Your call center collects a lot of data. But are you leveraging that data to your full advantage? Let’s look at five categories of call center analytics that can help you generate lasting results.
What Is Call Center Analytics?
Call center analytics is the process of collecting and interpreting data from various sources within a call center environment to gain insights and make data-driven decisions. It includes tracking and measuring key performance indicators (KPIs) to better understand how your call center is performing.
You can use call center analytics to analyze communication channels like customer interactions, call recordings and chat logs to understand customer behavior and needs.
Why Is Call Center Analytics Important?
Call center analytics has two main goals: to improve your call center operations and the customer experience. Your customers are the lifeblood of your call center, so it’s essential to provide high-quality service at every touchpoint. But you can only accomplish this by ensuring your call center operates at an optimal level.
However, simply gathering your call center data isn’t enough to make an impact. You need to analyze the data to determine how you’re excelling and how you can make improvements. If you aren’t sure where to start, here are five key types to review.
Harnessing Call Center Analytics for Success: 5 Key Types
1. Predictive Analytics & Forecasting
Predictive analytics utilizes historical call center data to forecast future trends and outcomes so you can make proactive decisions and perform proper resource planning. It involves various techniques to predict call volumes, staffing requirements, customer behavior and churn rates:
- Data modeling is the process of creating a structured representation of your call center’s data and its relationships. It involves designing a model that shows how data elements are organized, stored and related within your database. The overall goal is to ensure the data is accurate and consistent for your reports.
- Regression analysis is a technique used to examine the relationship between one dependent variable and one or more independent variables. For your call center, a regression analysis can show you the impact of various factors on call center performance, customer satisfaction or other relevant outcomes.
- Machine learning algorithms are computational models that can automatically learn and improve from your call center data without being explicitly programmed. The algorithms can analyze patterns, relationships and data characteristics to make predictions or take actions in your call center.
2. Customer Satisfaction Analytics
Customer satisfaction is crucial for your call center because happy customers mean repeat purchases. By analyzing customer satisfaction, you can tailor your call center experience in a way that meets customer needs and leaves them feeling happy.
To do this, start by looking at your customer satisfaction KPIs to understand what your customers are experiencing when they contact your call center. Are they dealing with long hold times? Long handle times? Multiple calls to reach a resolution? If these KPIs are high, it can mean your customers aren’t receiving the help they need in a satisfactory manner. The good news is you can use this information to make improvements, such as hiring more agents or providing more training.
You can also conduct customer surveys to gather direct feedback. Surveys can provide valuable insights into how your customers feel about your call center, their preferences and their experiences. Review these answers to identify patterns and common pain points so you can make adequate improvements.
3. Voice Analytics
Voice analytics is the process of analyzing and extracting insights from recorded voice interactions between customers and call center agents. You can use natural language processing (NLP) and speech recognition technologies to accurately record and process the conversations.
By analyzing the audio from the conversation, you can review various parameters:
- Tone: The emotional or expressive quality of someone’s voice. It can provide insight into the sentiment or intention behind spoken words.
- Pitch: The perceived frequency or highness/lowness of a voice. It’s analyzed and measured to extract insights about the speaker and the emotional content of the conversation.
- Stress: The emphasis placed on certain words or syllables in speech that gives insight into the meaning or emotional content. By analyzing stress patterns, you can better understand the emotional state of the customer or agent, which is valuable for sentiment analysis.
- Rhythm: The pattern or timing of speech. It provides a look into speech patterns and speaking rates, which can help you determine if your agents are communicating effectively.
Call center managers can leverage voice analytics to help them monitor calls in real time and intervene if needed. For example, if a customer sounds stressed on a call, but the agent continues to talk at a fast pace, your speech recognition technology can pick up on this and alert the manager of the situation. This allows the manager to provide instant feedback to the agent to slow down their pace, which can lead to a better outcome.
4. Text Analytics
Text analytics derives insights from written text, which can come from chat logs, emails, social media interactions, customer surveys and other forms of written communication. Like voice analytics, text analytics can use NLP for sentiment analysis and identify whether the writer’s emotion is positive, negative or neutral.
And by detecting repetitive keywords, topics or phrases, call centers gain valuable insights into areas that demand attention and can take effective measures to resolve or mitigate potential issues. For example, if your customers are constantly complaining about high hold times, it’s likely time to hire more agents. This heightened responsiveness to customer needs, coupled with data-driven decision-making, empowers you to enhance overall customer satisfaction.
5. Omnichannel Analytics
Omnichannel analytics involves analyzing customer interactions across multiple channels, including phone calls, emails, chat messages and social media. By aggregating and integrating data across all channels, you gain a holistic view of customer behavior, preferences and experiences. That way, you can optimize the customer journey across different touchpoints and ensure a seamless and consistent experience.
Satisfactory customer experiences are essential to your call center’s success, as 80 percent of customers say the experiences provided by a company are equally important as its products and services. So if you want to make the experience smooth, consider customer journey mapping to identify critical touchpoints and pain points. That allows you to eliminate friction points, streamline processes and improve customer satisfaction.
Call center analytics empowers you to dive deeper into your data to make faster and smarter decisions that lead to positive outcomes. By looking at the five key types of call center analytics – predictive, customer satisfaction, voice, text and omnichannel – you can enhance how your call center operates and improve the customer experience.