Discover, Improve, Remove: The Trifecta for Your Contact Center


Combining AI and intelligent automation technologies to address large-scale problems continues to gain popularity in enterprises today and can result in serious gains for the end user. Take the issue of increasing volumes in the contact center. This has been a thorn in the side of many an organization, especially while struggling to improve customer satisfaction and revenue generation at the same time.

Until now, seriously altering key metrics such as inbound volume in a contact center has been like turning an oil tanker – slow. Of course, it’s not for lack of trying. If every initiative in the process improvement pipeline delivered the expected benefit, you’d end up eliminating all customer contacts entirely.

But reality is more complicated, and obstacles exist. Today, intelligent automation technologies are helping us make great strides in operational efficiency.

Common roadblocks and how intelligent automation can address them

Lack of data. Useful data in the contact center can be quite scarce, if available at all, and subject to interpretation by the people helping collate it. In fact, many systems allow you to enter only a single reason for customer contact, when, in reality, the customer could have had upward of two or three reasons to make contact. This lack of understanding about demand is one of the most common challenges to reducing contact volumes. If you lack accurate data to quantify your organization’s “as is” today, how do you prove the actions you are taking to decrease customer demand have had the desired effect in the future?

Enterprises today need technology that can ingest their customer communications and highlight potential areas of improvement that will make a noticeable difference to their customer volume and user experience. Analytics must be an ongoing part of identifying and prioritizing process improvements. What you can’t measure is difficult to improve. Analytics are especially important when you need to assess the value of a conversational AI bot in your organization. Use analytics to help you identify which conversations to automate, and remove those contacts from the manned channels.

Poor alignment across channels. As the number of channels an organization supports increases – from traditional voice, to messaging, online chat, email, to social and conversational AI – customer support can become fragmented. Different technologies and different processes beget differing SLAs and experiences for customers. Disconnects between agent knowledge and online content also reveal a fragmented business to a customer, despite the best efforts to deliver an “omni channel” experience.

To automate large numbers of contacts, you have to create a single version of the truth. This is key to not only ensuring your agents are singing from the same hymn sheet, but also making sure automations are aligned to your best answers. Documenting the answers to your simplest and most complex customer queries will give you the foundation you need to start understanding your automation opportunity. Remember that not every query is automatable.

Juggling too many plates. If there is one guarantee about a customer contact center, it’s that it is busy. A contact center can be involved in tens if not hundreds of concurrent initiatives, from system and process changes to demand changes, staffing challenges, regulatory amends and the list goes on. Contact center employees are focused so acutely in the details of their work that they can find it difficult to lift their head and assess the efficacy of what they are doing and make sure it is delivering customer satisfaction. Any change to the process or system will affect the agent and thereby affect the customer experience. This is why investments in upskilling, training and rolling out new technologies are worthy ones.  

A single automated bot that covers all channels and generates meaningful analytics can play a transformative role. You only need to train a bot once, and it will offer the same experience across all instances. This means agents will need only a briefing on the expected changes to occur when the bot completes a new task or promotes a newly updated compliance script. The result is consistent work that helps agents juggle fewer plates and focus on the calls that need a human touch.

Residual contacts. As conversational AI improves and removes simple queries, it leaves longer, more complex conversations for human agents to handle. Unfortunately for the agents, they still have the pressure of an average handling time (AHT) target and limited time to resolve. Assisted automation technology can grab customer data out of multiple systems and present it to the human agent in the first few seconds of a contact. It can help ensure compliance scripts are read, complete manual tasks in the background, and even add a note in the customer history. All of this helps reduce total contact handling time (TCHT) by enabling swifter contact resolution. Because the automated assistant allows the human agent to concentrate on conversing with the customer, even complex contacts are dealt with better.

The goal is to discover, remove and improve

Until now, technologies at the right price and people with the right level of expertise to make the most of them have both been difficult to find. There is no easy path to wide-scale transformation of your contact center, but using proven enterprise-grade technologies as part of a multi-faceted approach can start to remove the existing barriers, provide a data-driven approach for automation and handle the residual contacts as efficiently as possible.

Configuring best-of-breed intelligent automation to do what it does best in the contact center can provide ROI in weeks and months – not years. A technology-first approach is paying dividends for early pioneers, whose biggest complaint now is that they didn't start years ago.

Join me and special guests from Re:infer, IPSoft and NICE for an ISG Smartalk to explore how organizations are leveraging technology to understand, automate and address customer demand while increasing competitive advantage.


About the author

Wayne Butterfield

Wayne Butterfield

Wayne is an automation pioneer, initially starting out as an early adopter of RPA in 2010, creating one of the first Enterprise scale RPA operations. His early setbacks at Telefonica UK, led to many of the best practices now instilled across RPA centres of excellence around the globe. Customer centric at heart, Wayne also specialises in Customer Service Transformation, and has been helping brands in becoming more Digitally focused for their customers. Wayne is an expert in Online Chat, Social Media and Online Communities, meaning he is perfectly placed to help take advantage of Chat Bots & Virtual Assistants. More recently Wayne has concentrate on Cognitive & AI automation, where he leads the European AI Automation practice, helping brands take advantage of this new wave of automation capability.