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This article was contributed by Callan Schebella, EVP of product management at Five9.
Nearly three-quarters of companies are wasting one of their most valuable resources, and it’s costing them dearly.
That resource is customer experience (CX) data, and businesses will spend as much as $1.4 million to collect it in 2022 — only to ignore it. These are some findings from the 2022 Customer Experience MetriCast study by Metrigy, a research firm that analyzes enterprise success metrics to advise companies on their technology transformation strategies. According to the research, 38% of companies gather customer feedback and do nothing with it, and another 36% gather feedback, analyze the data and never act on it.
Beyond the wasted expense of the Voice of the Customer (VoC) initiatives put in place to gather CX metrics, these companies miss out on important opportunities to continually improve customer satisfaction and operational efficiency, and risk damaging their customer relationships. To fully benefit from customer data, CX leaders must adopt a lifecycle approach toward identifying the right metrics, gathering the data, analyzing it and acting. Only 26% of companies have adopted such an approach, according to Metrigy’s research.
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Here, we’ll look at a few ways that CX leaders can begin to bridge the gap.
Connect the dots from outside-in to inside-out
Who better to take action on your CX metrics than your frontline customer service agents? They are the ones Who better to take action on your CX metrics than your frontline customer service agents? They are the ones delivering the experience that influences VoC metrics like Customer Satisfaction (CSAT), Customer Effort Score (CES) and Net Promoter Score (NPS).
But when measuring agent performance, CX leaders often look at analytics related to productivity and operational efficiency, such as Call Handle Time (CHT) and First Contact Resolution (FCR). Solving customer issues in a timely manner is important, but if agents are singularly focused on getting on and off calls as quickly as possible, CSAT may begin to drop, along with FCR and CES.
In this scenario, most CX leaders would want to adjust their metrics 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 to encourage supervisors to identify the issues that are causing scores to fall. Do those agents need additional training? Or maybe they’d benefit from new technology, such as agent assist tools that can provide real-time coaching during customer interactions. Once CX leaders have made an adjustment, they should continue closely monitoring CSAT to see if their actions made a difference. That’s the lifecycle approach.
Another agent performance metric that correlates highly with CSAT is agent turnover. Metrigy found that when agent turnover rates are less than 15% per year, customer satisfaction increases by 26%. But only one in four respondents to Metrigy’s survey say they currently measure Agent Turnover. This is a blind spot that many organizations will need to address as the Great Resignation continues to impact workforce retention.
Use the right metrics 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 a higher volume of interactions between 2020 and 2021. When survey respondents were asked to share the top strategies their contact center is pursuing to satisfy customer needs, 42% said they plan to enhance self-service channels, and another 42% plan to launch new digital engagement channels, such as 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 begin to look at metrics such as channels in use, chats handled simultaneously and self-service containment.
Regularly tracking channels in use allows contact centers to more accurately expand or reduce staffing to support customers’ preferred channels. This data can also be used to make a business case for investing in conversational AI and automation technologies that allow customers to self-serve for routine requests.
Self-service can enhance agent productivity, reduce an organization’s cost to serve and improve VoC metrics – so long as it works well. Measuring the extent to which customer requests are resolved – or contained – by self-service allows CX leaders to identify any roadblocks. For example, if containment is low, the FAQ may be outdated, or the website might need to be optimized for mobile devices. Increasing containment should result in customers getting their answers more quickly, which is good for CSAT and CES.
Live chat can also help customers get their issues resolved more quickly because 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 simultaneously and correlate that to post-interaction surveys. This helps supervisors understand at what point CSAT may start to suffer as a result of agent multitasking, and set limits on the number of simultaneous chats an agent can handle.
Use AI to optimize analysis and action
A lifecycle approach to CX metrics can benefit tremendously from AI and machine learning. For example, 35% of respondents to Metrigy’s survey use AI to speed up the analysis of open-ended customer survey questions, 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, which, for example, can help CX leaders discover new questions that could be added to an FAQ to improve self-service containment, or which words and phrases used by agents correlate with higher CSAT and NPS scores. With these AI-generated reports, supervisors can gain immediate insight into script adherence, compliance adherence and other quality metrics.
Over time, machine learning can be applied to this data to reinforce best practices. For example, if customer feedback reveals that specific agents seem to rush through their calls, CX leaders can cue up automated screen pops to remind those agents to slow down. Conversational AI technologies like Natural Language Processing and Sentiment Analysis can help detect when customers feel frustrated during a call and trigger real-time coaching insights to guide agents through the next best steps. After the call, an automated text message could be sent to the customer asking them to rate CES, NPS or CSAT, which will help CX leaders know if the coaching tools made a difference.
Customer contact centers provide a wealth of data that can dramatically improve a company’s customer experience and bottom line. To make the most of this resource, CX leaders must commit to a continuous lifecycle approach of measuring the data, analyzing it and acting. Organizations that correlate VoC metrics with agent performance, use the right metrics for each channel, and optimize analysis 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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