How are Omnichannel and Artificial Intelligence Changing Quality Management

The current decade has brought many changes to quality management, including the increase of treating customer interactions using omnichannel call center software – the ability to handle queries flexibly across self-service and agent-assisted media channels with full context. Omnichannel interactions became a goal of companies when it became clear that customers wanted the ability to use multiple methods to handle a single issue. While they may want to talk on the phone with call center agents when they have a problem or concern, they may also want the agent to text relevant information after the call – like an appointment reminder or tracking number.

More recently, the topic of artificial intelligence (AI) and its role in customer care has been one that contact center managers have wanted to learn more about. It often seems that AI is becoming the answer for EVERYTHING but contact center leaders can struggle to understand how it can help specific employees with specific roles.

In December 2018, Lauren Maschio, Product Marketing Manager, Quality Management at NICE, tackled the question of what impact omnichannel and AI are having on quality management. In the 1990s, companies began to see the value in recording calls, primarily for agent evaluation. By the 2000s, adoption of quality management software soared, supporting targeted agent coaching. In a webinar, Lauren and I discussed the changes the current decade has brought to QM and what companies should do in terms of planning for 2020 QM challenges.

Change Brought by Omnichannel Interactions

We started by discussing the dramatic changes in how people interact with companies they do business with – and how their definition of excellent service continue to rise. Data from the recently published NICE inContact 2018 CX Transformation Benchmark Study offers up-to-the-minute insights.

CX researchAs seen in the graphic, 91% of the 2,400+ consumers surveyed agree that they expect companies to provide a seamless experience when moving from one communication method to another, e.g., from phone to text or chat to phone.  And yet, Lauren presented data that only 57% of contact centers monitor interactions other than voice, e.g., email or chat, for quality.

As more and more companies add agents either dedicated to digital channels or who handle a mix of interactions both voice and digital, quality management must expand beyond voice and beyond a single channel. All contact channels must be displayed and tracked with the recording software for assessment. In addition, evaluation tools must be adjusted so agents assigned with diverse tasks are rated accordingly.

The underlying concept and goal of an omnichannel approach involves a seamless customer journey across multiple channels, but it can only be perfected if it is monitored and rated as such. Interactions must be viewed as a series of contacts instead of a collection of isolated incidents.

Change Brought by Artificial Intelligence

One of the biggest changes for contact centers that will result from the implementation of chatbots and voicebots is the need to re-think quality metrics. Just as today we evaluate human agents and interactive voice response (IVRs), we need to understand what metrics make sense for assessing bots.

In some cases, the same metrics can be applied – but how they are evaluated may be different. For example, when considering the Average Handle Time (AHT) for a bot, the goal is not only for customers to complete tasks quickly, but to do so seamlessly and efficiently. Increased session length could
mean users are confused or the conversational flow is inefficient. Reducing AHT is accomplished by analyzing chatbot responses and dialogs with users to discover places where the chatbot fails.

I hope this blog has whet your appetite to learn more about the impacts of omnichannel interactions and artificial intelligence on quality management. If so, register to listen to a replay of the webinar – which includes a quality management case study.