer service and experience. Although composed of routine tasks, it works as a driver for customer satisfaction.

Impact of after-call work on customers and contact centres

The importance of the actual conversation between a customer and an agent should not be underestimated, nor should the quality and effectiveness of after-call work. After-call tasks are critical to a customer’s overall perception of a brand and its experience.

A promise to follow-up which is not met, tasks not performed correctly, or errors made because of manual data entry can quickly negate the positive effects of a good conversational experience. In case of a customer follow-up, can a different agent immediately understand the history of the previous conversations?

Customer service operations must balance the amount of work accomplished, both during customer calls and after the call. Long customer wait times that arise due to data entry is a common source of frustration. After-call work that takes an average of two minutes indicates it takes that much longer before agents are available for the next call. The long hold time is especially frustrating today for customers. This is primarily due to the increased call volumes and customers’ preference to speak with a human agent.

According to Accenture’s Keep Me Index/Moments That Matter study from last year, 58% more customers prefer to solve urgent issues by calling for support rather than using other channels.

How much time should after-call work take?

There are no industry standards or benchmarks for the time spent on after-call work. The time should be as short as possible while still enabling quality and completeness. An agent cannot interact with another customer until the prior interaction is wrapped up, which is why after-call work counts as the average handling time of a call.

According to calculations conducted from February to August 2020 by Call Centre Helper’s Erlang Calculator, average handle time across multiple industries and geographies is six minutes and 10 seconds. AI and automation can reduce this after-call work, shorten the average handle time and call waiting duration, while, at the same time, increase agent productivity and efficiency.

Using AI for after-call work

Conversation-centric AI and automation are delivering significant and measurable business value, including automating after-call work where traditional solutions have failed. Conversational Service Automation (CSA) uses conversational AI, robotic process automation (RPA), and workflow automation to bridge humans and machines as well as back- and front-office operations throughout a customer’s conversation with a brand.

For interactions where a human agent is involved, CSA first automatically transfers interactions from conversational self-service to a human agent. The platform uses AI to detect a customer’s emotion, sentiment, and intent in real time to provide agents with the next best action and coaching. CSA automatically listens and transcribes calls in real time. The platform simultaneously serves as the agent’s co-pilot by providing insights and recommendations during customer calls.

After a call has been completed, a CSA platform presents the call summary for the agent to edit and confirm before automatically updating the customer relationship management (CRM) system. Automated call disposition to maintain quality of call categories is also provided. This allows an interaction with customers to remain conversational as the agent only needs to review and if necessary, edit the summary. CSA enables agents to wrap up calls efficiently, close post-summary work quickly and effectively, thus, reducing the number of errors.

Conversational AI delivers measurable results

Conversational AI analyses calls and automation eliminates the manual, repetitive tasks such as conversation data entry. A CSA platform shortens the time needed for after-call work, improves agent efficiency, ensures quality and consistency of wrap-up efforts, and reduces the average handle time. Automating call disposition improves accuracy and drives better quality of call categories in CRM systems.

Automation also reduces agent stress over capturing important information, both during and after a call. This allows them to pay attention to customer conversations without worrying about capturing every word said. A CSA platform automates and improves the entire conversation—self-service, human-to-human, and after the call.

Through the CSA platform, businesses can transform contact centres and customer experience while simultaneously reducing costs and maintaining high quality even as call volumes escalate.

Views are personal. The author is co-founder and president of Asia Pacific, Uniphore.