Data cleansing

Data enrichment is the process of attaching additional attributes to an existing customer record. These attributes can come from internal systems or external sources, so analysis can be segmented rather than read as a flat total. In CX, this usually means joining account tier, plan type, tenure, region, channel, or order history onto a feedback or support ticket. A plain complaint count only shows volume. An enriched record shows which segment it belongs to. For example, a spike in billing complaints might look like a general product problem until enrichment reveals it sits almost entirely within one plan tier. Enriched data carries the same privacy obligations as the source record it is attached to. OnClarity is SOC 2 Type II certified, is GDPR and HIPAA Ready, and aligns with Saudi PDPL. Enrichment pipelines should meet the same compliance standard as data cleansing.

Unstructured data is information with no predefined schema: call recordings, chat transcripts, email bodies, open-ended survey comments, app reviews, and social posts. It contrasts with structured fields like ticket category, CSAT rating, or handle time, which record that something happened but not why. In CX, the diagnostic detail lives in the free text. A ticket disposition coded “billing” only becomes useful once the transcript shows customers describing a confusing invoice layout. Voice of customer analytics depends on reading this text at scale. OnClarity’s Voice of Customer does this for Grubhub, unifying diner feedback into one view. Before analysis, unstructured text typically needs data cleansing to remove duplicates and standardize formatting.

Topic clustering groups conversations by theme to size the drivers behind contact volume, without requiring an analyst to define categories in advance. Because clusters emerge from what customers actually said, the method surfaces new issues that a fixed intent taxonomy would file under a generic other.

Interaction analytics is the analysis of full conversation transcripts to surface demand drivers, friction, and risk across every contact rather than a sampled few. Because it reads what customers actually said, it identifies emerging issues that structured ticket fields and survey scores are too coarse to reveal.