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This article was contributed by Callan Schebella, EVP of Product Management at Five 9,
Nearly three-quarters of companies are spending one of their most valuable resources, and that is costing them dearly.
That resource is customer experience (CX) data, and businesses will spend $ 1.4 million to collect it in 2022 – just to ignore it. These are some of the findings from the 2022 Customer Experience Metricast study by Matrigi, a research firm that analyzes enterprise success metrics to advise companies on their technological transformation strategies. According to research, 38% of companies collect customer feedback and do nothing with it, and another 36% collect feedback, analyze data and never act on it.
In addition to the wasteful use of Voice of the Customer (VoC) initiatives put in place to consolidate the CX metrics, these companies miss out on significant 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 identify the right metrics, collect data, analyze it, and operate. According to Matrigi’s research, only 26% of companies have adopted such an approach.
Here, we will look at some ways in which CX leaders can begin to fill the gap.
Connect the dots from the inside out
Who better to take action on your CX metrics than your frontline customer service agents? Are they better off taking action on your CX metrics than your frontline customer service agents? They deliver the experience that influences VoC metrics such as customer satisfaction (CSAT), customer effort score (CES) and net promoter score (NPS).
But when measuring agent performance, CX leaders often focus on analytics related to productivity and operational efficiency, such as call handle time (CHT) and first contact resolution (FCR). Timely resolving customer issues is important, but if agents focus on turning on and off calls as quickly as possible, CSAT with FCR and CES can be reduced.
In this scenario, most CX leaders will want to adjust their metrics strategies: Matrigi’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 for increasing CSAT scores, or encourages supervisors to identify issues that are causing scores to drop. Do those agents need additional training? Or maybe they will benefit from new technology, such as agent support tools that can provide real-time coaching during customer interaction. Once the CX leaders have made adjustments, they should continue to closely monitor the CSAT to see if their actions have made a difference. It is a life cycle approach.
Another agent performance metric that is very consistent with CSAT is agent turnover. Matrix found that when agent turnover is less than 15% per year, customer satisfaction increases by 26%. But only one in four in Matrigi’s survey says they currently measure agent turnover. This is a blind spot that many organizations will need to address as the great resignation continues to affect employee retention.
Use the appropriate 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 greater interactions between 2020 and 2021. When surveys were asked to share top strategies to meet the needs of their contact center customers, 42% said they plan to expand self-service channels, and another 42% plan to launch new digital connection channels such as web chat. Created.
These strategies will add new and different variables to the CX matrix equation. But Matrigi’s research found that 88% of CX leaders still use the same Key Performance Indicators (KPIs) regardless of the channel. This approach prevents organizations from seeing the full picture around agent performance and customer satisfaction. To bridge this gap, CX leaders can begin to look at metrics such as the channels used, the chats handled together, and the self-service control.
Regular tracking of channels used allows contact centers to more precisely extend or reduce staffing to support customer-preferred channels. This data can also be used to create a business case for investing in communication AI and automation technology that allows customers to self-service for regular requests.
Self-service can increase an agent’s productivity, reduce the organization’s cost of serving, and improve VoC metrics – as long as they work well. Resolving customer requests through self-service – or content – to the extent that CX leaders can identify any barriers. For example, if the control is low, the FAQ may be out of date or the website may need to be optimized for mobile devices. Increasing control should allow customers to get their answers faster, which is good for CSAT and CES.
Live chat can also help customers solve their problems faster because service agents can multitask and support more than one chat at a time. But it is important to monitor how many chat agents are handling it together and relate it to post-interaction surveys. This helps the supervisor understand at what stage the CSAT may begin to suffer as a result of agent multitasking, and sets a limit on the number of simultaneous chats that the agent can handle.
Use AI to analyze and optimize action
The lifecycle approach to CX matrix can benefit greatly from AI and machine learning. For example, 35% of Matrigi’s surveys use Survey AI to expedite the analysis of open-ended consumer 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 queries that can be added to FAQs to improve self-service control, or what words by agents and Phrases are used. Corresponds to higher CSAT and NPS scores. With these AI-generated reports, supervisors can gain an immediate understanding of script compliance, compliance compliance and other quality metrics.
Over time, machine learning can be applied to these data to reinforce best practices. For example, if customer feedback indicates that certain agents seem to be rushing to their calls, CX leaders can create automatic screen pops to remind those agents to slow down. Conversational AI technologies such as 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, the customer can be sent an automated text message asking them to rate CES, NPS or CSAT, which will help CX leaders know if the coaching tools have 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 be committed to a continuous lifecycle approach to measuring, analyzing, and working with data. Organizations that associate VoC metrics with agent performance use the right metrics for each channel, and optimized analytics with AI and automation will be in a better position to bridge the matrix gap.
Callan Schebella is the EVP of Product Management at Five9,
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