Quality Assurance
Definition
Quality assurance in customer support is the operational practice of reviewing a sample of customer interactions — chats, calls, emails, tickets — against a defined quality rubric to evaluate agent performance, identify systemic issues, and drive continuous improvement. A QA framework defines the criteria for a quality interaction: greeting and tone, problem understanding, accuracy of information provided, adherence to process, empathy, and resolution completeness. QA reviewers (dedicated QA analysts or team leads) score interactions and provide feedback to agents. Aggregate QA scores reveal training needs, process gaps, and the correlation between quality metrics and customer satisfaction.
Why It Matters
QA is the mechanism by which support teams maintain and improve the human side of the support experience — something that metrics alone cannot capture. A ticket can have a fast response time and a closed status while still delivering a poor customer experience through unhelpful responses, incorrect information, or cold tone. QA catches these issues before they become patterns. For AI chatbot quality, automated QA tools evaluate every bot conversation (not just a sample), flagging low-confidence responses, off-topic answers, and conversations that escalated after the bot failed — enabling continuous AI improvement at scale.
How It Works
QA programs operate through a sampling and scoring cycle: interactions are sampled (randomly or targeted to specific agents, channels, or issue types), reviewed against a scoring rubric by a QA analyst, scores and feedback are shared with the agent and their manager, patterns across multiple reviews inform training priorities, and improvement is tracked over subsequent review cycles. QA scores are typically separate from CSAT — QA measures process adherence and quality from the company's perspective, while CSAT measures experience quality from the customer's. Both are needed for a complete picture.
Quality Assurance — Scorecard & Review Workflow
QA Scorecard
90–100%
Excellent
80–89%
Good
< 80%
Needs work
QA Review Workflow
Random sampling
10% of tickets reviewed
Score
QA scorecard applied
Coach
1:1 feedback session
Improve
track delta week-over-week
Real-World Example
A 99helpers customer implements a bi-weekly QA review program where team leads review 5 chat interactions per agent per two-week cycle. They create a 10-point rubric covering: accurate information, appropriate tone, complete resolution, proper process adherence, and effective use of available tools. After three months, aggregate QA scores identify that new agents consistently underperform on the 'complete resolution' criterion — they close tickets after addressing the presenting issue without checking for related problems. Targeted coaching on resolution completeness improves both QA scores and FCR.
Common Mistakes
- ✕Scoring against a rigid rubric without considering context — QA criteria should allow for judgment calls when following the script would produce a worse outcome
- ✕Sharing QA feedback only as criticism without acknowledging strengths — effective QA coaching includes positive reinforcement of excellent behaviors
- ✕Sampling only problematic agents or interactions — QA should be systematic and sample all agents to provide comparative data and prevent selection bias
Related Terms
Support Analytics
Support analytics is the collection and analysis of operational data from customer support activities — ticket volume, resolution times, satisfaction scores, and agent performance — to drive data-informed decisions and continuous improvement.
Customer Satisfaction Score
Customer Satisfaction Score (CSAT) is a metric that measures how satisfied customers are with a specific interaction, product, or experience, typically collected through a simple post-interaction survey asking customers to rate their satisfaction on a numeric scale.
Agent Assist
Agent assist is an AI-powered tool that supports human support agents in real time by suggesting responses, surfacing relevant knowledge base articles, identifying customer intent, and recommending next best actions during live interactions.
Chat Transcript
A chat transcript is the complete written record of a conversation between a customer and a support agent or AI chatbot, preserving the full message exchange for reference, quality review, training, and compliance purposes.
First Contact Resolution
First contact resolution (FCR) is the percentage of customer support interactions resolved completely during the first contact, without requiring the customer to follow up or the issue to be escalated.
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