Connect Chartmogul to ChatGPT

Multi-Client Subscription Health Scan

Using the Catchr MCP, pull Chartmogul data for [List of Client Accounts] over [Period, e.g., month to date] and compare each account with [Comparison Period]. For every client, report MRR, ARR, Customer Count, ARPA, Customer Churn Rate, and MRR Churn Rate; calculate the period-over-period change for each metric and assess it against [Client Targets or Historical Baseline]. Label each account Healthy, Watch, or Critical, rank the accounts requiring the consultant's attention, and give the measurable reason plus one concrete next action for every Watch or Critical account.

Client Recurring Revenue Executive Brief

Using the Catchr MCP, analyze Chartmogul data for [Client Account] over [Reporting Period] versus [Previous Period]. Summarize MRR, ARR, Customer Count, ARPA, LTV, ASP, Customer Churn Rate, and MRR Churn Rate, then break material movements down by Customer Country, Customer Status, Customer Billing System Type, and Customer Currency where volume is sufficient. Produce a client-ready executive brief with an opening business outcome, three wins, three risks, the exact evidence behind each statement, and prioritized actions for retention, pricing, or customer expansion; distinguish observed relationships from proven causes and mark unavailable values clearly.

Cross-Client Churn Risk Watchlist

Using the Catchr MCP, review Chartmogul retention performance for [List of Client Accounts] over [Lookback Period] against [Comparison Period]. Use Customer Churn Rate, MRR Churn Rate, Customer Count, MRR, and ARR to identify clients with accelerating logo or revenue churn. For each flagged account, use Customer Name, Customer Company, Customer Status, Customer MRR, Customer ARR, Customer Since, Country, and Billing System Type to identify the highest-value customer records associated with the risk, without exposing Customer Email in the output. Apply [Materiality and Privacy Thresholds], rank risks by recurring revenue impact, and provide a client-safe explanation, the next diagnostic check, and one retention action.

Morning SaaS Client Priority Queue

Using the Catchr MCP, pull month-to-date Chartmogul performance for [List of Client Accounts] and compare each account with [Previous-Month-to-Date Period] and [Client KPI Targets]. For each client, summarize MRR, ARR, Customer Count, ARPA, Customer Churn Rate, and MRR Churn Rate; calculate pacing and period-over-period changes. Rank the clients I should work on today according to [Urgency Thresholds], explain the single largest measurable issue for each flagged account, and give one specific first action plus the Chartmogul metric to recheck after the action.

Monthly Subscription Client Report

Using the Catchr MCP, create a client-ready Chartmogul report for [Client Account] covering [Current Month] versus [Previous Month]. Report MRR, ARR, Customer Count, ARPA, ASP, LTV, Customer Churn Rate, and MRR Churn Rate, and explain material changes using Customer Status, Customer Country, Customer Currency, and Customer Billing System Type where supported by the data. Write in plain English with an executive summary, what improved, what declined, evidence for each conclusion, and the three priorities for [Next Month]. Do not claim causation from correlation, disclose any missing metric, and keep customer-level personal data out of the report.

Client Churn Recovery Backlog

Using the Catchr MCP, analyze Chartmogul data for [Client Account] over [Lookback Period]. Compare Customer Churn Rate and MRR Churn Rate with [Baseline Period], then use Customer Status, Customer MRR, Customer ARR, Customer Since, Customer Country, and Customer Billing System Type to quantify where the greatest recurring-revenue exposure sits. Apply [Minimum Revenue and Volume Thresholds], separate broad segment patterns from individual high-value records, and create a freelancer-friendly backlog ranked by expected recurring-revenue impact and effort. For every task, specify the data-backed rationale, exact retention action, owner, due date placeholder, and metric to monitor.

Online Subscription Growth Scorecard

Using the Catchr MCP, pull Chartmogul data for [Online Subscription Business Account] over [Period] and compare it with [Previous Period and Growth Targets]. Report MRR, ARR, Customer Count, ARPA, ASP, LTV, Customer Churn Rate, and MRR Churn Rate, with period-over-period changes and target gaps. Diagnose whether recurring-revenue growth is being constrained primarily by customer volume, revenue per account, or churn, based only on the observed metrics. Return a concise scorecard with the three largest opportunities and one measurable pricing, retention, or customer-value experiment for each, including [Success Threshold] and [Review Date].

Trial-to-Paid Cohort Opportunity Map

Using the Catchr MCP, analyze Chartmogul customer records for [Account] as of [Analysis Date]. Build cohorts from Customer Free Trial Started At and Customer Customer Since, then segment them by Customer Status, Customer Country, Customer Currency, and Customer Billing System Type. Calculate trial-to-customer elapsed time and the share of records reaching each observed status for cohorts old enough to evaluate, using [Minimum Cohort Age] and [Minimum Cohort Size]. Rank the strongest and weakest cohorts by paid-customer progression and Customer MRR or Customer ARR, clearly state that status-based progression is a proxy rather than a native conversion metric, and recommend three onboarding or market tests with a success metric.

High-Value Subscriber Retention Queue

Using the Catchr MCP, pull Chartmogul customer data for [Account] over [Current Snapshot or Period] and compare it with [Previous Snapshot or Period]. Use Customer Name, Customer Company, Customer Status, Customer MRR, Customer ARR, Customer Since, Customer Country, and Customer Billing System Type to identify high-value customers whose status or recurring revenue warrants attention. Prioritize records using [MRR/ARR Threshold] and [Status Rules], summarize total recurring revenue represented by each risk tier, exclude Customer Email and billing URLs from the output, and produce a retention queue with the evidence, a recommended owner action, and a follow-up date for each customer.

Recurring Revenue Data Integrity Audit

Using the Catchr MCP, audit Chartmogul data for [Account or All Connected Accounts] over [Period]. At Date or Year Month level, check completeness and non-negative values for MRR, ARR, Customer Count, ARPA, ASP, and LTV; verify Customer Churn Rate and MRR Churn Rate fall within [Expected Percentage Bounds]; and test whether ARR is consistent with annualized MRR within [Tolerance]. At customer level, flag duplicate Customer UUID or Customer ID values, missing External IDs, missing Customer Status, impossible Customer Since or Free Trial Started At dates, and non-positive Customer MRR or Customer ARR where [Status Expected to Be Active]. Return a reproducible exception table with account, date or customer identifier, failed rule, observed value, severity, and exact validation step.

MRR and Churn Outlier Monitor

Using the Catchr MCP, retrieve Chartmogul data for [Account or All Connected Accounts] over [Lookback Period, e.g., 24 months], grouped by Date or Year Month. Monitor MRR, ARR, Customer Count, ARPA, LTV, Customer Churn Rate, and MRR Churn Rate against a trailing [Baseline Window, e.g., 6 months]. Flag changes beyond both [Statistical Threshold, e.g., 2 standard deviations] and [Materiality Threshold, e.g., 20%], then localize each anomaly by Customer Status, Country, Currency, Billing System Type, or Data Source UUID where possible. Classify each case as likely data-quality issue, segment mix shift, or genuine business movement, show the evidence and confidence, and specify the next query needed to verify the classification.

Billing Source Revenue Reconciliation

Using the Catchr MCP, reconcile Chartmogul recurring-revenue data for [Account] as of [Snapshot Date]. Aggregate Customer MRR, Customer ARR, and distinct Customer UUID counts by Customer Data Source UUID, Customer Billing System Type, Customer Currency, and Customer Status, then compare the customer-level totals with account-level MRR, ARR, and Customer Count using [Tolerance]. Do not combine or convert currencies unless [Approved FX Table and Date] are supplied. Flag duplicate customer identifiers, missing source mappings, conflicting Billing System Type values, inactive customers with material recurring revenue, and unexplained reconciliation gaps. Return an issue table with source, currency, status, variance, likely pipeline layer, severity, and recommended remediation check.

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

Connect Chartmogul to ChatGPT

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How to connect Chartmogul to ChatGPT ?

Authorize the Chartmogul 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 Chartmogul prompts for analyzing real account data.

Chartmogul to ChatGPT FAQs

What Chartmogul data can ChatGPT analyze through Catchr?

How granular can Chartmogul analysis be in ChatGPT?

Can ChatGPT analyze subscription growth and retention in ChartMogul?

Can ChatGPT diagnose MRR movement and churn in ChartMogul?

What types of prompts work best for Chartmogul analysis?

Can I combine Chartmogul with other data sources in ChatGPT?

Can I analyze multiple ChartMogul accounts or customer segments together?

What do I need to connect Chartmogul to ChatGPT with Catchr?

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

Can ChatGPT change anything in Chartmogul 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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