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This article highlights how using AI capabilities to deliver agent guidance in real-time helps contact centers maximize agent performance, create happy customers, and reduce costs.

AI Enables Contact Center Success by Proactively Managing Interactions in Real-Time

In June 2020, Aberdeen surveyed 307 contact center leaders within companies of all sizes and all industries around the world. When asked about their top priorities, participants cited improving customer experience (CX) results as their number one objective, followed by reducing service costs to drive operational efficiency. Traditionally, contact centers rely on analysis of historical interaction data to evaluate their ability to achieve these goals. While this lookback analysis helps reveal opportunities for improvement, by definition, it means that firms will only learn of inefficiencies in their activities after they observe poor performance as an example, firms would learn causes of customer churn after losing their clientele and analyzing related interaction data. Modern contact centers don’t need to pay such a high price for frustrating and /or losing their clientele to continuously improve their activities.

Using Technologies such as speech analytics, text analytics, desktop analytics, and journey analytics to capture data, contact centers can use AI capabilities (see sidebar) to monitor and analyze customer/agent conversations in real-time to provide agents with (real-time) contextual guidance. Contact centers using AI capabilities to guide agents in such a timely and effective manner enjoy far greater annual improvement in creating happy customers and ensuring client loyalty (Figure1)

AI helps firms analyze historical interaction data through machine learning algorithms to identify activities associated with increased likelihood to achieve desired results such as reduced handle times and improved customer satisfaction. Indeed, Figure 1 shows that contact centers using AI capabilities achieve 3.5x greater annual increase in customer satisfaction rates (10.1% vs. 2.9%). Such increase in customer satisfaction is fueled by firms uncovering inefficiencies in their ability to address client needs and then addressing them. Hence, it’s no surprise that AI users also enjoy 8.0x greater annual improvement (decrease) in customer effort scores.

Typically, many contact centers analyze their performance results periodically (often monthly and quarterly) to evaluate strengths and weaknesses affecting their performance to improve the results of the next period based on those findings. If a contact center is doing such analysis on a quarterly basis, it may take up to three months until contact center leadership recognizes potential problem areas leading to lost clientele, frustrated customers, inefficiencies, and unnecessary costs. Meanwhile, customers will likely switch to other businesses that are more capable in addressing their needs.

Moreover, because of the volume of interaction data for most contact centers, analysis for quality assurance is often done through sampling of interactions (e.g., call recordings and chat transcripts). Thus, contact center leaders don’t have a full view of their activity results to improve upon. Using AI capabilities such as machine learning and automation, contact centers can analyze 100% of all interaction data in real-time — without the need to wait until the next monthly or quarterly analysis. This, in turn allows firms to minimize the risk of poor customer satisfaction resulting in customer churn due to delays in uncovering inefficiencies.

While analysis of interaction data is important, it only helps contact centers when the insights gleaned from real-time analysis is acted upon. Once again, firms using AI capabilities have an advantage in incorporating real-time insights to improve their activities. Specifically, AI capabilities such as automation and prescriptive guidance enable firms to provide agents with contextual guidance through the unified agent desktop to help with recommendations designed to maximize customer satisfaction based on the context of the interaction. The suggestions provided to agents are generated by machine learning analyzing previous similar interaction data to determine which activities were associated with positive results. Those suggestions aren’t always related to agent knowledge. Agent soft skills such as active listening, empathy, and ownership to address client issues also have a direct and significant impact on customer satisfaction results. Using AI to reveal how agents perform across those capabilities and where they should improve to meet customer expectations ultimately helps contact centers better align agent activities with their CX objectives. As such, it’s no surprise that Figure 1 shows that firms using AI capabilities to empower agents with real-time guidance enjoy 3.3x greater annual increase in customer retention rate (10.5% vs. 3.2%).

Findings in Figure 2 provide additional perspective as to how using AI capabilities to provide agents with real-time guidance serves as a competitive advantage to boost agent productivity and performance. Findings from Aberdeen’s The Intelligent Contact Center 2020 survey shows that on average, agents spend 14% of their time seeking information they need to help customers. Providing agents with automated real-time guidance through the agent desktop allows agents to find the information they need, helping boost agent productivity. In fact, Figure 2 shows that firms using AI capabilities enjoy 2.4x greater annual increase in agent productivity (7.4% vs. 3.1%). When agents are more productive, they can do more within the same amount of time. This, in turn allows contact centers to reduce the need for agent overtime and additional labor costs associated with poor agent productivity and utilization. As a result, firms with AI capabilities report 11.5x greater annual improvement (decrease) in service costs (4.6% vs. 0.4%).

While agent productivity and utilization rates are important for contact centers to ensure customer satisfaction and operational efficiency, there are other concerns companies must address to ensure that they keep their clients happy and costs in control. Among these concerns is minimizing the need for customers to repeatedly contact the business due to poor support / no resolution in previous interactions. When customers contact the business repeatedly for help in resolving the same issue, the company has failed in delivering effortless customer experiences that have become a differentiating factor for the modern contact center. By providing agents with relevant real-time guidance, contact centers maximize the likelihood of agents addressing client issues during first contact, and therefore minimize the need for repeat contact. To this point, Figure 2 shows that contact centers using AI capabilities achieve 2.3x greater annual increase in first contact resolution rates (6.3% vs. 2.8%).

Another important measure contact center leaders use to gauge their performance results is service-level agreement (SLA) attainment rates. This refers to the percentage of customer interactions where the company was able to fulfill its promises to clients (e.g., response within one hour). Again, using AI capabilities helps firms reveal patterns that are associated with SLA attainment, and utilizing these insights to adjust activities in real-time to achieve desired results. Contact centers using AI capabilities report 4.5x greater annual increase in SLA attainment rate (4.5% vs. 1.0%) which signals that the benefits of AI extend in helping firms improve various key performance metrics.

It’s important to note that AI capabilities aren’t just helpful for agents to do their jobs. They’re also equally valuable for supervisors. Firms using these capabilities report 2.9% annual improvement in time supervisors spent assisting contact center agents, compared to 0.1% worsening by others. This decrease in the time supervisors are spending to guide their agents is due to agents now being empowered with real-time AI-driven guidance.

Aberdeen’s The Intelligent Contact Center study revealed that the adoption of remote work capabilities increased from 14% in 2019 to 51% in 2020 across contact centers. This means that it’s even more important for agents today to be empowered with the tools and information to do their jobs remotely. The productivity and customer satisfaction gains enjoyed by AI-driven contact centers therefore will remain a competitive advantage for firms that aim to meet and exceed customer objectives — regardless of where their agents and supervisors are physically located. It will also empower supervisors with more real-time and accurate insights so when agents need one-on-one coaching and guidance, it’s truly driven by objective analysis of data through AI capabilities while leveraging the skills and experience of supervisors to improve agent skills and performance. Furthermore, real-time guidance can be used to reinforce coaching — if agents struggle applying the learnings from previous coaching sessions, the use of real-time AI-enabled performance monitoring enables firms to provide agents with repeat coaching and guidance to ensure they better retain and apply their learnings

Key Takeaways

Traditionally, contact centers have adopted a model of “learning by doing” when managing their activities. This meant that they had to learn from prior mistakes and successes to determine their strengths and weaknesses. Since customer needs and market dynamics are fluid, firms must monitor and evaluate all their activity results in real-time to remain competitive. Unfortunately, due to the volume of interactions and limited human resources, quality assurance programs are generally comprised of periodic (monthly or quarterly) evaluations of a sampling of customer interactions. This means firms must hope that the samples analyzed are reflective of all the broader trends impacting their activities.

Modern contact center leaders leverage AI capabilities to take quality assurance to the next level. Instead of periodic reviews and subsequent agent coaching and guidance, they use AI capabilities to review all interaction data in real-time — using the resulting insights to provide agents with real-time contextual guidance. This helps shorten the time to make necessary improvements in agent skills and activities to address evolving client needs. It also minimizes customer frustration and churn due to inefficiencies in service delivery activities.

If you’re not currently using AI capabilities to boost agent productivity and performance, we highly recommend you consider doing so. The solutions providing these capabilities are designed with algorithms that learn based on your unique business results. So, training these algorithms with your historical interaction data allows you to realize quick and significant gains in alignment with the benefits outlined throughout this report. The automated nature of analyzing interaction data also means that you’ll continuously uncover new ways to improve your activities to align with the changing needs of your clientele and the broader marketplace. When adding these AI capabilities, don’t forget to train your supervisors on how to leverage these insights, as those insights are equally helpful for them to do their jobs as they are in helping agents with real-time guidance.