Business Applications
Why CRM data quality decays, and what actually holds it
Data quality is an operating problem before it is a technical one. Controls help; ownership, incentives and a workable process matter more.
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Automation in customer service works when the boundary between machine and human is explicit, measured and adjustable.
Service automation fails in a specific way: it handles the easy cases well, mishandles a minority, and the mishandled minority generates the complaints that define the programme. The design question is not how much to automate but where the boundary sits and how quickly it can be moved.
Twelve months of case history usually shows that a small number of intents account for most volume. Classify that history first. The intents worth automating are high-volume, low-variation and low-consequence-if-wrong. Everything else stays with people until the evidence changes.
| Measure | What it tells you |
|---|---|
| Containment rate | Share of cases resolved without a human |
| Escalation after automated response | Whether the automation is genuinely resolving |
| Repeat contact within 7 days | Hidden failure that containment alone hides |
| Human triage volume | Whether confidence thresholds are set sensibly |
Drafting support for human agents is usually the higher-return starting point. It affects every case rather than a subset, keeps a person accountable for the response, and produces the review data needed to decide which intents can later be handled end to end.
Written by
Daniel designs and delivers Dynamics 365 and Power Platform solutions, with a strong bias towards configuration, explicit process design and data quality controls that hold up long after go-live. He has spent much of his career untangling CRM implementations that recorded administration rather than supporting work.
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Business Applications
Data quality is an operating problem before it is a technical one. Controls help; ownership, incentives and a workable process matter more.