Beyond gathering data, effectively analyzing data to acquire actionable insights is crucial. Per a Talkdesk report, 81% of CX professionals cite contact center analytics as critical to business success. Organizations that adopt an insights-driven approach can optimize performance far better than teams that play it by ear. 

This article covers data analysis, traits of data-driven teams, and the relationship between analytics and high-performing teams. 

What does it mean to be data-driven? 

Being data-driven refers to the evaluation and interpretation of information — rooted in numbers and facts — in alignment with business objectives to make strategic decisions. Companies that adopt a data-driven approach organize their data effectively, make decisions with certainty, optimize workflows, and improve service delivery. 

What is call center analytics?

Call center analytics collects and evaluates performance data to uncover insights about agent and organizational service delivery. Giving everyone access to realtime employee performance data promotes transparency and trust. With multiple metrics available for contact centers, leaders can boost agent performance and customer experience with contact center data analysis. 

Consider some of these contact center analytics for optimizing agent performance:

• Average handling time (AHT). Understanding the average duration of typical transactions, The measure is usually from the customer beginning the interaction and covers hold time, talk time, and other related tasks during the conversation. 

• First contact resolution (FCR). First contact resolution is when customer service agents properly address a customer's needs across customer-facing touchpoints (phone, text, chat, or email). By doing this, there is no need for the customer to follow up. FCR is an important indicator of your contact center team’s success rate and is important for customer retention, improving agent morale, and decreasing labor overhead. 

• Customer Satisfaction (CSAT). The primary objective for any business should be creating happy customers. CSAT is a measurement used to quantify the degree to which customers are satisfied with a service, product, or experience. This information is typically gathered in post-service or email surveys. 

• Quality Assurance analytics. Analyzing customer interactions enables managers to identify gaps in performance and tailor feedback to each agent, which improves individual performance. 

• Digital channel analytics. Managers can evaluate agent performance by reviewing customer interactions across digital channels such as email, chat, social platforms, and messaging. These results effectively measure how call center agents can evolve with new media. 

Be sure to compare agent performance to their peer group. Employees who are just starting are usually in a ramp period and may take longer to resolve customer issues. Experienced employees may take on more complex issues. It’s important to have context while setting goals and looking at performance metrics. 

Key features of a call center analytics program

Data analytics is the process of examining or analyzing raw data to draw out meaningful and actionable conclusions. A study by the Data Literacy Project reveals that 93% of business decision-makers believe that data literacy is relevant to their industry. Yet, only 24% of the global workforce is confident in interpreting data.

Managers should not have to be data scientists to interpret data from disparate sources. There is a risk of spending too much time analyzing data, misinterpreting, or not sharing the information in a timely basis. When you empower data-driven contact centers with realtime insights at all levels of the org, you can innovate quickly and offer personalized customer experiences based on insights from historical results. Data enables contact centers to identify process gaps, which encourages swift problem-solving.  

Some other traits of contact centers that leverage analytics include:

• Reliable and standardized information. Numbers are easier to quantify than soft skills like team spirit. By tracking metrics and historical data, organizations can make decisions based on standardized and verifiable information.

• Metrics-driven culture. Teams identify and track metrics that inform them of progress in a given area, such as resolving customer inquiries quickly. Data-driven teams have a consistent reporting process and evolve data usage as analytic tools become more sophisticated.

• Coaching attitude and winning spirit. In a study by Frost and Sullivan, 62% of agents report that more skill-based training will optimize performance. Data-driven teams are empowered to know how they are doing and where they need help. Managers with the insights and time to coach can improve agent job satisfaction and happiness, and reduce employee churn, which costs on average $14,113 to replace. 

• Tangible and quantifiable results. Performance intelligence data leverages historical results to create custom goals for each agent. This approach ensures that representatives can work towards realistic, tangible, and quantifiable goals. 

• Predictive analysis. Predictive analysis is essential for call center operations as it enables proactive decision-making based on data-driven insights. By analyzing historical data and trends, call centers can forecast call volumes, staffing needs, and customer behavior, leading to optimized resource allocation and reduced wait times. Additionally, predictive analysis can identify potential issues before they escalate, improving customer satisfaction and operational efficiency.

  
  

How call center analytics software steers data-driven teams

With Contact center data analytics, agents can optimize their performance and refocus their efforts on activities that directly impact the company’s bottom line and increase success rates. 

Here are some other ways customer support analytics software enhances data-driven teams:

• Performance visualization. Leaders, managers, and agents all have an overview of team performance across all systems in realtime. They instantly know where they are falling behind on goals, and where help or coaching might be needed.

• Individual and team health scores. Echo AI evaluates data across your key metrics and provides a single score that instantly tells you how your team and employees are performing and where you should focus your efforts today.

• Performance benchmarks. With benchmark standards, teams understand service delivery goals. With Echo AI, you can use AI to set auto-goals on your KPIs and save your managers a ton of time.  

• Tailored feedback. Analytics provide actionable insights that help managers to deliver focused training and agents take immediate action. With Echo AI coaching messages can be attached to data insights making communication quick, clear, and easy to understand. 

• Team agility. With a data-drive approach, managers can quickly identify gaps in processes and provide coaching to improve agent experience and reduce customer issues.  

Echo AI helps teams leverage data with actionable insights

Echo AI enables your team to unlock its full potential by consolidating data from all your contact center systems and providing actionable insights to meet business goals. You can configure organizational charts to roll up or drill down performance from department to employee. 

Echo AI helps leaders to understand how each team member performs against your organization’s top metrics and leverage AI to automatically create custom goals based on historical performance. 

Looking to make the most of your data insights and supercharge your team in minutes? Schedule a Echo AI demo today.  

Frequently Asked Questions

What metrics are used in a call center?

Contact center metrics vary depending on business objectives and can span customer experience, agent productivity, operations, and call initiation. Some metrics contact centers track is first call resolution (FCR), average handling time (AHT), customer satisfaction (CSAT), cost per call, and first response time. 

How do you measure customer support productivity in a call center?

Managers can measure contact center efficiency and agent productivity by tracking metrics such as abandonment rate, the average time in queue, service level, the average speed of answer, customer satisfaction (CSAT), and first call resolution (FCR). Metrics help team leaders understand customer behavior, reflecting call center strategies' effectiveness. 

How to set performance goals for contact centers?

Performance goals provide agents with targets and boost efficiency and productivity. To set performance goals, managers should consider each metric and create targets that match overall business objectives. Echo AI offers organizations AI to automatically set goals based on historical performance data, which ensures that targets are realistic and consistently contributing to team growth.