While other platforms lack a unified interface to monitor agent performance across support channels, Level AI allows admins to build custom reports from both outside data sources and newly captured data in Level AI.
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Contact Center Analytics are the analysis of standard contact center metrics to identify trends, impacts, causes, and results. Contact centers regularly need to record, measure, and analyze different types of data.
The four most common contact center analytics consist of the following:
Contact center team analytics measure the performance of agents and quality assurance personnel. Internal contact center teams focus on different metrics that are relevant to the industry of their product or service.
For contact centers, common performance metrics for internal teams include:
Customer analytics is the analysis of multiple customer-related data sources to identify customer trends, interaction opportunities, and to serve as a source for modeling.
Customer analytics can be historical or predictive. Data sources include the voice of the customer, behavior data, demographics, and purchase data.
Speech analytics are the analysis of transcribed voice and ingested text engagements, along with metadata including CRM (customer relationship management) and notes to identify trends, the voice of the customer insights, performance drivers, and other insights.
Advanced speech analytics are derived from both NLP (natural language processing) and NLU (natural language understanding) technologies.
Predictive analytics is the use of big data, artificial intelligence, advanced algorithms, and intuitive machine learning techniques to create predictions about future results based on historical data.
In addition, predictive analytics allows you to calculate expected (CLTV) customer lifetime value, anticipate future consumer behavior, and identify customers likely to churn
Call Center Performance Management is a program, generally led by a contact center operations manager, dedicated to calculating and improving the performance of contact center agents.
Performance Management generally requires a number of tools like call recording, quality monitoring, coaching, and gamification.
Contact center managers can rely on KPIs (key performance indicators) to track metrics such as net promoter score (NPS), customer satisfaction score (CSAT), and average handle time (AHT) to both measure and improve contact center performance.
However, advanced software is essential to improving the performance of a modern contact center at scale.
Here are 6 of the most effective ways to improve contact center performance:
The best way to assess your contact center’s performance is through a combination of quantitative and qualitative KPIs. Modern contact center technology, such as Level AI, is well equipped to give you all the data and conversational intelligence you need.
There are common contact center performance metrics, such as average handle time, customer wait time, schedule adherence and more. These kinds of call center metrics can help you to make informed decisions about workforce management and service level.
However, measuring customer satisfaction and the overall quality of the customer experience is more difficult, as CSAT and NPS scores are limited in scope.
A high CSAT score might come from someone who felt that their customer interaction was hurried and would have preferred more friendly service. A low NPS score could come from a caller who experienced wonderful agent engagement but didn’t get their issue resolved.
Fortunately, call center AI technologies such as real-time speech analytics and NLU (natural language understanding) that understand customer intent are available and should be taken advantage of. CCaaS platforms make it far easier to collect data on subjective factors such as a caller’s emotional state.
How do you incorporate AI and machine learning into a call center? AI now plays a major role in the most advanced QA teams. The key areas where AI technology in a contact center include quality assurance, agent monitoring and training, and analytics.
AI in customer service refers mostly to three core technologies:
The most advanced contact center software platforms for QA and agent training are using NLU (natural language understanding), as opposed to NLP (natural language processing) alone.
For example, Level AI is using NLU to uncover customer intent and to provide support agents with the information they need to communicate to customers in real-time.
In fact, in 2022, the best AI-powered contact center software outperforms legacy systems, which rely on keyword matching (text mining), with an average 2X (+100%) lift in accuracy.
The most advanced AI and NLU platforms for contact center QA teams will quickly learn how to best handle nearly any scenario your customer support agents encounter in real-time.
A Quality Assurance Scorecard (QA scorecard) – also commonly referred to as a “quality monitoring scorecard” or “call center quality monitoring scorecard” – is a rubric against which a QA manager, team lead, or analyst grades a contact center agent’s interactions with a customer.
QA scorecards give QA teams the integrated framework, accountability, and efficiency you need to track, train, and develop your key CX (customer experience) agents.
The best QA scorecards focus on your agents’ KPIs (key performance indicators) and organize them into a customized checklist of business values.
Regularly proposed as questions, a common QA scorecard performance indicator would be, “How well did the agent categorize the customer’s case?” This format allows you to objectively assess your agents’ service performance, identify strengths and weaknesses, and alter any problematic behaviors.
A best-in-class QA scorecard is holistic, actionable, and updated frequently to meet the right QA metrics for your business.