These days, a lot of work goes into tracking everything that’s done within an organization. From individual performance to ticket volume, pretty much every single stage of the customer support process can be measured and analyzed. Data is the holy grail of the digital age, but tracking it alone isn’t enough.
In a study done by Harvard Business Review, 93% of business leaders agreed that delivering a relevant, reliable and data-driven customer experience is critical to their company’s overall business performance but only 3% of respondents said they are able to act on all of the customer data they collect and only 21% said they are able to act on some data—yikes!
We know that making sense of your data is a large ask and with so much of it, it can be difficult to know where to begin. In this post we aim to take some of the guesswork out and have highlighted some of the most important metrics that can help you identify pain points and make changes in your customer support strategy. Though there are plenty of ways to measure customer success that can be used to gauge and improve the health of your support organization, these are 3 critical metrics for your support strategy that will give you visibility into its effectiveness, what’s working well as well as areas for improvement.
1. Customer Satisfaction
We all know that customer satisfaction is important. However, how do we actually know when our customers are satisfied? According to the Pew Centre, only 9% of customers will respond to satisfaction surveys, and of those that do, 80% will abandon the survey halfway through. So, how can support leaders gauge customer satisfaction in a more quantifiable and reliable way?
One solution is to monitor your Customer Effort Score (CES). CES measures how difficult it is for your customer to resolve their issue. Customers expect immediate access to relevant information, given their context and intent in that given moment. Failure to do so has inevitable consequences on overall customer satisfaction. By leveraging your usage analytics, you can track how long it takes and how hard it is (based on the number clicks it took) for your customers to find what they’re looking for. Not only is this an indicator of customer satisfaction, based on what they’re searching for and not finding, your customers are telling you what they need and expect from your organization, which in turn identifies content gaps and opportunities to better meet their needs.
Support leaders are conditioned to look at their time to resolution and average first response time data. While these can certainly be great indicators for customer satisfaction, it’s critical to look at your data holistically rather than in isolation. If for example, you’re using AI-powered search and recommendations to drive your organization’s self-service efforts, your customers can solve the “easy” queries on their own. With the repetitive queries being automated, your support agents are tasked with more complex issues which will likely have an impact on your time to resolution and average first response time. Your usage analytics will help you to gauge self-service success and its impact on other metrics which is why it’s important to step back to see the forest, not just the trees.
Learn more about how self-service helps to reduce customer effort and increase customer satisfaction in our eBook.
2. Agent Proficiency
It’s one thing for your support agents to be efficient; getting their jobs done as quickly and cost-effectively as possible, it’s another thing for them to be proficient; highly skilled at their jobs and a whole other thing to be able to track it what’s getting done and how well.
Your data holds key insight that can help you measure the effectiveness of your agent-assisted support strategy improve your team’s proficiency as a whole. Looking at things like onboarding time, average handle time and employee engagement will help you to gauge the proficiency of your support organization.
Onboarding can be an arduous and costly process, particularly in a department with a high churn rate. With an insight engine, you can accelerate the time to proficiency for your agents by ensuring they have access to company knowledge and case-critical information instantaneously. What’s more, you can track the behavior of your agents as well as which information they found most useful for their given query and use that insight to enhance the relevance of future searches via machine learning capabilities.
One of the most effective metrics for measuring the proficiency of your team is their average handle time (AHT). This metric is often calculated by adding together an agent’s total talk time, total hold time, and total after-call work and then dividing it by the number of calls handled. An insight engine unifies your company information into a single index, empowering your agents to resolve cases quickly and efficiently. Once again, if you’re using this metric as a key indicator or pillar of your support strategy, it should not be looked at in isolation.
Using your data to track and improve agent proficiency can have a significant impact on your organization. With AI-powered search and recommendations, Medallia increased agent proficiency by 34%, and subsequently saw an improvement in its Net Promoter Score (NPS) with a 5 point increase in one quarter alone.
If you’re interested in hearing more about how you can upskill your agents and deliver effective customer service, check out our eBook.
3. Case Deflection
Case deflection is the rate in which customers are able to find their own answers to issues that they would have otherwise called support or opened a ticket. Every time you deflect a case, you are effectively treating a problem before it’s ever put on your support team’s radar, never reaching your support team and ultimately driving higher customer satisfaction. Knowing how to measure and improve case deflection is an essential aspect of any customer support strategy, but how do you measure something that never happened?
You’ve got to track your customers interactions throughout their experience on your self-service site. Case deflection is measured by implicit and explicit signals that display a clear intention of logging a support case by accessing the case creation page, and aborting the process because the answer is suggested on that same page before a ticket is submitted. The more relevant your suggestions are, the higher your case deflection rate will be and the lower your ticket volume will be. Giving every customer exactly what they need the moment they need is only possible with AI and machine learning. With it, not only can you deliver personalized and relevant customer support, you can quantify the impact of these recommendations use that insight to further enhance your customer support strategy.
To find out how you can measure and improve your case deflection, check out our blog post.
In short, your support strategy should be entirely driven by data. Your customers are telling you what they want, need and expect from your brand or company, you just have to listen. Given the growing number of your customers, multiplied by every interaction they have with your organization, “listening” can seem impossible. An insight engine does the heavy lifting and makes data-driven decisions that will improve your support strategy, enhance your customer experience and exceed your customers expectations.
Want to see what an insight engine can do for your organization? Download your complimentary copy of the latest Gartner Magic Quadrant for Insight Engines report.