Skills Quality assurance

Quality across languages

Multilingual support quality data has a structural problem: the measurement is usually weaker in exactly the languages that score worse, so a real difference and a measurement artefact look identical in the output.

Quality assuranceScorecards and calibrationAnalysisAny helpdeskRead-only
Installnpx rulebase-skills install cx-multilingual-quality

When to use it

Reach for this when someone says any of these — they are the phrases the skill itself triggers on:

  • compare quality across markets
  • our Dutch team scores lower
  • quality by language

How it works

The method, in the order the skill runs it. The full procedure — tables, worked examples and the edge cases — is in the skill itself.

  1. Establish instrument parity before comparing anything

    Do this first. If it fails, the comparison does not happen.

  2. Separate the four things that produce a language gap

    When a market scores lower, it is one or more of these. They have entirely different remedies and the analysis has to distinguish them: Instrument, Mix, Rubric fit, Genuine capability.

  3. Check the supporting material, not just the agents

    A market scoring low very often has less to work with: Macros and templates, and whether they were translated well or at all, Training material availability, Escalation paths staffed in that language and timezone.

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