Call Analytics
Apr 17, 2024
How call analytics works (and 5 top best practices)
What's driving the high call volume in your contact center? Artificial intelligence (AI) may be the key to identifying the root causes without expending excessive time and resources.
AI-based call analytics is a powerful tool to understand reasons for high demand and identify other factors contributing to operational slowdowns. In this post, we share call analytics best practices and AI-driven solutions to help you find valuable insights about your customers' experience.
Table of Contents
What is call analytics?
Call analytics is the practice of gathering and analyzing customer information from contact center phone conversations. It compiles data from call recordings and phone logs to identify key trends in customer behavior through conversations with your company.
This process is typically executed by call analytics software, which employs a range of technologies such as:
Speech recognition: Transcribes spoken words into text, allowing call content analysis
Sentiment analysis: Evaluates the emotional tone and sentiment captured by the calls
Keyword spotting: Recognizes specific words or phrases to map common issues
Artificial intelligence: Automates and scales call data analysis, allowing actionable insights and more precise forecast
Conversation intelligence software like Echo AI incorporates call analytics powered by AI into its capabilities. AI processes a massive volume of inbound calls in real time and turns collected data into actionable knowledge. This way, your business can track contact center performance and enhance customer and employee experience.
Call tracking vs call analytics: Uncovering the differences
Call tracking and call analytics are both data-driven strategies that extract helpful information from phone calls. Here are the differences:
Steps to call analysis
Call analytics solutions focus on three essential data-driven steps:
Collection: At this stage, the call analytics software collects data from call recordings and transcripts. It includes information such as call duration, handling times, keywords, and sentiment a customer expresses.
Analysis: The software then processes and analyzes a substantial amount of data to identify customer issues and emerging trends. It is the stage where the system detects patterns such as product preferences and even shifts in customer sentiment.
Reporting: The call analytics tool creates comprehensive dashboards and reports to present its findings. This step provides an overview of key metrics, such as CSAT scores' evolution throughout the year. It also reports potential causes of recurrent problems, such as low customer engagement and FCR below average.
While traditional methods only gather a limited variety of metrics, advanced solutions based on AI expand the possibilities by processing verbal and non-verbal elements of a call. This integrated approach allows you to access deeper insights, such as how many calls identified churn risks and why customers are leaving.
Five call analytics best practices to drive results
Follow these five call analytics best practices to maximize the value of your customer interactions:
1. Collect data from various sources
If your business offers multiple customer touchpoints, these channels are valuable sources of insights. With AI-based software, you collect and integrate data from call recordings, transcripts, CRM systems, and customer feedback channels in one place. The more diverse your data sources, the deeper your understanding of customer interactions.
2. Identify patterns in customer behavior
Is your contact center facing a sudden decline in conversion rates or other key performance indicators (KPIs)? Voice analytics and speech analytics software recognize patterns in customer behavior to discover why they aren't taking action. AI-centric software like Echo AI incorporates these functionalities into a single platform, instantly notifying new trends. These capabilities enable you to address customer pain points more swiftly and launch targeted campaigns to increase conversion.
3. Analyze your highest scores on key metrics
Here is where you go beyond diagnosing critical areas to learn from the greatest results in CSAT rates, net promoter scores (NPS), and other key indicators. By analyzing high-performing calls, you value your team's efforts and replicate their strategies to enhance the overall CX. AI streamlines the quality assurance process, offering automated QA scorecards after each call.
4.Encourage your team to leverage data
Call center analytics best practices also improve your agents' efficiency through continuous learning. Encourage your team to monitor call performance by consulting their QA scorecards. In AI systems, call center agents receive real-time coaching, empowering them to develop call-handling skills, knowledge about products, and time management abilities.
5.Contact customers before they call you
Proactive outreach is a strategy to reduce call volume – a global survey by Deloitte reports that 36% of contact centers leverage this practice to prevent new queries. AI predictive analytics is a tool to help you anticipate customer needs based on data. This practice includes flagging calls with unresolved issues, sending post-purchase surveys, and creating segmented campaigns based on topics of interest.
