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What is the best metric for customer experience improvement?

What is the best metric for customer experience improvement?
While there’s no one-size-fits-all approach to customer experience improvement, AI is allowing new metrics to emerge that deserve the attention of CX leaders.

CX leaders use an assortment of metrics to assess their performance and guide improvement initiatives. Large B2C organisations have long focussed on their net promoter score (NPS) to understand and improve the virality of their products. Service-focused B2B companies constantly measure and optimise customer satisfaction (CSAT) to keep customers loyal. In today’s digital economy, measuring and reducing customer effort is seen as vital to success.

However, even with all of these metrics and the tools to measure them, CX leaders often still struggle to be heard in the boardroom. It’s difficult to build a business case, or gather executive support for a CX improvement programme, if you can’t prove your proposals will actually grow revenue and customer loyalty.

Fortunately, the growing use of AI to analyse customer communications is transforming the capabilities of CX departments. New, AI-enabled metrics are allowing them to understand the complete customer experience in unprecedented depth, and link the customer experience directly with revenue.

Understanding the ‘why’ behind the customer experience

Measuring CSAT, NPS and customer effort are crucial for benchmarking your CX performance. However, they are only the first step in any CX improvement initiative. These scores tell you how your organisation is doing, but they can’t explain why. To drive positive change, CX leaders need the tools to pinpoint the problem areas and identify areas for improvement.

The best way to do this is to analyse your customer communications. Consumers are constantly providing useful feedback on your customer experience across myriad communications channels - from email to calls and chats. Yet extracting the valuable insights from all of these unstructured communications hasn’t been easy.

Some CX leaders have attempted to extract the insight manually and usually at great cost - manual logging impacts productivity and it can never scale fast enough to cover all of an enterprise's communications channels. Sentiment analysis tools also struggle to achieve omnichannel coverage, and they only identify the sentiment of messages of messages anyway. The causes of demand and drivers of complaints and customer issues remain hidden.

Understanding the ‘why’ behind the customer experience requires CX leaders to embrace the latest AI-enabled Communications Mining tools. Communications Mining uses natural language processing (NLP) - a form of AI - to turn communications into structured data. It then extracts the intents - that is, the drivers and reasons for contact - as well as sentiment data from all of the company’s communications at speed and scale.

For the first time, CX leaders gain complete, omnichannel visibility into all company communications channels. They have access to and can understand the full voice of the customer, and that’s without any manual effort or extra headcount.

See how Communications Mining captures the voice of the customer and helps CX leaders understand every customer touchpoint.‍

Quality of Service (QoS)

When AI is applied to customer communications, new and more actionable CX metrics are opened up for use by CX leaders.

With an overview across every comms channel, CX leaders can actually quantify and qualify the quality of service the business provides to its customers. Doing so with Communications Mining is far more time and cost-effective than requiring service agents to log and categorise customer feedback after every interaction.

Understanding the quality of service a business provides requires an understanding of customer sentiment and intent, but it also demands an understanding of context-specific language as well. Every industry has its jargon and even individual companies have a special language that is unique to them. This nomenclature used to stump AI, but no longer.

Through the model training process employees are able to impart their subject matter expertise, including their understanding of context and specialist language. This gives AI models the context they need to interpret client messages accurately. Communications Mining tools are no-code by nature, meaning AI models can be created in a matter of hours rather than weeks, and zero technical knowledge is needed.

With AI, CX leaders are able to understand their client communications at speed and on a massive scale. The models can automatically estimate the impact of these communications on the business’s quality of service, aggregating them into a central quality of service (QoS) score. This opens up the company’s communications - its service - to trend analysis and real-time monitoring.

Automated alerts can even be set up that are triggered by specific events or deviations from acceptable quality of service levels. Service issues can be identified and resolved before they even have the chance to damage the customer relationship. This paves the way for a customer experience that is real-time, responsive, constantly monitored and continuously improving.

Learn more about QoS. 

Customer Experience Index (CXI)

AI can now capture the voice of every customer that communicates with your brand. But what about the silent majority of customers who say nothing? Representing this most valuable of cohorts requires a new kind of metric - but one that is only possible with Communications Mining.

To drive impactful CX improvement you need to know what all your customers care about - not just those who make the most noise. But first, it’s necessary for CX leaders to link the voice of the customer with the operations of the business. It’s the process of integrating the customer insight of CX with the data of Operations.

You start by analysing your customer conversations - your ‘vocal minority’ feedback - with Communications Mining. Extract the root causes of contact, find out why customers are providing feedback, where their pain points lie and what they want to see changed. Once you have this insight, compare it with your operations data. Does any of the feedback relate to coinciding changes in your ops execution? For example, the roll out of a new service, or changes to the ordering process, may correlate with a drop in CSAT.

Where these two areas overlap you now have your CX improvement priorities. No more guesswork or obsessing over fringe concerns. You know what changes will have the biggest impact, what will do the most to improve CX, strengthen loyalty and customer retention.

Combining CX and operations data in this way creates a new metric for CX improvement called the customer experience index (CXI). Online luxury fashion leader FARFETCH has been one of the first to use CXI to identify the most valuable improvement opportunities and transform their customer experience. Find out more about the FARFETCH story. 

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