Call centers (also referred to as contact centers) sit at the core of many blooming businesses. Today’s call center technology is complex and powerful, using a broad range of evolving technologies for the web, IVR, and other channels that must work in unity to deliver a seamless omnichannel customer journey.
Interaction mediums are changing at a rapid pace, and this is expected to continue. With more interactions occurring on virtual channels, like video chat and voice applications, and fewer in person.
The pandemic has dramatically altered how customers buy and how businesses are expected to accommodate and reply to customer needs. While retail businesses have long competed on Customer Experience (CX), present-day consumers have grown accustomed to new ways of communicating with brands. With a pronounced center toward digital sales and remote workforces, agents are now required to service consumers in more complex, holistic ways than ever.
The rapidly growing call center market:
The call center market size was valued at USD $18.14 Billion in 2018 and is expected to reach USD $53.65 Billion by 2026, rising at a CAGR of 14.7% from 2019 to 2026.
In the present business world, the expectation of on-demand customer service is greater than ever. Customers must meet or exceed customer expectations-with and have very less tolerance for sub-par service; else, they risk failure in a competitive market.
Delivering a dynamic customer service that allows companies to provide crucial insights into the CX is ultimately driving the growth of the call center analytics market. Below mentioned are a few of the key drivers:
- The adoption of social media platforms by customers
- Remote working
- A rise in multi-channel communication
- Advancements in technology in the areas of AI and ML
- The adoption of advanced analytics
What is call center analytics for?
The critical purpose of call center analytics is to gather and analyze customer data to reveal valuable insights about your company’s performance and turn it into actionable insights. This includes Customer Satisfaction (CSAT), revenue, customer effort score, customer retention, and service-level agreement (SLA) performance.
Customer analytics scrutinize multiple customer-related data sources to identify customer trends and interaction opportunities to serve as a source for modeling. Analytics can be predictive or historical; data sources include customer feedback, demographics, behavior data, and purchase data.
How is call center analytics changing CX?
Call center analytics has fundamentally changed the role of call centers from a primary service offering to a strategic differentiator that can make powerful improvements in CX and financial performance.
Organizations that apply analytics can minimize average customer handle time by up to 40 percent. In addition, call center analytics can help improve self-service containment rates by 5 to 20%, cut staff costs up to $5 million, and boost the conversion rate on service-to-calls by nearly 50%, along with the added bonus of enhancing CX and employee engagement.
While call center analytics is only one of a broader set of enhancements, including operational changes like coaching and process simplification, it is a powerful tool for companies to implement.
Five types of call center analytics
Call center analytics is the way of the future in call center operations.
Businesses can develop capabilities around analyzing transactional insights and data, surveys, and financial records. But those who analyze the unstructured social media data, voice data, and big data from app behaviors or websites can create a complete customer profile for personalized customer interaction.
Thanks to call center analytics, businesses can better personalize conversations around CX, allowing them to build customer loyalty, advance customer engagement, and maximize conversions.
Call center analytics also helps enterprises, small or big, measure performance and see how a call center representative can improve performance.
Let’s look at five types of call center analytics
Speech Analytics: Call center is the primary data source for speech analytics. The data gathered from it and interactions with customers focus on identifying some of the common issues customers are experiencing through the tone and intonation of the customer’s voice. In this way, emotions are recognized automatically and tagged by the software.
Though this arena of analytics for contact centers is relatively new, speech analytics is facing a huge uptake as users are finding great success with its implementation. By understanding speech analytics, businesses can observe shortcomings in current scripts and update them with more effective ones. They also can develop new systems to enhance CX and obtain desired results.
Desktop Analytics: Desktop analytics are extremely beneficial in utilizing real-time call monitoring to record inefficiencies, provide valuable feedback opportunities for agent performance, and improve security. Call center desktop analytics can lift the optimization of both agent and customer experience. Simple and repetitive tasks can be monitored and assigned to automation, thereby freeing up the in-house staff for more important and cognitive tasks.
Predictive analytics: In most reputed call centers, data and analytics tools are becoming a common practice. However, several companies all not utilizing the full advantage of the technology. In point of fact, according to a report by McKinsey, only 37% of businesses feel that they are using advanced analytics in order to create value. Predictive analytics offers a vital tool for call centers to track and file customer satisfaction, call volume, wait time, and service level. In addition, predictive analytics can help customer care departments solve current issues with historical data, turning it into actionable insights.
For instance, predictive analytics can help with forecasting for staffing and allowing managers to decide how many staff are required on holidays based on call volume. Moreover, it can also track and record how the rollout of a new product affects call volume and demand. With the help of call center predictive analytics, a business can better plan for the future by looking at the previous results and the intervention measures used to resolve issues.
Self-Service Analytics: While few customers, especially from the old demographics, are at first resistant to self-service, they quickly realize its benefits. Many tech-savvy companies are optimizing specific duties with self-service analytics. An example of a self-service option is instead of a customer calling to update their address or to see the status of an order, they can do it online seamlessly.
Call center self-service analytics can minimize the chance for human error as well as the volume of incoming calls received by a call center. Using self-service mediums translates to a drop in overhead expenses, more engaged call center agents, and happy customers. Self-service analytics, including the usage of chatbots, needs little human interference once it is set up within an organization’s technological infrastructure.
Call Center Text-Analytics: This majorly involves focusing on written communication, including emails, web chats, documents, and social media comments. In the last few years, the usage of social media has exploded, making text analysis of social media comments to be exceptionally informative. With so many online brands, social media has become one of the primary forms of communication.
Text analytics tools help in monitoring and assigning specific values to words and phrases. Data mining functions can then identify relationships and patterns in the sets of data. This data can make conclusions about the texts being sent by an organization and its customers, highlighting any issues from the customer’s mindset.
The Bottom Line
A call center representative’s performance can be the most significant driver for CRM and a seamless customer journey, but it can also be the biggest obstacle. Thus, it’s crucial to utilize call center analytics to monitor agent performance in real-time.
Data-driven advanced performance analytics software takes many of the variables out of creating the best agent/customer relationship experience. By using a conjoint of the above types of analytics, companies can determine which languages and behaviors are helping contact call agents reach their target goals and key performance indicators (KPIs).