Sales and call center managers operate in a pressure-cooker environment to improve team performance. With rep productivity directly tied to revenue goals, the burden falls on managers to maximize output, ensure call quality, and increase customer satisfaction.
But between coaching reps, developing strategic plans, and syncing with senior leadership, managers often struggle to manage their own time—leaving them with a limited window to gather the insights they need. While listening to every call for quality control is ideal, it’s not practical for busy managers—-which is why most are now turning to AI for support.
In our recent webinar, Aloware CEO Anoosh Roozrock describes how AI voice analytics give managers a quick, comprehensive overview of team performance. By pulling key information like customer sentiments, keywords, and conversation summaries, it helps managers quickly catch up on call center metrics and identify improvement opportunities.
Read on to learn the following:
- How AI voice analytics provide quick, specific insights on rep performance
- How AI voice analytics can help managers focus on pivotal customer interaction points
- Why understanding the emotional tone of calls is critical to improving customer service
Table of Contents
The challenge of efficient call quality control
Anoosh points out that managers, VPs, and founders often find themselves overwhelmed by the sheer quantity of calls they need to monitor. Traditional methods of quality control—like supervising live calls or reviewing full recordings and transcripts—simply aren’t scalable in high volume call centers.
Aloware’s AI voice analytics relieves managers of those demanding tasks. By efficiently analyzing each call and summarizing need-to-know information, AI helps managers log on to their dashboard and consume a digestible overview of call center performance. Whether they want to focus on aggregated metrics across teams or dive into individual rep interactions, the dashboard empowers them to do so in just a few clicks.
Aloware’s dashboard gives managers a one-stop view of business-wide, AI-driven stats like agent talk time ratio and contact talk time ratio, as well as positive vs. negative emotional sentiments on both sides. They can also see how their team’s averages for the latter compare to peers. There’s no cumbersome spreadsheets to build and export— just clear, visual data for quick review.
The dashboard also tracks preferred keywords and their occurrences across team calls, so managers can monitor their frequency of mention. It not only analyzes the number of occurrences by keyword but also measures how often reps talk about the important themes.
If managers prefer to focus their research on any individual rep, they can simply click the transcript icon beside each call logged under that agent’s name in the dashboard. Then they can view similar AI-powered summaries for the call at hand, along with viewing the full transcript.
How does it work?
AIoware’s AI voice analytics utilize advanced algorithms to transcribe conversations, much like other tools in the market. However, what sets Aloware’s solution apart is its ability to go beyond basic transcription. It analyzes calls for highlights, key words and phrases, and emotional tones on both the agent and customer sides, providing a bird’s eye view of your team’s service.
Some additional benefits of AI voice analytics include:
- Keyword identification: Managers can search for specific phrases or customer sentiments without wading through hours of audio. For instance, if a manager wants to know how often their reps say the word “success,” AI can pinpoint every instance of that mention across numerous calls.
- Improved performance evaluations: By quickly detecting sentiments and keywords, managers can evaluate rep performance more objectively. Instead of relying solely on personal observation, which may be biased, managers can access data-driven insights that reveal how reps are actually engaging with customers.
- Proactive issue resolution: AI voice analytics also help managers identify underlying customer issues before they escalate. For instance, if the support team frequently encounters frustrated customers, AI can highlight conversational trends and allow leadership to intervene early. Taking such a proactive approach not only enhances customer satisfaction but also prevents potential churn.
- Streamlined onboarding and training: Managers can identify common pitfalls in calls and create training materials that specifically address these roadblocks. Rather than guessing what might go wrong, managers have concrete data to guide training sessions—helping them to create more effective onboarding and skill development.
- Enriched coaching opportunities: AI can pinpoint the exact moments that customers express emotions, whether those are positive or negative. For example, if a rep struggles when a customer becomes angry, managers can highlight that particular segment of the call to provide constructive 1:1 feedback.
Business-saving applications
Anoosh emphasizes that when reviewing a 45-minute call, managers may only be interested in a specific moment—such as when a customer asks to cancel their service. Instead of spending nearly an hour listening to the full meeting, AI quickly identifies and summarizes the most relevant moments. This not only saves time but helps managers identify larger trends in why their business may be retaining or losing customers.
The future of call center management
AI is revolutionizing how call centers operate, making quality control more efficient and effective. With tools like Aloware’s AI voice analytics, managers can enhance their oversight without tedious and impractical call monitoring. The insights gained help managers to make informed decisions, coach their teams effectively, and ultimately improve customer satisfaction.
Ready to reduce your workload with AI voice analytics and instantly track team progress? Schedule a demo or start a free 14-day trial, no credit card required.
