> ## Documentation Index > Fetch the complete documentation index at: https://docs.mixpanel.com/llms.txt > Use this file to discover all available pages before exploring further. # MCP for Finance: Use Cases and Sample Prompts Financial services teams often have behavioral and business data — but they live in separate systems. The Mixpanel MCP server lets you query across both at once, so you can connect what users did in your product to what that means for revenue, risk, and compliance. ## Use Cases **New to MCP?** Start with [Explore Data with AI](/guides/guides-by-use-case/empower-your-team/mcp) for setup instructions and foundational concepts before diving into industry-specific use cases. Each use case below shows a cross-system question your team can ask, the data sources it draws from, and what you can do with the answer. ### Account Opening Funnel × CRM Pipeline **The question**: Which high-value prospects are dropping off during account opening, and what's their Salesforce lead score? | Data source | What you're pulling | | ----------- | ------------------------- | | Mixpanel | Funnel events | | Salesforce | Lead and opportunity data | When drop-off concentrates among high-AUM prospects, the problem isn't just a UX issue — it's a revenue problem. Combining funnel data with lead scores tells you where to focus first. ### Feature Adoption × Transaction Revenue **The question**: Do users who adopt mobile check deposit generate more monthly transaction volume than those who don't? | Data source | What you're pulling | | -------------------------------- | -------------------- | | Mixpanel | Feature usage events | | Payment processor / core banking | Transaction data | This is how you move from "people use this feature" to "this feature drives revenue." Use the answer to prioritize your roadmap around features with measurable financial impact — not just high engagement. **Pro tip**: Run this analysis across several features at once to build a clearer picture of which behaviors actually correlate with transaction volume. One feature rarely tells the whole story. ### Session Behavior × Fraud Signals **The question**: Are there behavioral patterns in Mixpanel session data that correlate with flagged fraud cases? | Data source | What you're pulling | | ---------------------- | ------------------------------- | | Mixpanel | Session replay, event sequences | | Fraud detection system | Flagged accounts | Traditional fraud detection flags transactions after the fact. Behavioral patterns — unusual navigation sequences, rapid form completion, atypical device switching — can show up earlier. This join gives your risk team a behavioral layer to work with alongside transaction signals. **Pitfall**: Behavioral patterns are correlational, not causal. Use them to triage and investigate, not to automatically flag or restrict accounts. ### Onboarding Completion × KYC Status **The question**: What's the completion rate for users stuck in KYC verification, and how does it vary by document type? | Data source | What you're pulling | | ----------------------- | ------------------- | | Mixpanel | Onboarding funnel | | KYC / compliance system | Verification status | KYC is one of the most common places onboarding falls apart in regulated products — and it's one of the hardest to instrument well. This join shows you exactly which document types or verification steps are driving abandonment, so you can address friction without compromising compliance requirements. ## Sample Prompts by Role These are starting points. Adjust the time ranges, segments, and metrics to match your product and data. * Show me the account opening funnel with conversion rates at each step, broken down by account type. * What's the 30-day retention for users who completed their first trade in week 1 vs. those who didn't? * Which features have the highest adoption rate among users who signed up in the past 60 days? * Compare onboarding completion for users acquired through paid ads vs. organic referral. * What's the average time from signup to first deposit, and how has that trended over 6 months? * Show me the top 5 events performed by users who are still active at Day 90. * Which onboarding step has the highest drop-off, and has it changed since our last release? * What's the funnel from app download to account created to KYC completed to first funded, by platform? * How does feature usage differ between users in the \$0–1K balance tier vs. \$10K+ tier? * What percentage of users who activated push notifications are retained at Day 30? * Pull the weekly trend of daily active users segmented by account type for the past 12 months. * What's the frequency distribution for login events — how many times per week do active users log in? * Run a retention analysis for the January 2026 signup cohort, broken down by acquisition channel. * Which user properties are most predictive of 90-day retention? Compare top decile vs. churned. * Show me event counts for all transaction-related events over the past quarter, grouped by week. * What are the top data quality issues in our project right now? * Compare segmentation of users by region and account type — where are our highest-engagement cohorts? * What's the correlation between number of features adopted in the first 14 days and 6-month retention? * Pull property values for `acquisition_channel` and show distribution of signups across channels for Q1. * List all events containing "transfer" and show their 30-day usage trends. * What's our signup-to-funded-account conversion rate by UTM campaign over the past 90 days? * Which acquisition channels have the highest Day 7 retention (not just signups, but users who came back)? * Show me the weekly new user trend overlaid with our activation rate. * How does the referral program funnel perform compared to paid acquisition? * What's the reactivation rate for users who received the win-back campaign last month? * Which landing page leads to the highest rate of account funding? * Compare behavior of users who signed up during our holiday promotion vs. non-promotional periods. * What's the cost-per-funded-account factoring in drop-off between signup and first deposit? * Show me top 3 acquisition channels by volume and their 30/60/90 day retention curves. * Which user segments have the highest cross-sell potential into investment products? * Which enterprise accounts have seen a decline in weekly active users over the past 30 days? * What's the health score breakdown for accounts in the \$50K+ ACV tier? * Show me the top support-related events (password resets, failed transactions) and their weekly trend. * Which accounts have users who haven't logged in for 14+ days? * What's the feature adoption gap between our most engaged and least engaged accounts? * Pull the usage summary for \[Account Name], including features used and activity trend this quarter. * Are there accounts where usage dropped below historical average by more than 25%? * Which newly onboarded accounts from the past 60 days haven't hit activation milestones? * Show me the retention curve for accounts managed by each CSM — are there patterns? * What's the satisfaction signal trend for our top-tier accounts? * Snapshot: total active users, 7-day retention, signup-to-activation rate (this week vs. last week vs. last month) * What's our overall funnel health — end-to-end from first visit to funded account with week-over-week trend? * Which product area is driving the most engagement growth this quarter? * How does mobile vs. desktop compare in terms of activation and retention? * What's the leading indicator for churn — which behavioral signal shows up first? ## Recommended Data Connections | Source | What it adds | | -------------------- | --------------------------------- | | Salesforce | CRM and pipeline context | | Stripe | Payment and transaction data | | Slack | Alert teams on key signals | | Google Sheets | Compliance tracking and reporting | | Snowflake / BigQuery | Data warehouse joins | ## Key Takeaways * Connect Mixpanel funnel data with CRM records to identify where high-value prospects drop off — not just where drop-off is highest. * Feature adoption means more when you can tie it to transaction volume or revenue outcomes, not just engagement metrics. * Behavioral session data can complement fraud detection systems, giving risk teams earlier signals to investigate. * KYC and compliance steps are often the highest-friction points in onboarding — instrument them carefully and join with verification status to find what's actually causing abandonment. * The MCP server handles the cross-system query; the value comes from knowing which question to ask. 👉 **Next step**: See the [MCP by Industry](/guides/guides-by-use-case/empower-your-team/mcp/mcp-by-industry) page for other industry guides, or visit [MCP Integration Pairings](/guides/guides-by-use-case/empower-your-team/mcp/integrations) to explore what each data connection unlocks.