Contact Center Analytics - Helping you Solve Four Major Problems

Analytics are becoming standard practice in contact centers. Global spending on big data analytics rose to more than $180 billion in 2019, and 84% of enterprises say data and analytics are important to their business growth. But while organizations have invested a significant amount of time and resources into contact center analytics, many just aren’t able to unlock the full spectrum of capabilities that analytics can deliver.

analytics enables contact centers to realize dramatic improvements 

Gartner predicts that through 2022, only 20% of analytics insights will deliver business outcomes, and a survey of Fortune 500 executives found that the number of businesses that identify themselves as being data-driven has declined from 37.1% in 2017 to 31% in 2019, which indicates that organizations aren’t taking full advantage of analytics technology.

The reality is that most organizations (71%, according to a recent Forrester study) don’t have the right kinds of analytics tools or expertise in place to truly transform their contact centers. As a result, contact centers are leaving many challenges unsolved – challenges that modern, artificial intelligence-driven analytics have the ability to overcome.

Challenge: Organizations have too much data to be used for contact center analytics

The amount of data enterprises must sift through to obtain powerful business insights has skyrocketed in the past decade – more than 463 exabytes of data will be produced daily around the world by 2025. Contact centers in particular have to process data from a massive variety of structured and unstructured sources like voice, emails, chat, social media, metadata, billing and more. Bringing all that data together in a way that makes it accessible and actionable takes a substantial amount of time, often leading enterprises to use only partial data sets or selective sampling. In other words, they are not using all the data that’s available and are leaving insights undiscovered.

Organizations can overcome this challenge by leveraging tools that bring together disparate data and remove the manual work associated with cleaning it up. Cutting-edge contact center analytics tools provide capabilities like data cleansing and a data exchange framework that remove the complexity and time involved in prepping data for analytics. For example, these tools can pair related data, such as information from a contact center interaction with data stored in an organization’s CRM. Tools such as these make it easy for contact centers to quickly begin incorporating a wider number of data sources into their analytics solutions.

Challenge: It’s difficult and time-consuming to understand customer sentiment

Most enterprise contact centers handle thousands of interactions each day, which makes it extremely difficult at scale to understand customers’ emotions and the actions they’re likely to take after a call. To pinpoint sentiment, organizations commonly rely on customer surveys that tend to highlight only extreme positive or negative emotions, making it hard to generate real insights. It’s also common for supervisors to manually listen to calls to uncover what customers are thinking and feeling – a costly endeavor.

Customer sentiment can be one of the most impactful insights generated by contact center analytics, informing everything from agent coaching to customer success to product development. To understand sentiment, contact centers need analytics tools with that can automatically assess customer emotions and categorize them to surface trends. Look for tools that can associate sentiment trends with critical topics and the volume at which a sentiment is being expressed during interactions, such as when and how often customers are asking to close accounts. To truly reduce time to insight and make data actionable for nontechnical business leaders, these tools should display sentiment trends visually and enable easy drill-down into the sentiment expressed on each topic.

Challenge: Contact center analytics aren’t robust enough to identify real-time changes

Changes to contact center performance can happen in a moment: Customer demand spikes, self-service tools experience an outage or a new back-office procedure has an unanticipated impact on the customer experience. Contact center real-time monitoring enables organizations to proactively address problems before they grow in size, but enterprises must have the right contact center analytics in place to do so.

To identify changes as they’re happening, enterprises need analytics with tools like anomaly detection. Solutions that intelligently automate the analysis and tracing of any outside-the-norm interactions ensure that irregularities are detected rapidly. These analytics solutions pinpoint normal trending patterns in an organization and alert contact center managers when anomalies are discovered. By using analytics in this way, contact centers can streamline resolution and mitigate the risk of churn.

Challenge: Unless you’re tracking a topic or query, gaps or issues in performance aren’t readily visible

The number of use cases that contact center analytics have is enormous, so how can organizations tell if they’re looking at the right queries? Simply put: You don’t know what you don’t know. With so much data, it’s difficult to identify new or different areas that analytics could or should be supporting, and failing to consider those areas leaves organizations blind to gaps in service and performance.

To build awareness of new topics an organization should be examining with analytics, contact center managers need query coverage analysis. Think of this tool like a smart assistant that double checks your work and monitors for all the things an organization isn’t looking at. It automates the analysis of topics not covered by existing structured queries and elevates new topics that could spark critical insights and help meet organizational goals.

By solving some of the toughest contact center challenges, analytics enables contact centers to realize dramatic improvements like reduced average handle time, increased self-containment rates, lowered staffing costs and increased conversion rates. Implementing analytics doesn’t have to be a complex, time-consuming endeavor; learn more about how the NICE Nexidia Customer Engagement Analytics Framework allows contact centers to quickly adopt analytics and begin bubbling new, actionable insights and solutions to the surface.

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