Private Label & OEM2 min read

Ceramic Coating Complaint Rates: Use the Right Denominator

Compare coating feedback with units, installations and exposure in view instead of judging quality from complaint counts alone.

Updated · NANOLAB Knowledge

Ten complaints mean something different across twenty installations than across ten thousand. When reviewing a ceramic coating programme, define the population behind the count. A complaint total without its denominator can encourage the wrong product or process decision.

Decide what one case represents

Separate customer contacts, distinct affected jobs and confirmed product issues. One installation may generate several messages, while a single distributor report may describe several vehicles. Keep those levels distinct so repeated correspondence is not counted as repeated failures.

Choose a relevant denominator, such as completed installations within a defined period, and state its limitations. Bottles shipped may not equal jobs completed, particularly where stock remains unused or bottle sizes differ.

Compare equivalent groups

Review product, batch, installer group, surface and time in service. A newly launched range may have many early application questions but little long-term exposure. Comparing its raw count with an established range can be misleading.

For a hypothetical example, five recorded cases in one hundred documented jobs is a five-percent contact rate for that defined dataset. It is not automatically a five-percent product-failure rate; the case classification still matters.

Keep diagnosis separate from the metric

Investigate recurring symptoms with process records and technical support. Report unresolved cases as unresolved rather than forcing them into a favourable category. Use trends to guide investigation, not to assign blame without evidence.

Discuss programme evaluation with NANOLAB Private Label and OEM. A useful dashboard combines counts, exposure and confirmed findings, making quality discussions more precise while preserving the uncertainty in incomplete data.