Your sales team is working hard, managers report numerous calls from prospects daily, yet month after month you’re only hitting 70-80% of your sales targets? Many sales leaders rely on gut feeling instead of hard data. However, there are several key metrics that can give you a complete and objective picture of your team’s performance. And no, it’s not just “number of calls per day” ― it’s about understanding that out of 50 opportunities, only a fraction actually converts into business.
- 1. Call Response Rate: Why "Picked Up the Phone" Doesn't Equal "Processed the Lead"
- 2. Real-time Activity Monitoring: Is Your Team Actually Working?
- 3. Call-to-Deal Conversion Rate: When Quantity Loses to Quality
- 4. AI-Powered Call Quality Score: Sometimes Artificial Intelligence Understands Better Than Humans
- 5. Individual Performance Deep Dive: Personalized Approach to Developing Each Manager
- Technical Implementation: From Simple to Complex
- Conclusion
1. Call Response Rate: Why “Picked Up the Phone” Doesn’t Equal “Processed the Lead”
Everyone knows that a missed call is a gift to your competitor, and every leader strives to reduce the number of missed calls to zero. But even if a manager reports 50 answered or outbound calls per day, that doesn’t necessarily mean countless new deals closed. Sometimes the problem isn’t the quantity of inquiries, but how managers handle each one.
The Call Response Rate metric shows the percentage of inquiries that actually converted into meaningful follow-up work with the lead. To calculate it, take the number of calls that resulted in a created lead or assigned task, and divide by the total number of inbound calls. Multiply by 100% ― and you’ll get the real picture of your team’s performance.
Here’s the formula:
Conversion Rate = (Number of closed deals / Number of target calls) × 100%
Based on our observations, the difference between “answered the call” and “processed the lead” can be significant ― sometimes a manager picks up the phone or makes a call but doesn’t create a follow-up task or record the result in the CRM. Data gets lost, and sales opportunities disappear.
Modern IP telephony integrated with CRM makes this calculation automatic. Moreover, it’s precisely through integration that actions like entering data into the system, creating contacts, assigning tasks, and setting follow-up reminders happen automatically. If a call was missed, the system will create a “Call back” task. You don’t have to rely on managers’ memory and punctuality ― the configured tool handles the routine, while managers can focus on actual selling.
2. Real-time Activity Monitoring: Is Your Team Actually Working?
Remote work has completely changed familiar processes ― a manager can’t simply walk over to a salesperson’s desk and see what they’re doing. But the need to monitor work remains ― to understand team workload and track task completion progress.
Real-time Activity Monitoring is a system for tracking current manager activity in work tools. The report shows who’s currently online and who’s in “Do Not Disturb” status, who’s talking to a client, who’s working in CRM, who can take the next call. The system records real work activity ― down to the duration of each phone conversation.
JayJay, an educational platform from Indonesia, is an example of effective use of this metric. Managers work from different corners of the country, and management needed a clear understanding of who’s available to handle calls right now. Data from the telephony system feeds into their own manager efficiency reports. Thanks to real-time data, management can instantly redistribute workload between employees.
Another Ringostat client, fastener retailer Dinmark, uses the “Real-time Employees” report to balance team workload. This allows management to understand the real work picture and respond quickly to peak loads.
What does this metric actually show? First, how much time a manager actually spends in CRM and telephony. Second, the distribution between active calls and time managers spend on other tasks. Third, availability statuses that help understand whether an employee can take the next call.
The metric can also reveal previously unnoticed team work patterns. For example, you might notice that peak team load occurs from 10-12 AM, when everyone tries to “ramp up,” creating call queues.
Also read ― Real-Time Call Report: 5 Ways to Use for Real-Time Team Control.
3. Call-to-Deal Conversion Rate: When Quantity Loses to Quality
One manager makes 100 calls and closes 2 deals. Another makes 50 calls and closes 5 deals. Who performs better? The answer is obvious. But many leaders continue focusing exclusively on work quantity, turning a blind eye to real effectiveness ― deals.
Call-to-Deal Conversion Rate formula:
Conversion Rate = (Number of closed deals / Number of target calls) × 100%
MONLIS, a network of beauty salons in Munich, radically changed their approach to evaluating team performance. Instead of counting calls, they focused on conversion from call to salon appointment. The result ― conversion grew from 30-40% to 55-60%. The secret is simple: you need to analyze not just the quantity of contacts, but quality, as well as sources of inquiries. Thanks to accurate data about call sources provided by call tracking, MONLIS was able to optimize their advertising budget and focus on the most effective channels.
Call tracking with AI also helps MONLIS analyze call-to-deal conversion. For example, a campaign to their existing client base unexpectedly showed high conversion to calls ― this seemed like success. But speech analytics helped understand that users simply misunderstood the advertising message and were asking for discounts on completely different procedures than those offered in the campaign.
So high call quantity indicators don’t always indicate lead quality.
Keramis, an online store for tiles and plumbing, faced exactly this problem. Managers were taking over 300 calls per week, but conversion to sales remained disappointing. Controlling such a quantity of conversations was impossible, so management didn’t understand for a long time why such an active team wasn’t showing corresponding results.
Managers could skip sales stages, incorrectly handle objections, or not bring conversations to logical completion.
This is why it’s so important to analyze not just the quantity of answered calls, but other indicators as well. Particularly interesting can be the relationship between conversion and call duration. For example, calls shorter than 2 minutes rarely lead to deals ― the manager simply doesn’t have enough time for a full presentation. But long conversations don’t necessarily indicate manager professionalism either.
4. AI-Powered Call Quality Score: Sometimes Artificial Intelligence Understands Better Than Humans
Keramis’s problem isn’t unique ― controlling the quality of every conversation is simply impossible. Even if you listen to 10% of calls, it takes hours daily and doesn’t guarantee identifying all problems.
But artificial intelligence helps change the approach and get desired data by instantly analyzing 100% of conversations and revealing the real reasons for low conversion.
AI analysis helped Keramis understand that the problem wasn’t call quantity, but systematic manager mistakes. Artificial intelligence identified patterns impossible to notice with selective, spot checking. The result ― gradual increase in conversion from calls to sales.
AI-Powered Call Quality Score is a comprehensive evaluation of each conversation across dozens of criteria simultaneously.
The formula looks simple, but complex algorithm work stands behind it:
Quality Score = (Sum of criteria points / Maximum possible points) × 100%
Artificial intelligence trained on internal company rules and scripts will automatically analyze how a manager follows conversation scripts, whether they correctly handle client objections, and what mood prevails during the dialogue on both sides. The system also tracks the duration of each conversation stage, records mistakes or use of prohibited phrases. And assigns scores ― without human involvement.
Criteria can have different weights ― for example, skipping the client needs identification stage “costs” more points than a forgotten greeting.
For example, Hillel IT School developed a graduated system where greeting and name-calling gives 0.5 points, objection handling ― 1 point, and sending an invoice ― a full 8 points. After all, not all conversation stages equally affect the result. Each conversation stage brings a certain number of points, which then add up to the dialogue’s final score. Analysis of several such dialogues creates a complete profile of the manager’s work.
GoITeens, an international IT academy for children, also created a thoughtful control system where artificial intelligence evaluates each lesson across nearly 20 criteria. AI checks whether the teacher actualized knowledge at the beginning, made an interesting announcement of the next lesson, followed the sequence of learning elements.
The system analyzes the employee’s attitude toward the conversation, communication tone, individual approach to the client, and explanation quality. If a teacher addresses the student by name during the lesson ― that’s a plus. If they use jargon or take too long to find words ― minus points.
“Betterton” Hearing Center configured AI to analyze across 15+ criteria with automatic highlighting of problematic dialogues. The system reduced control time by three times, but most importantly ― it enabled identifying patterns that humans might miss. For example, that certain phrases in service presentations correlate with higher conversion. Or that conversation quality depends on time of day and day of the week. This is a level of analysis unavailable with manual control.
5. Individual Performance Deep Dive: Personalized Approach to Developing Each Manager
General team training can be ineffective since each manager has their own strengths and weaknesses. Personal analytics allows creating individual development plans, focusing on specific skills.
The Individual Performance Deep Dive metric is a comprehensive system for evaluating each manager across several parameters simultaneously. Unlike general KPIs like “number of calls,” this metric combines several levels. For example, this is how this metric is tracked in Ringostat’s sales team:
- operational efficiency: how quickly leads are processed, whether procedures are followed;
- quality indicators: how conversations are conducted, whether objections are handled correctly.
The first level ― automated dashboards that help track “operations” in real time. For example, whether a lead is stuck in a certain status longer than the allowed time, whether the manager performs all mandatory contacts before losing a deal ― five calls at different times and an email after the first and last unsuccessful attempts. The system automatically generates notifications about procedure violations.
The second level ― qualitative analysis of sales managers’ work. Twice a month, the leader checks whether CRM records match actual conversations, correct objection handling, and adherence to time standards. Based on this, each manager receives a score. If it’s above 90%, the employee works correctly and doesn’t need additional attention.
Keramis uses a similar approach ― Ringostat’s AI analyzes who among employees follows all sales stages, who knows how to handle objections, and who needs additional training in product presentation.
The most interesting discovery ― there’s a direct correlation between following operational standards and sales results. Managers who regularly perform all procedures usually also exceed their targets. This confirms that Individual Performance Deep Dive isn’t just control for control’s sake, but a tool for real results improvement.
Technical Implementation: From Simple to Complex
You can start tracking metrics with basic tools ― IP telephony integrated with a CRM system will give you data to track the first three metrics. Modern systems like Ringostat automatically collect and structure data.
Pay attention: automated data collection makes sense. Managers should spend work time on sales, not filling reports. If a manager spends more than 20% of time on “paperwork,” the system is configured incorrectly. Every action for recording call results, creating tasks, and updating statuses should happen automatically or with one click.
The advanced level includes AI for transcription and analysis of 100% conversations across dozens of criteria, real-time dashboards, and automatic notifications about errors and deviations.
The main advice ― don’t try to implement everything at once. Start with 2-3 metrics that most resonate with current team problems. If sales targets are systematically not met, start tracking Call-to-Deal Conversion to understand where deals are lost. If clients complain about inability to reach you ― focus on Call Response Rate. If you suspect problems with individual managers ― build Individual Performance Deep Dive, which will show specific shortcomings.
Conclusion
These 5 metrics give you real control over your sales team without turning into a micromanager. Well-designed, clearly configured systems work automatically, generating accurate and complete data for decision-making. Instead of daily questions to employees “how are things with the sales plan?”, you get not only answers but also levers of influence. On time, without micromanagement and manual analytics.
