Unstructured data

Text analytics automates the process of extracting structure and meaning from unstructured written feedback: support tickets, chat transcripts, survey verbatims, reviews, and social posts. Core techniques include classifying text into predefined topic taxonomies, extracting named entities such as products, features, and locations, modeling topics to surface unlabeled themes, and tagging a sentiment score to gauge tone. The practical output is a count of how often each issue appears and how that frequency trends over time. Results are only as useful as the underlying taxonomy: a vague or inconsistent category structure produces noisy, hard-to-action data. OnClarity’s Voice of Customer applies these techniques across feedback aggregated from 100+ sources.

Speech analytics automates analysis of recorded or live customer calls. It uses speech-to-text transcription combined with natural language processing to extract keywords, topics, and sentiment. Many systems also measure acoustic signals such as silence, talk-over, and speaking pace, to flag stress or disengagement that word choice alone would miss. Real-time speech analytics prompts agents mid-call and raises live compliance flags. Post-call analysis surfaces trends and root causes across a larger call sample. Standard uses include detecting required compliance phrases, categorizing contact reasons, and feeding evidence into quality assurance scoring.

A verbatim is a customer’s exact words, kept unedited as evidence in voice-of-customer reporting. Verbatims carry the specificity that aggregate scores strip out, which is why they persuade product and executive audiences when a percentage change on a dashboard cannot do so on its own.

Data cleansing is the process of correcting or removing records that are duplicated, incomplete, malformed, or inconsistent before they enter analysis. Standard operations include deduplication, standardizing formats (dates, channels, agent names), validating entries against defined rules, handling missing values, and reconciling customer or ticket identifiers across source systems. Skipping this step lets duplicate tickets inflate complaint volumes and distort topic rankings. For example, a customer’s chat and email thread about the same issue should be merged into one case before counting contacts. Cleansing differs from data enrichment, which adds new attributes rather than fixing existing ones. It often happens as a first pass over unstructured data, before any topic or sentiment model runs.