Scaling interactions, converging the messaging, proving it with statistics.

Scale the interactions. Converge the messaging. Prove it with statistics.

A multi-brand manufacturer grows by adding brands, channels, and volume - but the feedback program has to scale with it. American Bath Group needed every customer interaction, across email, voice, and public reviews, to converge into one statistically significant view. Not just counted - tested. And not just tested - layered with the manufacturing expertise that turns a customer complaint into a line-item fix on the production floor.

A customer who emails about a freight-damaged shipment, calls about the same order a day later, and leaves a public review a week later is one person with one problem. But in most organizations, that problem is counted three times, in three tools, by three teams - and nobody sees the full picture. For a multi-brand manufacturer, the fragmentation multiplies: each brand has its own queue, its own inbox, and its own review profile.

A single taxonomy, a single scale, a single record.

StatQuestions ingests every support email, every inbound call recording, and every public review across the brand portfolio. Calls are transcribed with multichannel speech recognition, so the agent and customer are separated cleanly - which matters when you are measuring behavioral signals like empathy, vague language, and commitment to a timeline.

Every message - whether it arrived by phone, email, or public post - is classified into one canonical taxonomy. A freight damage complaint lands in the same bucket whether the customer emailed, called, or posted online. Repeat contacts across channels collapse into a single canonical record, so volume stays honest.

All three channels are then scored on the same 0–100 scale, so email, voice, and public feedback can be ranked and compared directly - not stranded in separate tools with incompatible metrics.

Root causes ranked by impact, with confidence attached.

Instead of counting mentions and guessing which trend matters, the customer experience team now sees root causes ranked by estimated dollar impact - rework hours, repeat-contact cost, and effort per case - with statistical significance attached to every movement. A spike in delivery complaints is labeled real or random before anyone schedules a meeting.

The manufacturing expertise layers on top: a freight damage trend isn't just a number, it maps to a packaging line, a carrier lane, or a loading dock. The CX team can hand a finding to operations with the statistical proof and the manufacturing context attached - so the fix happens on the floor, not in a deck.

The result is a feedback program the CX team can defend to finance: here is the issue, here is what it costs, here is the test that says it is real, and here is the record that proves we are not double-counting.