Connect Braze to Claude

Multi-Client Braze Health Radar

Using the Catchr MCP, analyze Braze for [List of Client Accounts] over [Reporting Period] against [Comparison Period]. For each account, review Revenue, Conversions, and Sent and break the results down by Campaign Name Summary and Campaign Detail Channel. Focus on lead-source quality and lifecycle progression. Rank accounts as Healthy, Watch, or Critical using [Client Targets or Historical Baseline], and show the evidence behind each status. Return a client-ready table with the account, main change, supporting fields, business implication, recommended next action, and one sentence for the next client meeting. Separate observed changes from possible explanations, respect the reporting grain, and do not claim causation when Braze only shows correlation.

Client-Ready Braze Performance Brief

Using the Catchr MCP, review Braze for [Client Account] during [Reporting Period] versus [Comparison Period]. Review Sent, Delivered, and Opens and break the results down by Canvas Name Summary and Step Name. Explain what improved, what declined, and which changes are material for leads, lifecycle stages, pipeline, deals, owners, sources, and revenue. Produce an executive summary, three evidence-backed wins, three risks, and a prioritized action plan with owner and review date. Include a compact evidence table that ties every conclusion to the returned fields. State any unavailable field, incompatible reporting level, or small-sample limitation before recommending action, and write the final recap in language an agency account manager can use with the client.

Braze Opportunity and Risk Map

Using the Catchr MCP, compare Braze across [List of Client Accounts] for [Period] using [Materiality Threshold] and [Client-Specific Targets]. For every account, review Opens, Unique Clicks, and Unique Recipients and break the results down by Segment Name Summary and Event Name. Investigate pipeline coverage, stage conversion, and deal risk and owner performance, handoff delays, and revenue concentration. Build an opportunity-and-risk map ranked by business relevance, confidence, and urgency. For each finding, show the account, observed change, supporting fields, plausible explanations, first validation step, and recommended client action. Keep each client separate before creating a portfolio summary, and avoid applying one account's baseline, currency, attribution rule, or reporting definition to another.

Morning Braze Priority Queue

Using the Catchr MCP, pull Braze for [List of Client Accounts] over [Recent Period] and compare it with [Baseline Period]. For each client, review Unique Recipients, Daily Active Users, and Conversions 1 and break the results down by Campaign Tag Value and Date. Flag changes beyond [Alert Threshold], separate high-value issues from low-volume noise, and rank the work I should handle today. Return a queue with the client, issue, exact evidence, likely impact stated cautiously, first validation step, recommended action, and a short update I can send to the client. Do not merge accounts until each result has been checked against its own target, currency, timezone, and reporting grain.

Monthly Braze Client Report

Using the Catchr MCP, create a monthly Braze report for [Client Account] covering [Current Period] versus [Previous Period]. Review Conversions 1, Conversions 1 By Send Time, and Conversions 2 and break the results down by Campaign Updated At and Canvas Updated At. Explain the most important movement in leads, lifecycle stages, pipeline, deals, owners, sources, and revenue, including what stayed stable and what needs attention. Write a plain-English report with an executive summary, a concise evidence table, three wins, three concerns, and three actions for [Next Period]. Attach an owner and success measure to each action. State the latest available date and any missing fields or intervals so the client can distinguish measured facts from assumptions or incomplete coverage.

Braze Optimization Backlog

Using the Catchr MCP, analyze Braze for [Client Account] over [Lookback Period]. Review Conversions 2, Conversions 2 By Send Time, and Conversions 3 and break the results down by Segment Updated At and Campaign Conversion Position. Identify the largest opportunities related to lead-source quality and lifecycle progression and owner performance, handoff delays, and revenue concentration, then create an optimization backlog ranked by expected impact, effort, and confidence. For every task, include the observed evidence, a specific action, an owner, a due date, a success metric, and a guardrail that would stop the test. Use [Minimum Volume] and [Materiality Threshold] to avoid reacting to noise, and label any recommendation that requires a field or business definition not available in the returned dataset.

Braze Lead Source and Lifecycle Review

Using the Catchr MCP, analyze Braze for [Account or Entity] over [Reporting Period] versus [Comparison Period]. Review Conversions 3, Conversions 3 By Send Time, and Conversions By Entry Time and break the results down by Campaign Description and Behavior Type. Concentrate on lead-source quality and lifecycle progression. Identify the entities and periods driving the largest change, distinguish material movement from normal variation using [Minimum Volume] and [Business Target], and explain what the available fields can and cannot prove. Return a ranked findings table with evidence, business implication, confidence, and recommended next action, followed by a short decision summary. Keep calculations at a compatible reporting grain and do not invent a metric when Braze exposes only a related signal.

Braze Pipeline and Deal Risk Analysis

Using the Catchr MCP, investigate Braze for [Account or Entity] across [Period] with a focus on pipeline coverage, stage conversion, and deal risk. Review Conversions By Entry Time, Conversions By Send Time, and Sent To Carrier and break the results down by Window Days and Campaign Detail Tag Value. Compare relevant entities against [Target, Peer Group, or Historical Baseline], then locate the strongest opportunities and clearest deterioration. For every finding, show the returned values, comparison dimension, possible explanations, and next check required before action. Deliver a prioritized opportunity map with owner, action, expected signal of improvement, and review date. Treat associations as hypotheses unless the available Braze fields establish a direct relationship.

Braze Owner and Handoff Performance Map

Using the Catchr MCP, review Braze for [Account or Entity] over [Lookback Period] to assess owner performance, handoff delays, and revenue concentration. Review Sent To Carrier, Unique Opens, and Clicks and break the results down by Canvas Detail Tag Value and Segment Detail Tag Value. Flag changes outside [Tolerance], rank them by materiality and confidence, and separate confirmed data observations from possible operational causes. Return an exception table with the affected entity, period, supporting fields, severity, owner, first validation step, and recommended response. End with three repeatable monitoring rules, including the threshold, minimum volume, and required field. Do not present an unusual value as proof of an error or business cause.

Braze Data Quality Audit

Using the Catchr MCP, audit Braze for [Account or All Connected Accounts] over [Period]. Review Clicks, Campaign Message Channel, and Count and break the results down by Campaign Id and Is Api Campaign. Check completeness, unexpected nulls, duplicate identifiers at [Expected Grain], invalid values, incompatible aggregation levels, and date consistency where the returned fields support those tests. Return rule-level failure counts and a severity-ranked exception sample with account, field, observed value, expected condition, and reproducible validation step. Distinguish source-data issues from extraction or modeling issues, and do not infer uniqueness, join keys, or business meaning unless confirmed in [Data Contract or Source Documentation].

Braze Freshness and Coverage Monitor

Using the Catchr MCP, monitor Braze delivery for [Account or All Connected Accounts] across [Lookback Period]. Review Count, Delivery Failed, and Enrolled and break the results down by Campaign Last Edited and Campaign Archived. Measure date coverage, latest available record, row or entity volume, missing intervals, and field population against [Expected Schedule] and [Freshness SLA]. Flag stale extraction, flatlined volume, schema coverage changes, and gaps beyond [Absolute and Relative Thresholds]. Return an account-level health summary plus an investigation table with first affected period, supporting fields, likely failure domain stated cautiously, owner, and next validation query. Separate genuine source inactivity from a pipeline issue whenever the evidence cannot distinguish them.

Braze Drift and Reconciliation Review

Using the Catchr MCP, compare Braze across [Current Window] and [Baseline Window] for [Account or All Connected Accounts]. Review Enrolled, Entries, and First Button Clicks and break the results down by Campaign Created At and Campaign Draft. Profile distributions, null rates, distinct-value counts, volume, and material changes at the declared reporting grain. Reconcile related fields only when their definitions and aggregation levels are compatible. Flag abrupt shifts beyond [Drift Threshold], inconsistent totals, and values that violate [Business or Data Contract Rules]. Produce a reproducible alert table with evidence, severity, probable domain, and next check, followed by a concise assessment of which analyses may be unreliable until each issue is resolved.

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

Connect Braze to Claude

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How to connect Braze to Claude ?

Authorize the Braze account in Catchr

Add the Catchr MCP as a custom connector in Claude

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 Braze prompts for analyzing real account data.

Braze to Claude FAQs

What Braze data can Claude analyze through Catchr?

How granular can Braze analysis be in Claude?

Can Claude compare Braze performance across periods or accounts?

Can Claude investigate unusual changes in Braze?

What types of prompts work best for Braze analysis in Claude?

Can I combine Braze with other data sources in Claude?

Can Claude compare multiple Braze accounts or entities?

What do I need to connect Braze to Claude with Catchr?

How recent is the Braze data, and how much history can Claude analyze?

Can Claude change anything in Braze through Catchr?

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

Can I try the Claude integration before choosing a plan?

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