How to Close the Metrics Gap

Callan Schebella at Five9 explores the metrics gap, looking into the untapped space between what you measure and what you’re doing.

One of businesses’ most valuable resources is being squandered, and it’s costing them time and money. This is because almost three-quarters of businesses are missing the mark when it comes to customer experience (CX) data.

This year, it’s estimated businesses are spending as much as $1.4 million to collect it— only to ignore it.

A recent CX study by research firm Metrigy found not enough is being done by companies to measure and act upon this data.

Over a third of companies were found either to gather customer feedback and do nothing with it (38%), or gather feedback, analyse the data and never act on it (36%).

Beyond the wasted expense of putting initiatives in place to gather CX metrics, these companies are missing out on gaining a competitive advantage when it comes to improving customer satisfaction and operational efficiency. Not only this, it risks damaging customer relationships.

To fully benefit from this customer data and be at the top of the game when it comes to CX, businesses must adopt a lifecycle approach.

This means identifying the right metric – not just gathering the data, but analysing and acting upon it. Right now, Metrigy’s research paints a woeful picture – just over a quarter (26%) of companies have adopted such an approach.

So, how can CX leaders begin to bridge this ‘Metrics gap’ for good?

Overcoming Blindspots

There’s no one better placed to take action on CX metrics than frontline customer service agents. They are, after all, the ones delivering the experience that influences Voice of the Customer (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 scores 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 organisations prioritise improving customer satisfaction over agent productivity.

So, perhaps it’s time to employ a new programme that rewards agents whose CSAT scores are improving, or encourages supervisors to recognise and remedy issues that are driving lower scores. 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 organisations will need to address, particularly as the Great Resignation continues to impact workforce retention.

One Size Doesn’t Fit All

An ICMI study from last year found that more than half of customer contact centres (55%) saw a higher volume of interactions between 2020 and 2021.

When survey respondents were asked to share the top strategies their contact centre 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, like web chat.

These strategies will add new and different variables to the CX metrics equation. But 88% of CX leaders still use the same Key Performance Indicators (KPIs), regardless of channel.

This approach prevents organisations 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.

Contact centres can appropriately expand or reduce staffing to support consumers’ chosen channels by tracking channel usage on a regular basis.

This information can also be used to justify investment in AI and automation technologies that allow customers to self-serve for typical requests.

Self-service can enhance agent productivity, reduce an organisation’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. Increasing containment should result in customers getting their answers more quickly, which is good for CSAT and CES.

Live chat allows agents to better multitask, since it can support more than one chat at a time. This helps customers get their issues resolved faster, however it’s important to keep track of just how many chats agents are handling simultaneously.

This must be correlated with post-interaction surveys, which can help supervisors understand when agents have too much work on their plate, and also whether this negatively affects customer satisfaction. Surveys like this can also set limits on the number of simultaneous chats an agent can handle.

AI and ML – Key to Unlocking the Lifecycle Approach

AI and machine learning can hugely benefit a lifecycle approach to CX metrics. Metrigy’s survey found that over a third (35%) of respondents use AI to speed up the analysis of open-ended customer survey questions. This makes it easier to categorise 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.

Machine learning can be applied to this data over time 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.

Closing the Gap for Good

A company’s customer experience and bottom line can be drastically improved by the wealth data that customer contact centres can provide.

CX leaders can make the most of this underutilised resource by committing to a continuous lifecycle approach; measuring the data, analysing it and acting.

Organisations that link VoC metrics to agent performance, use the right metrics for each channel, and optimise analysis with AI and automation will be best prepared to close the Metrics Gap.

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Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Author: Five9