In the last decade, there has been an influx of customer experience platforms that all offer the ability to capture data and giveÂ actionable insights to deliverÂ exceptional customer service. Without a doubt, these platforms provide metrics that produce valuable guidance when it comes to customer retention, average handle time, first call resolution, service levels, response times, and even customer churn.
While technology certainly has advanced to the point where it’s possible to quickly gather a tremendous amount of data about the customer experience, it can only track customer interactions and engagement. The technology behind these platforms does not provide sufficient insight into why customers do what they do and how their emotions impact their decisions. In other words, metrics do not tell the whole story. What they fail to effectively measure is the emotional connection that customers have or don’t have with a company.
Memories versus Experiences
It’s flawed thinking to assume that any customer experience platform can singlehandedly improve customer engagement. This is because more is required to effectively measure the customers’ emotions or memories of past experiences that influence their decisions and behaviors.
Interestingly, behavioral research has shown that memories are more important than experiences when it comes to determining customers’ behaviors and their purchasing decisions. Nobel Prize winning researcher Daniel Kahneman has offered an explanation for this. His research highlights that an individual perceives reality from two unique perspectives – the experiencing self and the remembering self. While the experiencing self is there throughout the customer journey, it’s the remembering self that evaluates the emotional significance of each experience and decides which memories are worth retaining.
This concept plays out across every customer interaction, regardless of the channel. Most experiences have minimal value and are only kept in the customer’s short-term memory. However, some will be stored in a customer’s long-term memory and will be drummed up every time they think about the brand. Current metrics do not differentiate between non-eventful interactions and highly impactful experiences that will stick around long after customer engagement has ended.
Determining Which Interactions Matter
Perhaps, the holy grail of metrics is a single data type that identifies which moments are most meaningful to customers. However, most customer experience experts agree that to identify which interactions are most significant, both quantitative analytics and qualitative data collection and analysis are required.
Fully-engaged, satisfied customers believe that the company they’re purchasing from delivers on what they promise. Although it’s not entirely possible to determine what leads customers to this belief, by combining qualitative research with quantitative analysis, companies can identify key drivers that can improve engagement and subsequently increase positive customer opinions.
Of course, companies need to determine the right type of qualitative research based upon their industry and customer type. Examples include interviews, focus groups, conversational analysis, and ethnography. Through analysis of this research, companies can obtain a better understanding of customer feelings and emotions and how they can be applied to specific situations. When this analysis is combined with quantitative data, concrete numbers and statistics can be uncovered to provide guidance and drive decisions.
One Brand, Many Channels
Thanks to today’s advanced CRM platforms, it has never been easier to collect customer data, but the challenge is compiling this information and putting it into the context necessary to know how to predict and guide customer behavior. This challenge becomes even more complex when you consider that the customer experience happens on a variety of channels. Whether a customer interacts with a company on a digital channel, a voice channel, or face-to-face, they still only see a single brand and not a channel-specific version of the brand. Because of this, it’s essential to evaluate every channel and ensure that it consistently delivers the customer experience to support the overreaching brand objectives.
What Metrics Are Important for Exceptional Customer Service?
So what metrics should be tracked and evaluated? Any metric worth considering should meet all of the following criteria:
Precise – It needs to be specific enough to offer insight and accuracy. For example, data can show the average number of channels used to resolve an issue or the average handle time of calls.
Credible – It needs to be widely accepted and based on proven methodology.
Reliable – It needs to be applicable across multiple channels and across the customer life cycle.
Accurate – It needs to be representative of your entire customer base and not just an outspoken, small segment of customers.
Predictive – It needs to help determine future behaviors.
Actionable – It needs to offer insight to determine what can be done to make an improvement.
Any metric that doesn’t meet the above criteria has the potential of providing false information that can lead a company to make bad decisions.
Measuring the Gap
The customer experience is fundamentally the measure between customer expectations and the actual experience. Yet, there are countless ways to measure and evaluate the gap between expectations and experience. While every company will have its own unique way of measuring the customer experience, it still will require a mix of quantitative and qualitative data to effectively see the big picture. By taking this multi-pronged approach and not simply relying on CRM-generated metrics, companies can delve deeper into understanding their customers and their behaviors to make decisions based on the most accurate customer experience analytics.
About the author
Walter Lash works for Virtual Hold Technology – A company that offers multichannel callback and virtual queuing solutions to contact centers online. As Manager of Business Intelligence and Analytics for VHT, Walter and his team are responsible for the development and validation of the return on investment resulting for the VHT implementation.