There’s no way around it: technology will continue to enhance the business landscape and companies will need to innovate in order the stay ahead of the competition. As artificial intelligence (AI) and other technologies become more common, customers are conscious of the shift, making it more important than ever for contact centers to quickly adapt to meet the needs of modern customers.
While many members of the C-suite continue to put the customer experience as a top priority, the challenge of creating a customer-first model lies in the lack of tools–or access to the right tools–and siloed data. Even as contact centers use more technologies to manage customer interactions, many find it challenging to extract the voice of customer (VoC) insights hidden in their own data.
In our report, Business Transformation and Analytics: Driving Change in a Customer-Centric World, we found that 85 percent of executives agreed that data and analytics are important to informing sales and marketing changes. However, 68 percent also admitted to avoiding a major change initiative in their business because of an “if it ain’t broke, don’t fix it” attitude.
This same report also revealed that when it comes to data sources, 39 percent of executives admitted to relying on a limited set of numbers that are the easiest to derive, such as:
While these data points are important, they lack the deep insights that can be found in the real customer conversations that happen in the contact center. This can put companies at risk of losing customers to a competitor providing a more engaging experience.
Yet, in most contact centers, only around 2 percent of conversations ever get a second look, and the other 98 percent of insights remain buried. This first-hand intelligence is being wasted when in fact it could be used to drive organizational change, increase profits and improve the customer experience.
So how can analytics help? Data-driven organizations who utilize the insights extracted from their contact center data will have access to the unfiltered VoC, so you’ll know exactly what customers want–and more importantly, what they don’t–and how they want it delivered.
Yet, there are so many ways to use analytics that contact center leaders often are stumped with where to start when they want to deploy it for the first time—or what to do once their first analytics project is up and running.
Calabrio CMO Rebecca Martin, recently shared with customer service expert, Shep Hyken, that “to stay ahead, companies need to figure out how to turn data into action.”
At Calabrio, we consistently see how call center analytics deliver extremely high value when it comes to customer engagement in the contact center–no matter each organization’s unique goals and pain points.
Here are nine ways, grouped by three common organizational objectives, that companies can use analytics and customer data to improve their relationships with customers.
The happier customers are, the more likely they are to remain loyal to an organization–which is why many organizations rank customer retention as a top priority.
The first area we see companies using analytics—including analytics that leverages artificial intelligence (AI)-driven machine learning—is to discover ways to makes a customer’s journey smoother by alleviating contact center pain points and anticipating a customer’s future needs.
Call center analytics easily alleviates these pain points, and in turn, increases customer engagement by:
Understanding how a customer feels about your organization can be a challenge, but it’s a powerful way to gain critical insights about your contact center, your product and your company. That’s because sentiment analysis, a new technology that’s part of call center analytics, can help you understand not only what callers say but how they feel.
Sentiment analysis works by analyzing each element of a customer conversation to assign a sentiment score of positive, negative or neutral. It runs concurrently with a call center workforce optimization solution to correlate sentiment data with metrics such as call duration, hold time, silence, NPS and even evaluator scores. By spotting trends in sentiment in near-real time, organizations can quickly make changes that impact the customer engagement.
Here’s how call center analytics can be used to identify customer sentiment–and enable your organization to quickly respond.
With customers interacting with organizations across more channels than ever before, it’s becoming harder and harder to keep track of all the factors that impact customer experience. Data Management can be used to break down those data silos by combining call center data with automatic call distribution (ACD), interactive voice recognition (IVR), quality monitoring, workforce management, CRM, human resources, homegrown software applications, and even social media data.
By seamlessly compiling both voice of the customer (VoC) and voice-of-the-employee (VoE) information from multiple systems and visually displaying the integrated data, analytics lets contact centers comprehensively understand and continually improve their customer experiences without having to constantly increase budget or headcount to do it.
Here’s how analytics can help improve customer engagement by compiling data from multiple channels:
The race to innovate is well underway, and it’s clear that in order to increase customer engagement in the call center, organizations will need to find ways to better utilize the data that’s right at their fingertips–and they’ll need to leverage analytics to do so.
At its core, analytics automates tasks that otherwise would dominate an agent’s, manager’s or organization’s time, while delivering meaningful insights that can be used to continually improve the customer experience – and might otherwise go entirely undiscovered.
In an era when recognizing the signs of customer dissatisfaction after a customer is lost just won’t do. Companies need to determine when and where customers start to become disengaged with your company. And that means analytics is a “when”—not an “if”—investment. And the “when” is now.
Find out how Radial uses analytics to deliver more actionable insights to its internal agents and clients—read the case study.