When you picture a contact center, you may simply imagine a room full of representatives answering calls. Meeting the immediate needs of customers and problem-solving is important for your organization’s standing with users, but other factors at play determine a customer’s contact center experience as well. A well-run contact center entails a significant amount of behind-the-scenes, data-driven work that must take place for a contact center to achieve success. One element of this success is accomplished through having methods for consistent optimization of customer experiences while ensuring operational efficiency.
Contact center analytics are used to gain important insights and dive deeper into the data gathered. While contact center reporting looks at historical data to review past performance, contact center analytics offer a broader, real-time perspective focused on future decision-making.
Analytics are crucial for contact centers aiming to improve customer satisfaction, comply with regulatory mandates, and streamline operational costs. This is particularly important for business leaders to make decisions that will enhance analytics in customer service, support their staff, and stay compliant with constantly evolving industry mandates.
Contact center analytics are important, but they are only as strong as the metrics and underlying source data available to analyze. The Kenway Guide to Contact Center Reporting dives into more detail regarding key metrics to track, but as a refresher, some of the biggest key metrics that set contact center analytics up for success are:
Consistently monitoring these metrics helps contact centers identify performance bottlenecks, adjust staffing according to customer demand, and implement customer experience and process improvements. For instance, a low FCR could indicate training gaps or inefficiencies, while tracking AHT can balance customer satisfaction with operational costs. On the flip side, tracking metrics where your organization’s numbers look good will help you capitalize on success and prioritize efforts for improvement.
Data architecture determines how data is collected, stored, and accessed across all of your organization’s systems. Creating reliable data architecture can be challenging due to disparate storage systems, including modern and legacy systems. There could be millions of rows of data to sift through in the organization process, which is a time-consuming and expensive endeavor.
While tedious, this process is extremely important for analytics efforts because without a standardized data structure, your analytics will become fragmented at best and unreliable at worst. Before moving further into a plan for your data architecture, communicate the necessity of strong data architecture for call center analytics to your stakeholders to ensure complete buy-in.
When data architecture is well-defined, your data will be secure, consistent, accurate, and accessible for every user. This means that data sets will be interpreted in the same way across your organization, so there is no risk of inaccurate reporting and forecasting.
A robust infrastructure also takes compliance into account. Telecommunications is one example of a call center industry that is heavily mandated by government agencies, so it is crucial to have clearly defined architecture for successful compliance.
Another necessary element of modern contact centers is using Business Intelligence (BI) tools and contact center analytics software. These tools enable companies to transform raw data into visual insights so decision-makers can understand performance trends and implement changes where necessary.
Popular BI tools such as Power BI, Tableau, Qlik, and Domo are effective for visualizing contact center data. These platforms offer the ability to develop dashboards that provide real-time insights into key metrics like call volume, customer satisfaction, and agent performance. Your CCaaS platform may offer built-in reporting tools, but we strongly suggest using a third-party BI tool for more tailored and powerful analysis.
Now that we’ve laid out the necessary components for successful contact center analytics, let’s cover the most practical tips for contact center analytics.
At Kenway Consulting, we help organizations implement customized contact center solutions that align with their unique needs. Whether your company needs to improve its data architecture, integrate advanced analytics tools, or simply optimize day-to-day operations, we offer tailored support based on your unique needs. Contact us to learn how Kenway can bring your organization’s contact center analytics up to speed.
Optimizing contact center analytics requires clean data, aligned stakeholders with definite buy-in, and choosing the right BI tools for your infrastructure.
Contact center analytics is the analysis of contact center data to gain broad insights that will enable future decision-making and optimize customer experiences, all aimed to mature your existing contact center further.
Some emerging trends include the use of AI for predictive analytics, real-time data visualization, and personalized customer experiences enabled by advanced data modeling.
Key metrics include Average Handle Time (AHT), First Call Resolution (FCR), Customer Satisfaction (CSAT), and Agent Utilization Rate.
