Automate QA scoring, calibration workflows, and coaching actions so every interaction is evaluated consistently and improvement plans are tracked to completion.
Most contact centres evaluate fewer than 3% of interactions manually. The sample is too small to be statistically meaningful, QA scores vary between evaluators, and the gap between evaluation and coaching feedback can stretch to weeks — by which time the agent has already repeated the same mistakes dozens of times.
Evaluating 1-3% of interactions means quality issues go undetected for weeks or months.
Different QA analysts interpret the same scorecard criteria differently, undermining agent trust in the process.
Weeks can pass between an interaction and the coaching session, reducing the impact of feedback.
Spreadsheet-based QA lacks version history and cannot demonstrate compliance to regulators or clients.
Purpose-built capabilities — not generic templates you have to work around.
Build weighted scorecards with auto-fail criteria, section scoring, and conditional questions — all in a no-code builder.
Distribute evaluations evenly across QA analysts, weighted by team, campaign, or interaction type.
Run calibration sessions where multiple analysts score the same interaction, then compare results to align standards.
Automatically generate coaching tasks from failed evaluations and track completion through to sign-off.
Visualise quality trends by team, agent, question, and time period to pinpoint systemic issues.
Interactions are selected for evaluation based on configurable sampling rules — random, risk-based, or triggered by customer feedback.
A QA analyst completes the digital scorecard, with auto-fail flags and mandatory comment fields enforced by the system.
If the score falls below threshold or an auto-fail is triggered, a coaching task is automatically assigned to the agent's team leader.
The team leader delivers coaching, records notes, and the agent signs off — all tracked within SwiftCase.
QA managers review dashboards to identify recurring issues and adjust training programmes accordingly.
Try these tools to assess and improve your operations.
Yes. Each section and individual question can be weighted, and you can define auto-fail criteria that override the overall score regardless of other answers.
Yes. If you use a speech analytics platform, SwiftCase can ingest flagged interactions for priority evaluation via API or webhook.
You select a calibration interaction, assign it to multiple QA analysts, and SwiftCase compares their scores side-by-side, highlighting variances by question so you can align scoring standards.
Absolutely. All evaluations, scores, and coaching records are stored with full audit trails and can be exported or accessed via API for regulatory reporting.
See how SwiftCase transforms your QA programme. Book a demo tailored to your contact centre.
