As we have promised, here is the second part of our topic about call center data collection and call center data analytics into contact center operations. As we have already mentioned, the main thing about customer interaction analytics you have to know to receive actionable insights is the importance of the use of AI (Artificial Intelligence) and advanced contact center technology in order to receive real-time analytics to make data-driven decisions.
Overall contact center performance depends on your ability to handle phone calls in accordance with service level, reach your Key Performance Indicators (KPIs), analyze agent performance, utilize self-service channels, analyze customer feedback, and thus you will improve operational efficiency, resolve customer issues faster and reduce response times.
Another vital thing about insights into customer behavior is your ability to understand customer preferences through the use of cross-channel analytics, contact center analytics dashboards, staffing levels, volumes of incoming calls and IVR (Interactive Voice Response) data to make informed decisions to reach business success.
Keep track of your vital call center metrics and KPIs
The Average Handle Time (AHA)
The Average Handle Time is one of the key call center data analytics and it shows the average duration of interaction, and can be described as sum of average talk time, average on-hold time and wrap-up time – duration of all activities that are related to handling customer calls. The main issue is that some call center managers confused this metric with Average Resolution Time, which measures the overall time spent on resolving issue – and that’s obvious that sometimes agent alone can’t do it without involving other specialists or departments. Thus, average handling time is about how much time is spent by agents to complete all actions that are under their responsibility.
Call Abandonment Rate
Call Abandonment Rate is one of the key call center analytics and it measures the percentage of inbound calls abandoned by customers before they’d have been connected to the customer service agents. Thus, measuring and analyzing this rate is critical for every call center – abandonment rate points out to the problems with average speed of answer, average waiting times, and so on, and always leads to customer churn – the biggest problem for any business. Reducing call abandonment rate is synonymous with improving customer experience.
Call Answer Rate
Call Answer Rate (Call Pick-up Rate or Answer Success Rate, Call Connection rate – there are many names for it ) is one of the key call center analytics for outbound call centers, as it measures the percentage of calls that were answered correctly by real potential customers. For manual outbound calls, it rarely exceeds 35%, which isn’t very effective, yet with the use of specific automated calling software, it can be improved up to 75% – that’s why choosing modern software solutions is always about better business performance.
Leverage speech call center analytics, text call center analytics, and sentiment analysis
Standard call center data analytics are about taking poor numbers out of context and comparing them in order to find answers to your questions. Nevertheless, implementing AI-driven software can open new opportunities, especially in the field of analytics of customer experience.
There are numerous of such solutions available when it comes to speech recognition and call centeranalytics – these solutions track keywords in customer messages to identify customer sentiment and emotions and then provide you with valuable insights.
Some advanced solutions can even analyze customer speech – speech analytics allows you to find out whether customers are satisfied with the service or not during conversations, by tracking keywords, intonation, tone of voice, and so on.
Customer Sentiment Scores
Customer sentiment analysis is a new trend in contact center software industry as it helps to understand customer experience deeper and without using indirect metrics which require customer surveys to be measured – and this always leads to issues, such as low customer engagement rate which makes it impossible to measure average customer satisfaction. Customer sentime scores can be measured specifically for each agent in order to find out who of them provides better service, and provide additional training to those who struggle with providing positive customer experiences.
Use customer surveys to collect customer experience data
As we have already mentioned, it is required to organize customer surveys in order to measure some vital customer experience metrics, such as customer satisfaction levels, customer effort score, net promoter score, and some of the less popular metrics. Nevertheless, it is difficult to survey customers manually, modern call center software always provides options for customer surveying automation.
Customer Satisfaction Score (CSAT)
Customer Satisfaction Score (CSAT) evaluates the satisfaction level of customers following interactions with your customer service representatives. An example CSAT survey question could be: “On a scale from 1 to 10, how satisfied are you with the support received today?” These surveys can be sent post-complaint resolution or after significant touchpoints in the customer journey, like a purchase. A CSAT score ranging between 70 and 90% is typically considered good; lower scores may indicate a need to improve Average Handling Time (AHT) and First Call Resolution (FCR) rates, which directly impact customer satisfaction.
Customer Effort Score (CES)
Customer Effort Score (CES) measures the ease with which customers interact with your business. A basic CES survey question could be: “On a scale from 1 to 5, how easy was it to handle your issues today?” Open-ended questions can provide detailed feedback (e.g., “What could we have done to improve your score by one point?”). CES, alongside NPS, helps identify pain points in the customer journey, facilitating efforts to reduce customer effort and enhance overall customer experience.
Net Promoter Score (NPS)
Net Promoter Score (NPS) gauges customer loyalty based on the likelihood of recommending your product, service, or company. The survey question typically asks: “On a scale from 0 to 10, how likely are you to recommend us to a friend or colleague?” NPS surveys can also assess employee sentiment through Employee Net Promoter Score (eNPS), reflecting agent loyalty and satisfaction with the workplace.
Predictive analytics, powered by AI and machine learning, offers insights into future trends and customer behavior by analyzing historical data. For instance, analyzing call volume trends aids in staffing adjustments to handle peak traffic efficiently, such as during holidays or specific times of the day.
Utilizing call center analytics software is essential for efficiently collecting and analyzing data. Call center metrics analytics and reporting solutions track key metrics, generate reports, and predict trends, automating call recording, transcription, and analysis. Integrated within call center software systems, these tools streamline data processing without the need for multiple platforms.