Contact centers are increasingly turning to customer support analytics to improve their service delivery and strengthen contact center efficiency. By measuring key performance indicators (KPIs) like average handle time (AHT) and first contact resolution (FCR) rate, contact center leaders gain a foundational understanding of their operational efficiency. They can also use conversation analytics to go beyond traditional KPIs and granularly measure the key elements of customer conversations to identify a host of nuanced or complex issues both with customers, and operationally, that would otherwise fall under the radar.
With that being said, the real power of customer support analytics lies in its ability to provide deeper (and actionable) insights. By using advanced technology and methods to supplement their existing playbook, contact centers can go beyond surface-level metrics to uncover the 'why' behind customer interactions—ultimately leading to more informed decision-making, better agent performance, and enhanced customer satisfaction.
Beyond the basics: Behind the insights that can take you further
While traditional KPIs provide a high-level snapshot of performance, they often fail to uncover the deeper drivers behind customer behavior and sentiment. This gap is where advanced customer support analytics and conversation intelligence shine. Beyond analyzing call recordings, chat logs, and support tickets, contact centers can use a wide array of tools to manipulate and analyze data sources to paint a more comprehensive picture of the customer journey and overall agent performance.
For example, text and sentiment analysis platforms can extract insights from visual conversation data, revealing patterns in tone, emotion, and recurring keywords of interest. This allows teams to proactively address customer issues or areas for agent improvement. Speech analytics takes it a step further by analyzing vocal attributes within a customer interaction such as pace, pitch, and sentiment, which can offer clues about a customer's urgency or satisfaction even before an agent responds.
Outside of direct conversations, customer journey analytics provide a broader perspective by logging and analyzing data that is gathered when customers navigate across multiple touchpoints. This may present as a customer visiting a website and subsequently placing a phone call or interacting with a chatbot on the website and then sending an email hours later. This encompassing view helps contact centers identify bottlenecks (such as frequent transitions from self-service tools to live support) and optimize processes to reduce friction.
Additionally, predictive analytics and machine learning models can use historical data to forecast future customer behavior, such as churn risk, common rebuttals, or a customer’s likelihood to purchase. By combining these insights with real-time analytics to help an agent within an interaction, contact centers can become proactive instead of reactive. Incorporating these advanced analytics tools allows contact center leaders to move beyond basic metrics—not only improving operational efficiency but also delivering a seamless customer experience at every stage of the journey.
The benefits of data-driven customer service
Now that the “what” and “how” of advanced customer service analytics have been explored, let’s dive into the “why.” Why should leaders invest in a data-driven approach to customer service? The answer lies in the transformative benefits advanced analytics deliver—both for customers and for the teams serving them.
Adopting a data-driven strategy empowers contact centers to proactively address issues, preventing minor customer queries or complaints from escalating into larger frustrations and dampening operational efficiency. When using the data and advanced analytics from customer interactions, contact center leaders have the opportunity to positively impact service delivery and increase agent performance. These insights are impactful for workforce development and wider organizational goals or improvement. By identifying specific patterns and pain points in customer interactions, contact centers can pinpoint training and development needs, modify offerings or products to better suit their customer, and equip agents with the skills they need to deliver exceptional support.
Creovai customers have used conversation intelligence and real-time agent guidance to enable real-time insights that were once painstakingly manual. Darryl Nixon, Sr. Manager of Quality at Noom, explains:
“We're able to use some of the category functionality within Creovai to produce a line graph or a bar graph that shows percent of volume reduction or improvement in [the amount of] happy customers just based on the language that they’re using. Those hero moments that would usually take having to have a check sheet and deploying a QA team to look at 500 interactions to let me know what they found is now at the click of a button."
This kind of process agility is monumental for cross-departmental collaboration as well. Alison Miles, VP, Members at Connexus Energy, highlights how Creovai’s conversation intelligence platform helps her team to uncover hidden trends:
“The beautiful thing with Creovai is with category search, I can pull up exactly how many calls [a frequent customer service issue] happens--and note that our CRM is limited in terms of how a call is classified. If a member calls in and talks about 18 different things, the agent has to pick one thing. That doesn’t matter anymore. We can grab those different categories, we can grab the language that was used, and then go to another department and say this [frequent customer service issue] is actually a really big deal."
By leveraging Creovai’s contact center reporting and customer service analytics, organizations can not only improve their customer experience but also drive organizational efficiency and meaningful business outcomes. Data-driven customer service is not a luxury for these Creovai customers—it’s a necessity for staying competitive.
Next steps for becoming a data-driven contact center
If you want to get started using customer support data analytics to improve customer satisfaction and operational performance, introspectively evaluate where you’re at today. Transitioning to a data-driven contact center requires knowing what problem you’d like to solve before finding the correct solution or platform to help solve it. Take the next step by investing in an agile and comprehensive analytics platform that can seamlessly integrate with your existing systems. This will ensure you have access to historical and subjective data.
Foster a culture of data literacy within your organization. With a tool like Creovai, provide ongoing training and support to help your team understand how to interpret and act on data insights. Encourage collaboration between departments to ensure that insights are shared and leveraged across the organization.
Finally, continuously monitor and refine your strategy with the insights customer conversation analytics can provide. The space of customer service is always evolving and changing, so staying agile will allow you to adapt and maintain a competitive advantage.
For more detailed guidance and information about taking the step towards data-driven customer service and operational improvement, check out Creovai's resources today.
