How to bridge the metrics gap that hurts your customer relationships?

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This article was contributed by Callan Schebella, EVP of product management at Five9.

Nearly three-quarters of companies waste one of their most valuable resources, at a cost.

That source is customer experience (CX) data, and companies will spend as much as $1.4 million in 2022 collecting it — only to ignore it. These are some of the findings from the 2022 Customer Experience MetriCast study from Metrigy, a research firm that analyzes business success metrics to advise companies on their technology transformation strategies. According to the survey, 38% of companies collect customer feedback and do nothing with it, and another 36% collect feedback, analyze the data and never act on it.

In addition to the wasted cost of the Voice of the Customer (VoC) initiatives designed to collect CX metrics, these companies miss important opportunities to continuously improve customer satisfaction and operational efficiency, and risk damaging their customer relationships. To take full advantage of customer data, CX leaders must adopt a lifecycle approach to identifying the right metrics, collecting the data, analyzing and acting. According to Metrigy’s research, only 26% of companies have taken such an approach.

Here we take a look at some of the ways CX leaders can bridge the gap.

Connect the dots from outside-in to inside-out

Who better to take action based on your CX metrics than your frontline customer service reps? They are the ones. Who better to take action based on your CX metrics than your frontline customer service reps? They are the ones who deliver the experience that influences VoC metrics like Customer Satisfaction (CSAT), Customer Effort Score (CES), and Net Promoter Score (NPS).

Figure 1: Measures Used to Track Customer Success, MetriCast Customer Experience, Metrigy 2022

But when measuring agent performance, CX leaders often look to productivity and operational efficiency analytics such as Call Handle Time (CHT) and First Contact Resolution (FCR). It’s important to resolve customer issues in a timely manner, but if agents focus solely on turning calls on and off as quickly as possible, the CSAT can begin to drop, along with FCR and CES.

In this scenario, most CX leaders would want to adjust their metric strategies: Metrigy’s research found that 85% of organizations prioritize improving customer satisfaction over agent productivity. So maybe it’s time to implement a new program that rewards agents with rising CSAT scores, or encourages supervisors to identify the issues that are causing the scores to drop. Do those agents need additional training? Or they may benefit from new technology, such as agent assistance tools that can provide real-time coaching during customer interactions. Once CX leaders make an adjustment, they should continue to monitor CSAT closely to see if their actions have made a difference. That is the life cycle approach.

Figure 2: KPIs used to measure agent success, MetriCast customer experience, Metrigy 2022

Another agent performance metric that correlates strongly with CSAT is agent churn. Metrigy found that when agent churn is less than 15% per year, customer satisfaction increases by 26%. But only one in four respondents to the Metrigy survey say they currently measure agent revenue. This is a blind spot that many organizations will need to address as the large layoffs continue to impact employee retention.

Use the right stats for each channel

A 2021 study by the International Customer Management Institute (The Contact Center Workforce of the Future) found that more than half of customer contact centers (55%) saw more interactions between 2020 and 2021. When respondents were asked about the top strategies their contact center is pursuing to meet customer needs, 42% said they plan to improve self-service channels and another 42% plan to launch new digital channels for engagement, like web chat.

These strategies will add new and different variables to the CX metrics equation. But Metrigy’s research found that 88% of CX leaders still use the same Key Performance Indicators (KPIs), regardless of channel. This approach prevents organizations from seeing the full picture around agent performance and customer satisfaction. To bridge the gap, CX leaders can look at metrics such as channels in use, chats handled concurrently, and self-service containment.

By tracking regularly used channels, contact centers can more accurately expand or reduce headcount to support customers’ preferred channels. This data can also be used to build a business case for investments in conversational AI and automation technologies that allow customers to self-serve for routine requests.

Self-service can increase agent productivity, lower an organization’s service costs, and improve VoC metrics – as long as it works well. By measuring the extent to which customer requests are resolved — or mitigated — through self-service, CX leaders can identify any roadblocks. For example, if the containment is low, the FAQ may be outdated or the website may need to be optimized for mobile devices. Increasing containment should lead to faster responses for customers, which is good for CSAT and CES.

Live chat can also help customers resolve their issues faster, as service agents can multitask and support more than one chat at a time. But it’s important to monitor how many chats agents are handling at the same time and correlate that with post-interaction surveys. This helps supervisors understand at what point CSAT can suffer as a result of agent multitasking, and sets limits on the number of simultaneous chats an agent can handle.

Using AI to optimize analytics and action

A lifecycle approach to CX metrics can greatly benefit from AI and machine learning. For example, 35% of respondents to the Metrigy survey are using AI to accelerate the analysis of open-ended questions from customer surveys, making it easier to categorize responses around key topics and spot trends. AI-enabled analytics can also be applied to live chat transcripts and call recordings, helping CX leaders, for example, discover new questions to add to a FAQ to improve self-service containment, or what words and phrases are being used by agents correlate with higher CSAT and NPS scores. With these AI-generated reports, supervisors can gain instant visibility into scripting, compliance, and other quality metrics.

Over time, machine learning can be applied to this data to reinforce best practices. For example, if customer feedback shows that specific agents are rushing their calls, CX leaders can create automated popups to remind those agents to slow down. Conversational AI technologies such as Natural Language Processing and Sentiment Analysis can help detect when customers are feeling frustrated during a conversation and activate real-time coaching insights to guide agents through the next best steps. After the call, an automatic text message can be sent to the client requesting a CES, NPS, or CSAT assessment so CX leaders know if the coaching tools have made a difference.

Final Thoughts

Customer Contact Centers provide a wealth of data that can dramatically improve a company’s customer experience and bottom line. To get the most from this resource, CX leaders must commit to a continuous lifecycle approach to measuring, analyzing, and acting on the data. Organizations that correlate VoC metrics with agent performance, use the right metrics for each channel, and optimize analytics with AI and automation will be well positioned to bridge the metrics gap.

Callan Schebella is the EVP of product management at Five9.

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