3 Ways to Optimize Call Center Operations with AICC Data Analytics

Struggling with Call Center Operations?

Common Challenges Faced by Call Center Managers

If you’re responsible for call center operations, you’ve likely encountered issues such as declining response rates, performance inconsistencies among agents, or sudden spikes in specific inquiry types.

• Surging Call Volume
  Unexpected increases in incoming calls lead to a shortage of agents and longer customer wait times.
• Managing Response Rates Across Partners
  When working with multiple outsourcing partners, discrepancies in their performance can create service quality imbalances.
• Difficulty in Conducting In-Depth Analysis
  While basic call dashboards provide an overview, identifying critical management points within vast call data remains a challenge.

Call center managers must analyze thousands of call data points daily, but quickly identifying the key factors that contribute to declining response rates is a challenging task.

These issues ultimately lead to increased call center operating costs and higher customer churn, which, in the long run, can weaken a company’s service competitiveness.

HEARTCOUNT Call Analyzer: Smarter Call Center Management Through AI

HEARTCOUNT’s Call Analyzer is an AICC (Artificial Intelligence Contact Center) solution that integrates a dashboard optimized for call statistics with AI-powered analysis.

It helps call centers not only detect real-time issues but also meet the growing need for in-depth operational insights and problem-solving.

In this article, we will introduce three key strategies for solving major call center management challenges and improving operational effectiveness.

I. Optimizing Response Rates During Sudden Spikes in Call Volume

Issue: Decrease in Response Rate Due to Increased Call Volume

When call volumes surge suddenly—whether due to promotions, service disruptions, or seasonal factors—agent shortages can lead to longer wait times and lower customer satisfaction.

In addition, call traffic concentrated on specific days or times, and varying call handling times by inquiry type, can further impact response rates.

Identifying High-Impact Call Types with AI Analysis → Improving Response for Targeted Issues

Using the dashboard, you can filter key metrics such as call duration and silence time to pinpoint the call types most affecting response rates.

A detailed review of call records revealed that inquiries related to opening savings accounts for minors stood out.

Instant Insights with AI Analysis

By simply clicking the AI Analysis button, you can ask questions like: “What are top 10 call summary types?" in natural language.

Using the Data Q&A feature, we found that call types related to minor account openings and branch visit questions were indeed among the most frequently received.

II. Managing Partner Performance: Quickly Identifying and Addressing Underperformance

Notably Low Response Rates Observed at Partner B Among Three Partners

When managing a call center that operates with multiple partner companies, differences in response rates between partners can naturally occur.

If a particular partner consistently shows lower response rates, it can negatively affect the overall quality of service provided to customers.

Such discrepancies are often driven by factors like the number of incoming calls handled by each partner, the number of available agents, or differences in agent skill levels.

Pinpointing Underperformance at the Agent Level → Accelerating Targeted Improvements

HEARTCOUNT’s Call Analyzer allows you to set up dedicated dashboards for subsidiaries and partner companies, making it easier to monitor performance separately.

Upon reviewing the response rates by partner and agent, it became clear that Partner B had a lower-than-average response rate, along with a higher volume of incoming calls.

With this insight, you can swiftly identify the root causes and take targeted actions to improve performance.

It’s important to also check whether partner performance issues are related to the performance of individual agents.

By using AI analysis on response rates by agent over a given period, you can accurately identify the root causes of performance issues.

Using Smart Search to Find Underperforming Agents

To check how agents with low response rates and low total call volume are distributed across partners, we utilized the Smart Search function, which allows you to search and filter records based on specific conditions.

In Smart Search visualizations, the closer a record is to the center, the more closely it matches the search conditions (in this case, low response rates and low call volume).

By clicking the View More button, you can switch to a table view and continue searching and comparing various metrics in a tabluar format.

III. Automatically Identifying Key Management Points with AI Analysis

The Challenge: Managing Key Factors Hidden in Growing Data Volumes

Every day, call centers accumulate thousands of call records and conversation logs.

For managers, analyzing this ever-growing volume of data and identifying key management points becomes a major burden. Given the limited time and constant flow of new data, it’s difficult to monitor real-time changes effectively.

When critical metrics suddenly shift or anomalies occur, failing to detect them early can lead to delayed responses, decreased customer satisfaction, and operational inefficiencies.

Quickly Pinpointing Core Causes of Change with AI → Reducing Management Burden and Speeding Up Decision-Making

Traditional dashboards, while useful for monitoring general operations, often make it difficult to spot sudden anomalies or uncover the true drivers of trends.

By leveraging AI analysis, however, it becomes much easier to automatically identify and focus on what matters most.

Detecting Anomalies and Trends with Signal Analysis

Within the AI Analysis menu, by selecting the “Trend” inquiry in the Dialogue feature, you can use the Signal function to automatically detect anomalies and significant changes hidden in the data.

This allows managers to quickly spot critical issues and respond proactively.

At the top of the Signal Analysis view, you can get an overview of general trends.

Below that, in the Key Insights section, you can explore:

• Work categories that have shown continuous growth over the past four periods,
• Categories that shifted from a declining to a rising trend,
• And those continuing a downward trend.

Focusing on Areas That Require Immediate Attention

To specifically focus on critical management points, you can filter to view only continuous trend changes.

By filtering for the "task category" variable, you can easily identify tasks or inquiry types that show consecutive increases or trend shifts allowing you to prioritize them for management action.

Text-to-SQL: Instantly Retrieve Insights from Massive Data with Simple Questions

To address specific management issues more efficiently, HEARTCOUNT’s Call Analyzer provides a Text-to-SQL (TTS) feature.

With this function, users can input natural language questions and instantly retrieve the desired information from massive raw datasets.

Simply type your question, click the Generate button to automatically create the SQL query, and then click Execute to immediately view the relevant data.

As customer contact points expand across various channels—including calls, chat, text messaging, and AI-assisted conversations—contact centers are generating more data than ever before.

Especially during the transition to AICC (Artificial Intelligence Contact Centers), accurate data analysis becomes absolutely essential.

Don’t waste precious time wondering:

“Why are our response rates dropping?”

“Where should we start to improve operational efficiency?”

With dashboards and AI analysis tools optimized for contact center environments, you can derive instant, actionable solutions and maximize operational effectiveness.

Optimize your call center operations today with HEARTCOUNT’s AI Analysis Solutions! 😊