Connect Trustpilot Review to ChatGPT

Multi-Client Trustpilot Reputation Command Center

Using the Catchr MCP, pull Trustpilot Review data for [List of Client Account IDs] over [Monitoring Period] and compare it with [Baseline Period]. For each Account ID, report new-review volume from distinct Review ID values, average Rating, the 1-to-5-star distribution, low-rating share, reply coverage from Reply Text, median reply time from Review Date and Reply Date, and average Likes per review. Treat Average Rating, Review Count, and Stars Count fields as possible account-level snapshots and do not sum repeated values unless their grain is verified. Infer recurring praise and complaint themes from Review Title and Review Text, clearly label them as inferred, flag breaches of [Client-Specific Rating, Reply-Time, and Review-Volume Thresholds], rank the clients the consultant should review first, and recommend one evidence-based action for each priority account.

Client Trustpilot Reputation Executive Brief

Using the Catchr MCP, analyze Trustpilot Review data for [Client Account ID] during [Reporting Period] versus [Comparison Period]. Use Review ID, Review Date, Rating, Average Rating, Review Count, the five Stars Count fields, Country Code, Likes, Review Title, Review Text, Reply Date, Reply Text, and Review URL to explain review volume, rating direction, geographic differences, response coverage, response speed, and the leading customer-feedback themes. Clearly separate measured fields from themes inferred from text, identify which issues are new, persistent, improving, or worsening, and produce a client-ready brief with an executive summary, measurable wins, reputation risks, supporting Review URLs, and three prioritized actions with [Owner] and [Target Date].

Cross-Client Review Escalation Queue

Using the Catchr MCP, retrieve Trustpilot Review records for [List of Client Account IDs] from [Recent Period]. Build a consultant escalation queue using Account ID, Review ID, Review Date, Reviewer Name, Rating, Likes, Review Title, Review Text, Reply Date, Reply Text, Country Code, and Review URL. Apply [Priority Rules, e.g., Rating at or below 2, unanswered beyond the reply SLA, Likes above a visibility threshold, or a recurring high-severity complaint], then rank items by severity, recency, visibility, and inferred theme recurrence. For each priority review, explain the evidence, recommend the internal service or reputation action, draft a concise empathetic response for client approval without making unsupported promises, and return urgent, monitor, and positive-opportunity counts per client with [Owner] and [Resolution Target].

Morning Client Trustpilot Triage

Using the Catchr MCP, pull Trustpilot Review data for [List of Client Account IDs] from [Recent Period] and compare each account with [Baseline Period]. For every Account ID, calculate new-review volume from distinct Review ID values, average Rating, low-rating share, reply coverage, median reply time, and average Likes; scan Review Title and Review Text for urgent or repeated issues and clearly label those themes as inferred. Apply [Client-Specific Targets and Priority Rules], rank the clients I should handle today, and provide the Review ID, Review URL, evidence, urgency reason, suggested owner, and exact first action for each priority item.

Monthly Trustpilot Client Report

Using the Catchr MCP, create a client-ready Trustpilot Review report for [Client Account ID] covering [Current Period] versus [Previous Period]. Summarize distinct Review ID count, average Rating, Average Rating, Review Count, the five Stars Count fields, Country Code distribution, Likes, reply coverage, response time from Review Date to Reply Date, and trends over time. Avoid summing repeated account-level snapshot fields unless their grain is verified. Explain in plain English what improved, what declined, and which inferred praise or complaint themes from Review Title and Review Text accompanied the change. End with three priorities for [Next Period], the owner and review-based KPI for each action, plus an appendix of Review URLs supporting the main findings.

Client Reply Quality Workbench

Using the Catchr MCP, retrieve Trustpilot Review records for [Client Account ID] over [Period] using Review ID, Reviewer Name, Review Date, Rating, Likes, Review Title, Review Text, Reply Date, Reply Text, Country Code, and Review URL. Separate unanswered reviews from answered reviews, prioritize them using [Rating, Age, Likes, and Theme Rules], and evaluate existing Reply Text against [Response Guidelines] for timeliness, empathy, specificity, acknowledgement, and unsupported promises. For each priority item, summarize the customer's concern or praise, draft a new or improved response for client approval, recommend the internal follow-up, and return an approval-ready workbench with evidence, [Owner], [Target Date], and aggregate reply-coverage and reply-time metrics.

E-commerce Buyer Trust Scorecard

Using the Catchr MCP, create a Trustpilot Review buyer-trust scorecard for [E-commerce Account ID] over [Period] versus [Previous Period]. Report distinct Review ID count, average Rating, Average Rating without summing repeated snapshots, the 1-to-5-star distribution, low-rating share, review Likes, reply coverage, and median time from Review Date to Reply Date. Infer the strongest trust drivers and purchase objections from Review Title and Review Text, clearly label those themes as inferred, and quantify their frequency, associated average Rating, and change over time. Conclude whether review-based trust signals are strengthening, identify three evidence-backed proof points the business may validate for marketing, and list three reputation risks with [Owner] and [Review-Based Success Target]; do not claim an effect on sales or conversion without commerce data.

Customer Experience Friction by Market

Using the Catchr MCP, analyze Trustpilot Review records for [E-commerce Account ID] over [Period] and compare them with [Previous Period]. Use Country Code, Review ID, Review Date, Rating, Likes, Review Title, Review Text, Reply Date, Reply Text, and Review URL to infer customer-experience themes such as product quality, delivery, returns, refunds, support, pricing, or website experience only when the text supports them, and label every theme as inferred. For each country and theme, report distinct review count, average Rating, low-rating share, change over time, reply coverage, and median reply time, applying [Minimum Review Count] before ranking markets. Prioritize the top three friction points by frequency, severity, growth, and Likes, then assign one concrete operational action, [Owner], and measurable review-based outcome to each.

Review Recovery and Advocacy Planner

Using the Catchr MCP, pull Trustpilot Review data for [E-commerce Account ID] from [Recent Period]. Split reviews into a recovery queue based on [Recovery Rules, e.g., Rating at or below 3 or unanswered beyond the reply SLA] and an advocacy queue based on [Advocacy Rules, e.g., Rating at or above 4, relevant Review Text, and Likes above a threshold]. Use Review ID, Reviewer Name, Review Date, Rating, Likes, Review Title, Review Text, Reply Date, Reply Text, Country Code, and Review URL to explain every selection. For recovery items, draft an empathetic response and recommend the internal follow-up; for advocacy items, identify inferred product or service proof points and a proposed validation step. Return prioritized queues with [Owner] and [Target Date], and require human approval plus customer permission before republishing names or review text.

Trustpilot Review Data Integrity Audit

Using the Catchr MCP, audit Trustpilot Review data for [Account ID or All Connected Accounts] over [Period]. Check completeness and consistency across Account ID, Review ID, Review Date, Rating, Average Rating, Review Count, Reviewer Name, Country Code, Likes, Review Title, Review Text, Reply Date, Reply Text, Review URL, the five Stars Count fields, Platform Name, and Extracted Date. Flag duplicate Review ID values within an Account ID, missing identifiers or dates, Rating values outside [Valid Rating Range], negative counts or Likes, malformed Review URLs, Reply Date earlier than Review Date, Extracted Date earlier than Review Date, reply text without a reply date or vice versa, unexpected Platform Name values, and conflicting records sharing a Review ID. Return a reproducible exception table with account, review, failed rule, observed value, severity, and recommended pipeline check.

Extraction Freshness and Review Coverage Monitor

Using the Catchr MCP, pull Trustpilot Review data for [Account ID or All Connected Accounts] over [Lookback Period]. By Account ID and [Day or Week], compare Extracted Date with Review Date, count distinct Review ID values, calculate average Rating from record-level reviews, and track Review Count and the five Stars Count fields as possible account-level snapshots. Flag extraction lag above [Maximum Lag], missing calendar intervals, duplicate reviews, unexpected new-review-volume gaps against [Baseline Window], and snapshot changes above [Change Threshold]. Do not sum repeated snapshot values unless their grain is verified; distinguish definite pipeline failures from possible real changes in review activity, and return an account-level health summary plus a record-level investigation table with the next validation query for every anomaly.

Rating, Text, and Reply Consistency Monitor

Using the Catchr MCP, retrieve Trustpilot Review records for [Account ID or All Connected Accounts] over [Period] using Review ID, Review Date, Rating, Review Title, Review Text, Likes, Country Code, Reply Date, Reply Text, and Review URL. Derive sentiment, theme, and urgency labels from the review text, explicitly marking them as inferred, then validate sentiment against Rating using [Agreement Rules]. Flag missing or insufficient text, repeated text across different Review IDs, rating-text disagreement, replies missing one of Reply Date or Reply Text, reply times above [Reply SLA], and high-Likes severe reviews without a reply. Return agreement and reply-lifecycle metrics by Account ID and Country Code, a stratified exception sample keyed by Review ID, and concrete recommendations for the ingestion, classification, or alerting pipeline before operational use.

Replace [variables] Facts ≠ hypotheses Action + follow-up metric

Connect Trustpilot Review to ChatGPT

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How to connect Trustpilot Review to ChatGPT ?

Authorize the Trustpilot Review account in Catchr

Add the Catchr MCP from official ChatGPT store

Choose, adapt, and run a prompt.

A simple client question should not trigger an hour of data prep.

Pull another CSV

Rebuild the context

Summarize it by hand

Ask the account. Get to the explanation.

Prepare data before every question.

Connect once. Keep asking.

Twelve Trustpilot Review prompts for analyzing real account data.

Trustpilot Review to ChatGPT FAQs

What Trustpilot Review data can ChatGPT analyze through Catchr?

How granular can Trustpilot Review analysis be in ChatGPT?

Can ChatGPT summarize recurring customer themes in Trustpilot Review?

Can ChatGPT compare reputation across locations, markets, or periods in Trustpilot Review?

What types of prompts work best for Trustpilot Review analysis?

Can I combine Trustpilot Review with other data sources in ChatGPT?

Can I analyze multiple Trustpilot profiles or markets together?

What do I need to connect Trustpilot Review to ChatGPT with Catchr?

How recent is the Trustpilot Review data, and how much history can I analyze?

Can ChatGPT change anything in Trustpilot Review through Catchr?

How much does it cost to connect marketing data to ChatGPT with Catchr?

‍Can I try the ChatGPT integration before choosing a plan?

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