The richest customer signal: open-text reviews and complaints: is also the hardest to read at scale and the easiest to ignore.
Thousands of reviews land every month across Google, Trustpilot, social and complaint logs: manual reading is impossible, so most banks fall back on tag-based reporting and NPS dashboards that are coarse, lagging, and blind to emerging themes until they balloon. An angry billing complaint and a routine operational gripe get scored alike, despite one quietly breaking trust.
Customer Intelligence came out of watching real signal get treated as noise. We built the layer that reads what customers actually wrote, at the volume they actually wrote it.
Two stages, from raw review text to a concrete remediation list. Classify turns text into signal; Act turns signal into a next step.
BERTopic clustering, BERT emotion classification, LDA cross-validation and LLM-driven topic extraction, combined into one pipeline.
Anger, which breaks trust, is separated from disappointment, which is usually just an operational fix.
Themes are mapped to specific branches and sites, with cross-platform validation flagging the highest-conviction targets.
Concrete actions are extracted from the review corpus: not just a score, a next step.
Temporal analysis measures whether remediation actually moved the numbers across review cycles.
A scheduled job classifies new reviews weekly and alerts on emerging clusters before they escalate.
The problem, the way it’s handled today, and what changes once Customer Intelligence is running.
Turn open-text reviews from a defensive metric into a diagnostic engine for operational improvement: at branch level, in near real time.
Speak to the team