AI customer feedback analysis
Customer feedback analysis that explains why
Poth connects what customers say with account and product evidence, then answers concrete questions with citations, retrieval context, and the uncertainty left in the data.
Feedback is not one dataset
A request in a call, a complaint in support, and a drop in usage may describe the same customer problem. Analyzing those sources separately produces themes. Connecting them produces a testable explanation.
Customer conversations
Calls, meeting transcripts, emails, shared Slack threads, interviews, and survey responses.
Support and requests
Tickets, escalations, feature requests, public reviews, and the account context behind each one.
Commercial context
CRM records, deals, subscriptions, billing, and the identities that connect a person to an account.
Product behavior
Usage and activation evidence that shows whether what customers say matches what they do.
See the current, verified connection paths on the integrations page.
A theme is a symptom. The decision depends on the cause.
Tagging feedback can show that onboarding, price, or a missing feature appears often. It does not establish which explanation caused churn, a lost deal, or a stalled account.
Poth keeps the original source, customer identity, and related behavior available to the analysis. That lets a team move from “customers mention onboarding” to a claim it can inspect account by account.
Example question
Why did new accounts stall after the onboarding call?
- Compare stalled and activated accounts.
- Read the conversations tied to each account.
- Check product behavior and connection status.
- Open the citations behind the leading explanation.
From raw feedback to a checkable answer
- 01
Resolve the customer
Map people, accounts, conversations, issues, and events so evidence does not lose its owner.
- 02
Test an explanation
Ask a concrete question and compare candidate causes across the connected evidence.
- 03
Show the proof
Return cited evidence and retrieval context so readers can inspect what supports the analysis.
- 04
Expose the gap
When the evidence cannot answer, say so and identify what the team would need to learn next.
Customer feedback analysis questions
What is customer feedback analysis?
Customer feedback analysis is the process of organizing and interpreting qualitative customer evidence—such as calls, tickets, surveys, reviews, and requests—to understand recurring problems, needs, and causes. Poth extends that analysis by connecting feedback to account and product context and keeping each finding tied to evidence.
How is Poth different from sentiment analysis or theme tagging?
Sentiment and themes describe what appears in a body of feedback. Poth is designed for questions that also need account identity, product behavior, source provenance, and a citation trail. The goal is an explanation a team can inspect, not only a label or summary.
Which feedback sources can Poth analyze?
Poth supports a verified set of managed app connectors, direct data sources, meeting and transcript sources, structured databases, websites, and CSV or TXT imports. Available records are always bounded by the connected account, permissions, scopes, and import settings.
Does Poth invent an answer when evidence is missing?
Poth is designed to ground answers in retrieved evidence and can return that the available data does not answer the question. Readers should still inspect the cited sources and treat missing or contradictory evidence as a reason for further review.
Keep exploring
Ask the question your dashboard cannot answer
Connect customer evidence, ask a concrete question, and inspect what supports the answer.