CustomerFeedback Intelligence
Turning thousands of unstructured customer reviews into same-day operational decisions — what to fix, how urgent it is, and who should act.
It isn't volume.
It's ambiguity.
A retailer's reviews are full of mixed signals — praise and problems in the same breath. Traditional sentiment analysis flattens them to "neutral," and the most fixable issues stay buried in the text.
I love this dress — but the brand runs large. I'd order a size down.
Fragmentation
Separate NLP models for sentiment, classification, summarisation — each with its own data and upkeep.
Complexity
Cost and latency compound when review volume triples during seasonal peaks.
Delay
Insight arrives in periodic reports, days after the signal — too late to act on.
Same rating.
Different customers.
Clustering on behaviour — not stars — reveals four customer types that traditional review metrics fail to distinguish. A single response strategy can't serve them all.
K-Means clustering
Segments were derived from behavioural signals including review length, recommendation patterns, and sentiment conflict revealing actionable differences that rating averages alone could not capture
The highest-value insight isn't from angry customers. It's from the ones who still recommend — while telling you exactly what to fix.
One review in.
Five decisions out.
A single GenAI pass replaces a fragmented stack of models — and returns outputs teams can act on, not just analyse.
Sentiment
Four-label, incl. mixed-negative — the nuance 3-class systems miss.
Category
Product division & department, for buying and merchandising.
Summary
The customer's core message in ≤25 words.
Message
A brand-toned, specific reply ready for the customer.
Insight + urgency
Root cause, owning team, action, and a four-tier urgency tag.
Every signal routed to
the team that owns it.
The real shift isn't the model — it's the redesigned flow around it. Each review is tagged by urgency and sent to the right team with a concrete action. Tap a tier.
Human-in-the-loop, by design. A validation gate sits before any automation — CX teams confirm sentiment, urgency and tone, logging every correction as a labelled example. The system proposes; people decide. Trust is earned before scale.
From reactive support to proactive decisions.
Decision latency, collapsed.
Feedback-to-insight drops from days or weeks to same-day — early enough to act before issues scale.
Configuration-driven scalability.
New categories, departments, or classification schemes can be introduced through prompt and workflow configuration
Brand voice, at scale.
Every reply reflects the customer's specific experience while holding a consistent tone across thousands of responses.
A cost structure that invites experiments.
Testing a new category is effectively free — no budget-approval cycle to learn something.
The system's own output names the primary cost driver: fit & sizing — the single highest-leverage fix in apparel retail.
Built for production, not demos.
Six prompt configurations — zero-shot, few-shot and chain-of-thought, each in baseline and business-aware versions — were scored against two LLM-as-judge rubrics.
Few-shot won on consistency, not peak score. Chain-of-thought reached the highest single scores but proved unstable under strict, business-aligned evaluation — the wrong trade-off for high-volume production. Choosing the reliable option over the flashy one is the call that matters.