Contact center analytics to track and monitor.
What are Contact Center Analytics?
Contact center analytics, sometimes referred to as call center analytics, help teams track important data about people calling in to centers, such as call time length, customer satisfaction, and how many times customers called before resolving an issue. They also help organizations analyze online chat conversations, including important keywords, conversation length, and more.
Each day agents deal with dozens of customers. Yet in the past they have often had limited access to the information. The rise of multichannel contact centers means that agents are now using SMS, in-app chat, and WhatsApp to communicate with customers. To address these challenges, contact center analytics tools help companies break down data silos to ensure that agents have access to a single source of truth.
Contact center analytics tools help companies that work across multiple digital channels streamline and optimize their operations. They help teams track customer journeys, analyze call lifecycles, uncover agent availability, and determine staffing needs. With contact center analytics tools, companies can boost productivity by helping agents anticipate needs and solve issues more quickly, as well as support data-driven decision making on staffing and call routing decisions.
API technology enables contact center analytics tools to leverage real-time and historical data from multiple channels and data sources. Anomaly detection tools, for instance, can help companies understand sudden increases in consumer spending, especially during the holiday season.
With the help of contact center analytics, companies can better address:
- Inbound calls/messages coming in from customers
- Outbound calls/messages to customers for telemarketing, market research, debt collection, and solicitation
What are the advantages of Contact Center Analytics?
Investing in contact center analytics tools reduce overall costs, increase revenues, improve customer satisfaction scores, and drive employee engagement. Companies accomplish this by reducing call handle time, increasing self-service containment rates, and boosting conversion rates on service-to-sales calls.
Types of Contact Center Analytics.
Contact center analytics can help companies step into the customers’ shoes, understand the employee perspective, as well as understand how the contact centers impact on the business’s bottom line.
Speech analytics help agents to identify, understand, and analyze what the customers need by analyzing their conversation. It can help pinpoint problems with procedures, operations, and systems.
Text analytics are associated with email, chatbot, social media, and other written interactions. contact center text analytics make it possible for organizations to review and monitor messages sent to customers.
Desktop analytics track employee activity and are helpful for understanding how agents are performing. They help to improve contact center security, enhance real-time monitoring, identify issues before they become a problem, eliminate repetitive tasks, reduce call handle time, and more. They can help surface agent and customer friction, as well as provide opportunities for managers to coach more junior agents.
Cross channel analytics help contact centers optimize across tablet, desktop, mobile, kiosks, and other channels. They make it possible to gear service towards a specific device or use case.
Self-service interactions analytics help companies refine the customer experience for self-service channels and applications. They help contact centers develop frictionless user experiences that empower customers to accomplish tasks onlines, which reduces errors, incoming call volume, and overall costs.
Predictive contact center analytics take into account past performances, including call volume, service level, handle time, and customer satisfaction scores. Using this information, contact centers can perform prediction analysis to understand staffing needs, or how a new product launch might affect call volumes on weeknights vs. weekends.
Evaluating the customer contact center experience
Customer effort quantifies how easy or difficult it is to complete a task with the contact center. In other words, customer effort measures how easy it is to resolve a problem, or how long customers must wait before connecting with an agent.
First contact resolution is the percentage of inquiries agents resolve on the first contact. A higher first contact resolution rate means that contact centers are eliminating multiple calls.
Automation rate is the percentage of tasks that can be completed without the help of an agent.
Contact centers keep track of how many times an agent transfers a call or puts a customer on hold during one call. They also track how many callbacks it takes to resolve one issue. These measurements can help teams keep track of the number of escalations per channel, which is the total number of contacts needed to complete a task. Call time, which includes hold time, is how long it takes for an agent to address an issue.
Queue time is how long it takes for an agent to answer your question, while ring time is how long the phone rings before an agent picks up the phone. When you queue time and ring time together, you get waiting time.
Complaints per channel measure how many customers express frustration with service on one channel. Holds, transfers, and callbacks can lead to more complaints per channel.
Similar to shopping cart abandonment rate, contact center abandonment rate is the percentage of people who leave the phone call before completing a task or addressing an issue.
Customer satisfaction score (CSAT) is the percentage of customers that are satisfied with their service, while net promoter score measures the likelihood of a customer recommending a customer, product, or service to others.
Customer engagement score takes into account contact frequency and contact type, including customer inquiries, social media & email activities, channel utilization, and more.
How Contact Center Analytics help prioritize the customer.
Contact center analytics tools are becoming an industry standard. They have a number of important use cases, such as:
- Reduce average handle time by removing unnecessary words, redesigning questions that surface pain points more quickly, and eliminating unnecessary steps in workflows
- Reduce call volume by enhancing the customer journey. People shouldn’t be calling for minor issues or calling back to try a new agent after the first one was unable to complete a task. Simple fixes on a websites navigation, for instance, can drastically reduce contact center volume
- Create hassle-free return or cancellation policies, backed by customer data
- Predict impact that outages will have on service levels
- Analyze calls that were successful in the past and iterate in similar future scenarios
- Customize the pitch for outgoing calls based on a customer’s profile
What problems do companies face with integrating Contact Center Analytics?
Unfortunately, many companies are weighed down by legacy infrastructure, outdated organizational structures, poor data visibility, and silos. Even when companies have data, they often do not know how to aggregate and analyze that data to surface insights related to important KPIs, such as revenue and conversion rates. Many companies fail to turn data insights into actionable steps.
Some approaches, such as Voice-of-Customer tools, can help teams track metrics such as first-call resolution (FRC) and customer satisfaction. Yet these approaches don’t take real-time customer signals into account and thus make it difficult to redesign contact center workflows that aren’t working.
To fully benefit from contact center analytics, companies must develop data analytics strategy across an entire enterprise, which involves investing in platforms and ecosystem partners, such as Quantum Metric. Such platforms and partners make it easier to build out a roadmap and cultivate a culture of objective decision making.