Evaluating Support Calls Using Call Center QA Software
Support call evaluation requires a different rubric than sales call evaluation. Where sales QA weights closing behavior and objection handling, support QA weights empathy, first-call resolution, and compliance with service recovery processes. This guide walks QA managers through a 6-step process for evaluating support calls using call center QA software, from criteria design through automated scoring and coaching routing.
This guide is for QA managers overseeing 20 to 150-plus support agents in financial services, healthcare, or retail environments where resolution quality and compliance are the primary performance dimensions.
What You'll Need Before You Start
You need access to your last 30 days of call recordings, a list of your current support evaluation criteria if any exist, and clarity on which compliance requirements apply to your team. For financial services and healthcare teams, identify the specific regulatory dimensions, for example disclosure timing or HIPAA consent language, that must appear in your rubric before configuring anything else. Allow 2 to 4 hours for initial rubric configuration and 4 to 6 weeks for calibration to reach reliable alignment with human reviewer scores.
Step 1: Define Evaluation Criteria for Support Calls
What to do: Identify 4 to 6 scoring dimensions specific to support call quality. Core dimensions for support evaluation include empathy and emotional acknowledgment, first-call resolution behavior, compliance with disclosure or service recovery scripts, process adherence, and communication clarity. Each dimension needs a behavioral anchor at each scoring level, not just a label.
Why this matters: Support rubrics that copy sales rubric structures will weight the wrong behaviors. A support agent who correctly resolves a billing dispute without ever attempting a cross-sell should score well. A rubric that includes "closing behavior" as a weighted criterion will penalize that agent inappropriately.
Decision point: Decide whether to weight dimensions equally or by business impact. Equal weighting is simpler to maintain but less diagnostic. Weighted rubrics, for example compliance at 30 percent, empathy at 25 percent, resolution at 25 percent, and process adherence at 20 percent, surface which specific behaviors are driving quality outcomes. According to ICMI's contact center quality monitoring research, teams with 50 or more agents benefit from business-impact weighting because the diagnostic value justifies the setup time.
Common mistake: Defining empathy as a binary yes or no rather than on a 1 to 5 scale. Binary scoring cannot distinguish between an agent who technically acknowledges the customer's frustration and one who genuinely shifts the emotional tone of the call. Use behavioral anchors at each point on the scale.
Step 2: Configure Weighted Rubrics in QA Software
What to do: Enter your criteria into your QA platform with weights that sum to 100 percent. For each criterion, write the behavioral anchor descriptions for what good, adequate, and poor performance look like. The "what good looks like" and "what poor looks like" context descriptions are the most important configuration step because they determine whether automated scores align with human reviewer judgment.
Why this matters: First-run automated scores without behavioral anchor context can diverge significantly from human judgment. A top-performing agent may score 56 percent on an unconfigured rubric because the system is evaluating against generic language patterns rather than your team's specific service standards.
Insight7 supports configurable weighted rubrics with main criteria, sub-criteria, and behavioral anchor descriptions per score level. The platform applies both script-compliance checking, for exact regulatory language, and intent-based evaluation, for conversational criteria like empathy. Weights are editable at any time as your criteria evolve.
See how rubric configuration works for support teams at insight7.io/improve-quality-assurance/.
Common mistake: Loading criteria into the platform without behavioral anchor descriptions and expecting accurate scores from the first run. Plan for a calibration period of 4 to 6 weeks before treating automated scores as reliable.
Step 3: Run Calibration Against Human Reviewers
What to do: Score a set of 20 to 30 calls with both the automated system and two or more trained human reviewers independently. Calculate the percentage of criterion-level scores where the automated score and the human score agree within one point. Your target is 85 percent agreement or higher before using automated scores for agent performance decisions.
Why this matters: Calibration agreement tells you whether your rubric behavioral anchors are specific enough. If automated and human scores disagree on empathy in 40 percent of calls, the empathy criterion definition is too abstract. Refine the behavioral anchors at each score level until agreement reaches threshold.
According to SQM Group research on call center first-call resolution, programs running monthly calibration sessions maintain rubric accuracy better than those calibrating less frequently. SQM data shows that rubric drift, where scores gradually diverge from current human judgment, accumulates when calibration sessions are skipped.
Decision point: If calibration agreement is below 75 percent after two calibration cycles, revisit the behavioral anchor descriptions before expanding to full automated scoring. Expanding to 100 percent coverage with a rubric that disagrees with human judgment at this rate amplifies inaccurate data rather than providing useful performance insight.
Step 4: Score 100% of Calls Automatically
What to do: Once calibration agreement reaches 85 percent, activate automated scoring across all incoming support calls. Set alert thresholds for criterion-level scores below target, for example any call scoring below 3 out of 5 on compliance, so that high-priority issues surface for human review without requiring manual triage of every call.
Why this matters: ICMI benchmarks show that contact centers reviewing only 3 to 10 percent of calls cannot reliably detect individual agent performance trends. Sample sizes this small cannot distinguish a persistent behavior pattern from a bad day. Population-level data changes coaching from hypothesis to evidence.
Insight7 processes call recordings automatically through the scoring pipeline, applying the configured rubric to every call and delivering criterion-level scores per agent, per call, and per time period. A 2-hour call processes in under a few minutes. The platform supports batch processing for high-volume environments through integrations with Zoom, RingCentral, Amazon Connect, and other telephony infrastructure.
Common mistake: Treating 100 percent coverage as the goal rather than the baseline. Coverage enables visibility. What you do with the visibility, calibration, trend analysis, and coaching routing, is where the performance improvement happens.
Step 5: Surface Agent-Level Trends by Criterion
What to do: After two to four weeks of automated scoring, pull criterion-level performance reports per agent. Identify which agents show consistently low scores on the same criterion across multiple calls. Separate individual patterns from team patterns: if 60 percent of agents score below threshold on the same criterion, that is a training problem, not an individual coaching problem.
Why this matters: Aggregate QA scores mask the specific behavior patterns that coaching needs to target. An agent with an overall score of 74 might be scoring 90 on compliance and 45 on empathy. The coaching target is empathy, not general performance improvement.
Insight7's dashboard shows dimension-level performance per agent, per team, and per time period. Managers can see whether empathy scores are improving after coaching without manually reviewing individual calls. The agent scorecard clusters multiple calls into a single performance view, showing both the average and the distribution across calls.
Step 6: Route Low-Scoring Criteria to Coaching
What to do: Set automated triggers that route calls where a specific criterion falls below a configured threshold to a coaching workflow. The routing should identify the criterion that failed, link to the specific transcript evidence for the coaching conversation, and suggest a targeted practice scenario based on the gap.
Why this matters: Manual coaching routing is where QA data dies. Managers review reports, identify issues, and then fail to schedule coaching sessions before the behavior repeats across another 20 calls. Automated routing ensures that criterion gaps generate coaching assignments without depending on manager follow-through as the sole mechanism.
Common mistake: Routing all low-scoring calls to coaching rather than routing specific criterion failures. If an agent scores low on 3 of 6 criteria in the same call, route coaching for the highest-weighted criterion first. Coaching multiple gaps simultaneously produces less behavior change than sequencing by priority.
FAQ
What criteria matter most for evaluating support calls?
For support calls, first-call resolution behavior and empathy during escalation moments are the two criteria most predictive of customer satisfaction, according to SQM Group research. Compliance with disclosure or service recovery scripts matters highly in regulated industries. Weight your rubric with resolution and compliance as the highest-weighted dimensions, and define behavioral anchors at each score level rather than using binary pass or fail scoring.
How do you configure QA software for 100% call coverage?
Start with a calibrated rubric: run 20 to 30 calls through both automated scoring and human review, measure criterion-level agreement, and refine behavioral anchor descriptions until agreement reaches 85 percent or higher. Once calibrated, activate automated scoring across all calls with alert thresholds for criterion-level failures. Plan for a 4 to 6 week calibration period before treating scores as reliable for agent performance decisions.
QA managers configuring support call evaluation programs: See how Insight7 handles weighted rubric configuration, 100 percent call coverage, and criterion-level trend reporting for 20 to 150-plus agent teams at insight7.io/improve-quality-assurance/.
