Connect Yelp Review to ChatGPT

Multi-Location Yelp Reputation Command Center

Using the Catchr MCP, pull Yelp 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 share of reviews at or below [Low-Rating Threshold], Language mix, and management reply coverage based on non-empty Reply Text. Treat Average Rating and Review Count as possible account-level snapshots and do not sum repeated values unless their grain is verified. Infer recurring praise and complaint themes from Review Text, clearly label them as inferred, apply [Client-Specific Rating, Volume, and Reply-Coverage Targets], rank the client accounts that need attention first, and recommend one immediate reputation action and one operational follow-up for each priority account.

Client Yelp Reputation and Feedback Brief

Using the Catchr MCP, analyze Yelp Review data for [Client Account ID] during [Reporting Period] versus [Comparison Period]. Use Review ID, Review Date, Rating, Average Rating, Review Count, Reviewer Name, Review Text, Language, Reply Text, and Review URL to explain review volume, rating direction, reply coverage, and the customer-feedback themes associated with improvement or decline. Clearly separate measured results from themes inferred from Review Text, identify which issues are new, persistent, improving, or worsening, and produce a client-ready executive brief with three evidence-backed wins, the three highest reputation risks, supporting Review URLs, and a prioritized 30-day action plan with [Owner], [Target Date], and one review-based success metric per action.

Cross-Client Critical Review Response Queue

Using the Catchr MCP, retrieve Yelp Review records for [List of Client Account IDs] from [Recent Period]. Build a consultant action queue using Account ID, Review ID, Review Date, Reviewer Name, Rating, Review Text, Language, Reply Text, and Review URL. Apply [Priority Rules, e.g., Rating at or below 2, unanswered reviews older than the response SLA, or repeated high-severity complaint themes], then rank cases by severity, age, and inferred theme recurrence. For every priority review, explain the evidence, recommend the internal service or reputation action, draft a concise reply in the recorded Language for client approval without making unsupported promises, and return urgent, standard, answered, and positive-opportunity counts per client with the exact next action and [Owner].

Morning Yelp Client Triage

Using the Catchr MCP, pull Yelp 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 using [Low-Rating Threshold], and reply coverage from non-empty Reply Text; scan Review Text and Language for urgent or repeated issues and clearly label text themes as inferred. Apply [Client-Specific Targets and Priority Rules], rank the clients I should handle today, and provide the affected Review ID, Review URL, evidence, urgency reason, suggested owner, and exact first action for each priority item.

Monthly Yelp Client Report

Using the Catchr MCP, create a client-ready Yelp Review report for [Client Account ID] covering [Current Period] versus [Previous Period]. Summarize distinct Review ID count, average Rating, rating distribution, trend by Review Date, Language mix, and management reply coverage based on Reply Text. Show Average Rating and Review Count separately as possible account-level snapshots and do not sum repeated values unless their grain is verified. Explain in plain English what improved, what declined, and which praise or complaint themes inferred from Review Text accompanied the change. End with unanswered low-rating review count, three priorities for [Next Period], the [Owner] and review-based KPI for each action, and an appendix of Review URLs supporting the main findings.

Multilingual Yelp Reply Workbench

Using the Catchr MCP, retrieve Yelp Review records for [Client Account ID] over [Period] using Review ID, Reviewer Name, Review Date, Rating, Review Text, Language, Reply Text, and Review URL. Separate records with empty Reply Text from answered reviews, group unanswered cases by [Priority Rules], and sort them by rating severity, age, and recurrence of inferred complaint themes. For each priority review, summarize the customer's concern, draft a concise and empathetic management reply in the recorded Language, avoid unsupported claims or compensation promises, and recommend the internal follow-up required. Return an approval-ready workbench with counts by priority, Language, rating band, and inferred theme, plus the exact next action for the freelancer.

Local Customer Trust Scorecard

Using the Catchr MCP, create a Yelp Review customer-trust scorecard for [Business Account ID] over [Period] versus [Previous Period]. Report distinct Review ID count, average Rating, rating distribution, low-rating share based on [Low-Rating Threshold], trend by Review Date, Language mix, and reply coverage from Reply Text. Treat Average Rating and Review Count as possible account-level snapshots and do not sum repeated values unless their grain is verified. Infer the strongest customer-confidence signals and purchase or visit objections from Review Text, clearly label those themes as inferred, and quantify their frequency and associated average Rating. Conclude whether review-based trust signals are improving, identify three proof points the business may validate for marketing, and prioritize three reputation or customer-experience fixes with [Owner] and [Review-Based Success Target]; do not claim an effect on revenue or conversion without another data source.

Customer Experience Friction Finder

Using the Catchr MCP, analyze Yelp Review records for [Business Account ID] over [Period] and compare them with [Previous Period]. Use Review ID, Review Date, Rating, Review Text, Language, Reply Text, and Review URL to infer customer-experience themes such as product quality, service, wait time, fulfillment, returns, staff interactions, or value only when the text supports them, and label every theme as inferred. For each theme, report distinct review count, average Rating, low-rating share, change over time, and reply coverage, applying [Minimum Review Count] before ranking results. Prioritize the top three friction points by frequency, severity, and deterioration, cite representative Review URLs, and assign one concrete corrective action, [Owner], [Due Date], and measurable review-based outcome to each.

Review Recovery and Advocacy Planner

Using the Catchr MCP, pull Yelp Review data for [Business Account ID] from [Recent Period]. Split reviews into a recovery queue using [Recovery Rules, e.g., Rating at or below 3 or an empty Reply Text] and an advocacy queue using [Advocacy Rules, e.g., Rating at or above 4 and Review Text containing a specific proof point]. Use Review ID, Reviewer Name, Review Date, Rating, Review Text, Language, Reply Text, and Review URL to explain every selection. For recovery cases, draft an empathetic reply in the recorded Language and recommend the internal follow-up; for advocacy cases, identify inferred product or service strengths and a validation step before reuse. Return prioritized queues with [Owner] and [Target Date], and require human approval plus reviewer permission before republishing names, avatars, or review text.

Yelp Review Data Integrity Audit

Using the Catchr MCP, audit Yelp 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, Reviewer Avatar, Review Text, Language, Languages Review Count, Reply Text, Review URL, 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 Review Count values, malformed Review or avatar URLs, Extracted Date earlier than Review Date, unexpected Platform Name values, and conflicting records sharing the same 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 Yelp 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 Average Rating as possible account-level snapshots. Flag extraction lag above [Maximum Lag], missing calendar intervals, duplicate reviews, unexpected gaps in new-review volume against [Baseline Window], snapshot changes above [Change Threshold], and sudden drops in the completeness of Review Text, Language, Reply Text, or Review URL. Do not sum repeated snapshot values unless their grain is verified; distinguish definite pipeline failures from possible real reputation changes, then return an account-level health summary and a record-level investigation table with the next validation query for every anomaly.

Rating, Text, and Reply Consistency Validator

Using the Catchr MCP, retrieve Yelp Review records for [Account ID or All Connected Accounts] over [Period] using Review ID, Review Date, Rating, Review Text, Language, Reply Text, Review URL, and Extracted Date. Derive sentiment, theme, and urgency labels from Review Text, explicitly marking all three as inferred, then validate inferred sentiment against Rating using [Agreement Rules]. Flag missing or insufficient text, repeated text across different Review IDs, rating-text disagreement, unsupported Language values, replies populated for records that fail [Reply Eligibility Rules], and severe recent reviews with empty Reply Text. Return agreement and reply-coverage metrics by Account ID, Language, and rating band, 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 Yelp Review to ChatGPT

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How to connect Yelp Review to ChatGPT ?

Authorize the Yelp 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 Yelp Review prompts for analyzing real account data.

Yelp Review to ChatGPT FAQs

What Yelp Review data can ChatGPT analyze through Catchr?

How granular can Yelp Review analysis be in ChatGPT?

Can ChatGPT summarize recurring customer themes in Yelp Review?

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

What types of prompts work best for Yelp Review analysis?

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

Can I analyze multiple Yelp locations together?

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

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

Can ChatGPT change anything in Yelp 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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