As the primary connection for customers’ concerns, call centers have massive amounts of data pouring in hourly. While companies are tapping this information to personalize messaging and spot trends, contact center management can also leverage Big Data to streamline service processes, boost agent productivity and deliver exceptional customer experiences.
Collecting the information is the easy part. The challenge lies in consolidating, analyzing and converting all this unstructured data into actionable opportunities that drive revenue. Integrating an advanced analytics system into the call center management software gives you powerful tools. Tools that are necessary to evaluate and improve the overall health of the center. Here are three critical ways that managers can utilize Big Data Analytics technology to operate a smart call center:
Create Omnichannel Conversation Threads
One of the biggest challenges call centers are facing right now is how to capture insightful feedback across the multiple contact channels, especially when they track different statistics. A central data repository is essential for combining all customer conversations from distinct sources into a single thread. This gives agents real-time access to what each customer’s journey looks like so that they can deliver a seamless experience.
Harnessing all this data is a critical factor. It enables you to personalize customer services. This can deliver up to eight times the ROI on marketing spend, according to research from McKinsey. By tracking interest in products or features, agents can offer relevant solutions that customers may not have even known existed.
Contextualizing Calls to Improve Service Quality
Call centers traditionally measure success by how well they meet key performance indicators, such as average handle time and first call resolution. However, these traditional KPIs do not provide a complete picture of internal performance and service quality. Predictive voice analytics software examines not only the content of text, audio and video files but also vocal tone, pitch and emotional behaviors so that managers can fully understand the context of interactions between agents and callers.
Predictive analytics also help agents be proactive in guiding the customer journey. It can pinpoint potential problems with certain customers, allowing agents to provide solutions for issues that customers weren’t aware of. This ability to anticipate needs prevents churn. Customers hang up knowing that the company has their best interests in mind. Call centers can also use predictive analytics to calculate which customers are likely to respond favorably to products, messages and post-interaction surveys.
Assess Performance to Improve Efficiency
Using Big Data can also give you valuable insights into how well your contact center is meeting its KPIs. Managers are tasked with the enormous challenges. Continuous improvements, and unstructured data can be the key to understanding why metrics are lagging, where processes are stalling and how operations can be tweaked to achieve greater efficiency and productivity. It is not enough to know if you are meeting targeted AHT and resolution rates. Let’s take it to the next level. You must understand why your call center is not exceeding these goals so that you can place the right resources in the right places to meet clients’ service level agreements.
A root-cause analysis is useful for assessing agent performance, such as how closely they are following call scripts, adhering to company policies and actually responding to customers’ situations with empathy. You can use this information to create a strategies. Reducing agent attrition, including improving training for underperforming operators and establishing achievable call result goals. This deeper analysis can also detect which practices are causing customers to contact service agents. This allows you to make adjustments in meeting their needs.