Competition drives innovation, and for contact centers, analytics is in the forefront in the battle for competitive differentiation.

The landscape of call center analytics is continually evolving. New trends are emerging that help redefine how businesses interact with their customers. First among these trends are the integration of artificial intelligence (AI) and machine learning algorithms. That integration reveals predictive insights into customer behavior, and the adoption of sentiment analysis and speech analytics for customer/agent voice interactions.

As powerful as these transformational technologies are their ultimate utility and effectiveness depend on how well you manage them.

The Role of Data in Contact Center Analytics: What You Need to Know

In analytics the data and systems are symbiotic. Feeding bad or incomplete data into a powerful IT infrastructure produces compromised analytics.

What is ‘good’ data? Data that takes a 360-degree view of customers. It should include:

Call center data including all call recordings, average call times, surveys, and other touchpoints
CRM data
Social media feedback
Other customer interaction points

Data must be fresh and accurate. Old data needs to be archived. Remember that data has a cost, so businesses need to weed out dead weight data.

Choosing the Right Call Center Analytics Systems for Your Business

The term ‘IT Systems’ refers to the platform and application providing the analytics. Choosing the right call center analytics system that aligns with your different business objectives is crucial. Also, it is important to ensure that the IT system can handle your current and future data volume.

Common call center analytics and features include:

Predictive Analytics

can help anticipate customer needs and behaviors. Machine learning is good for identifying trends and patterns within vast datasets. By analyzing past interactions, machine learning algorithms can predict which customers are at risk of churn.

Audio Analytics

encompass speech and customer sentiment analysis of phone calls, voice mails and other audio communications involving call center agents. Sentiment analysis can evaluate and score the tone, mood, and emotions expressed during customer interactions.

Text Analytics

helps identify trends and insights in unstructured e-mails, chat transcripts, reviews and social media posts. Both audio and text analytics utilize natural language processing (NLP), a subset of AI.

Cross-channel analytics

streamlines the communication process by evaluating the most effective means of customer interaction. These channels can include calls, e-mails, texts, chats. Omnichannel analytics is helpful in resolving customer problems, it is especially useful in leveraging sales and other marketing activities via actionable insights.

Real-time access and monitoring

are a must for observing and improving agent performance in live scenarios. Real time monitoring can lead to quicker responses to emerging issues. It can also assist leaders with overseeing new hires in a less intrusive manner.

Ease of use

is especially important to those that are not technically savvy. Systems that do not require dedicated business analysts are highly desirable.

Flexibility and adaptability

are critical to a growing business or one that is entering a new market.

Some of the impacts in the cost area for contact centers include:

Identifying areas relating to Scheduling and staffing optimization
Using NPL and other technologies to replace human response to simple call center inquiries and to route calls to the appropriate call agent or agents
Analyzing calls, training, and customer service productivity

Areas of continuous improvement for call center managers include:

Reducing wait times
Improving call handling
Reducing average handle time (AHT)
Reducing call escalations
Reducing customer complaints and building customer loyalty rates
Increasing customer satisfaction (CSAT) and Net Promoter Score (NPS)
Increasing first call resolution
Reducing repeat calls

Some impacts on the business sales side include:

Improved understanding of customer preferences and customer feedback
Better understanding of types of calls
Holistic approach to the customer journey
More targeted sales
Personalized promotions

Training and Development: Building an Analytics-Savvy Workforce

Businesses want to see and take advantage of every benefit available to obtain the highest return on their investment. Accomplishing this in a rapidly changing technology environment means employees must be knowledgeable, motivated, and adaptable.

Knowledge is one of the bedrocks of employee performance, so skills training is essential. In an analytics-driven environment it is important that employees:

Understand the analytics
Know how to use the analytics
Apply the analytics
Learn from the analytics

Data Integration & Security:

Can the software integrate with multiple digital channels efficiently and securely?
Is customer data well-protected, with robust security protocols in place?
Does the system allow for collecting and routing data from various sources, including IVR and first call interactions?

Performance Monitoring & Optimization:

Are there tools to monitor and track agent productivity and center performance in real-time?
Does it offer capabilities to set performance thresholds and trigger alerts for specific events?
Can the software quickly analyze call resolution success and route conversations for optimal outcomes?

User Experience & Accessibility:

Is the interface user-friendly and easy for all team members to navigate?
Are there features that allow for a simplified view of complex data sets and analytics?
Does it support quick access to customer conversations and data points for on-the-fly decision making?

Scalability & Flexibility:

Can the system handle a high volume of data and interactions well without compromising speed or accuracy?
Is it flexible enough to increase capacity and include new data sets as your business grows?
Does it provide scalable solutions for monitoring a growing number of conversational topics and customer touchpoints?

Advanced Features & Analytics:

Does the software provide advanced analytics for tracking the customer journey across all channels quickly and effectively?
Are there modern tools for exploring data trends and extracting actionable insights?
How well does it support digital transformation initiatives, such as integrating with modern IVR systems and other automation technologies?

Cost Efficiency & ROI:

Does the system offer features that might enhance call routing and first call resolution to improve overall efficiency?
Will the analytics provided focus on reducing costs and becoming a catalyst for improved agent productivity?
Is there evidence that the software can lead to measurable improvements in center performance?

Before making a final decision, consider requesting a demo or trial.

A Guide to Call Center Analytics: Trends, Tools, and Impact

What Is Call Center Analytics?

The Role of Data in Contact Center Analytics: What You Need to Know