Every step of your customer's journey, mapped and measured.
StatQuestions traces the customer journey from pre-sale to billing — across email, voice, and public reviews — surfacing exactly where friction spikes and which trends are statistically real.
American Bath Group set out to scale customer interactions across a portfolio of brands - converging email, voice, and public reviews with statistical significance, then layering the findings with manufacturing expertise.
Here is how they unified three fragmented channels into one tested, defensible view.
Counting mentions is not insight.
Most feedback tools tally keywords and call a rise a trend. But a jump from 5 mentions to 7 can be pure chance. StatQuestions tests whether a change is statistically real before you spend a dollar chasing it, then shows you the confidence and the caveats side by side.
Stop counting mentions. Start classifying.
A hybrid pipeline reads every message and files it into one canonical taxonomy, so the same issue lands in the same place every time.
Canonical taxonomy
One agreed structure of issues and sub-issues, enforced at the database layer so it never drifts.
Hybrid pipeline
Regex for the obvious, a trained classifier for the rest, and an LLM only where it earns its place.
Confidence you can see
First-pass confidence moved from 28% to 52% as the taxonomy tightened. The number is on the screen, not hidden.
The same complaint, counted once.
One angry customer who emails, calls, and posts is one problem, not three. StatQuestions collapses duplicates into a single canonical record and keeps count of how many collapsed in.
Canonical records
Near-identical messages merge into one record with a stable identity you can track over time.
Multiplicity kept
Every merge carries a count, so volume stays honest and nothing gets silently dropped.
Counts you can defend
Report the real number of distinct issues, not an inflated tally of restated duplicates.
Significance shown, not implied.
A control chart tells the difference between normal variation and a real shift. Every trend arrives with a test attached, so you know what deserves action.
A test on every trend
χ² and p-values run on the movements that matter, so a spike is labeled real or random.
Signal versus noise
Control limits mark the band of normal variation. A point outside it is worth a meeting.
Confidence and caveats
Sample size and limitations sit next to every finding, so the claim is only as strong as the data.
Email, voice, and public. One scale.
A complaint in an inbox, on a call, and in a public review is the same complaint. StatQuestions scores all three on one comparable scale so you can rank them together.
Comparable by design
Every channel scores 0 to 100 on the same Handle scale, so the numbers actually line up.
Voice scored like email
Calls are transcribed and scored on the same rubric, not stranded in a separate tool.
Public reviews included
What shows up in the open counts too, weighted alongside private channels.
One platform, every view.
Six connected modules over the same tested, deduplicated corpus.
Complaint Dashboard
Root-cause view across brands and business units, with significance on every trend.
Causal Analysis
Control charts and χ² tests that separate a real shift from normal variation.
Waste Audit
Find the duplicated, misrouted, and low-value work hiding inside the queue.
Email Intelligence
Classify and score the full inbox corpus, confidence shown on every record.
Voice Intelligence
Calls transcribed and scored on the same Handle scale as email and public.
Decision Catalyst
Turn a tested finding into a ranked, owned next step, not another chart.
StatQ Org Health
Statistical rigor applied to employee experience. Eight dimensions, scored and triangulated.
Enterprise security by design.
Your customers' words are sensitive. StatQuestions handles them with encryption, tight access control, and careful PII treatment.
The thinking behind the numbers.
Field notes on statistical rigor for customer feedback, written for the people who have to act on it.
Read the blogMost of your complaint trends are noise.
Sample size, significance, and the multiple-comparisons trap, in plain terms for feedback teams.
See it on your own data.
Bring a slice of your feedback. We will show you which trends are real and which are noise, on the same call.