# What Can I Ask My AI? Spiffy MCP gives your AI access to real commerce data: customers, orders, payments, subscriptions, payment plans, products, promo codes, affiliates and webhooks. When you combine Spiffy MCP with other tools, you can ask better business questions. Spiffy shows what happened commercially. Other tools add context from traffic, campaigns, support, CRM, email, analytics or product usage. Use these prompts to find patterns, explain performance and spot opportunities. ## Prompts by Category - [Revenue Insights](#revenue-insights) - [Conversion Insights](#conversion-insights) - [Customer & Retention Insights](#customer-amp-retention-insights) - [Marketing Insights](#marketing-insights) - [Affiliate Insights](#affiliate-insights) - [Product Insights](#product-insights) - [Support Insights](#support-insights) - [Payment Insights](#payment-insights) - [Integration Insights](#integration-insights) - [Good Insight Prompt Pattern](#good-insight-prompt-pattern) ## Revenue Insights ### Explain why revenue changed: Use Spiffy to compare successful payments from this week with last week. Then use analytics data to compare traffic, conversion rate and top landing pages over the same period. Tell me what most likely caused the revenue change. ### Find which channels generate actual revenue: Use Spiffy to find orders from the last 30 days, including products, totals and customer details. Then compare that with analytics or attribution data. Show which channels drove the most revenue, not just the most traffic. ### Find high-value customer patterns: Use Spiffy to find high-value customers from the last 90 days. Then compare them with CRM fields, acquisition source, company type or campaign data. Summarize what the best customers have in common. ## Conversion Insights ### Find where checkout performance is weak: Use Spiffy to find recent orders by checkout or product. Then compare with website analytics for the same checkout pages. Tell me which pages appear to have strong traffic but weak sales. ### Compare product interest with actual purchases: Use Spiffy to show orders by product from the last 30 days. Then compare with website analytics for product or sales pages. Tell me which products get attention but do not convert well. ### Find pricing friction: Use Spiffy to compare purchases across product prices or options. Then compare with analytics, support tickets or sales calls mentioning price. Tell me whether a specific price or plan seems to create friction. ## Customer & Retention Insights ### Find likely churn reasons: Use Spiffy to find recently cancelled subscriptions. Then compare those customers with support tickets, CRM notes and email history. Summarize the most common reasons customers appear to cancel. ### Identify customers at risk: Use Spiffy to find customers with failed payments, cancelled subscriptions or delayed payment plans. Then compare with support tickets and CRM notes. Show customers who may need proactive follow-up. ### Understand refund drivers: Use Spiffy to find refunded orders from the last 60 days. Then compare with support tickets, product purchased and customer notes. Summarize the most common refund patterns. ## Marketing Insights ### Measure promo code performance: Use Spiffy to find orders using promo ID `12345`. Then compare those customers with email campaign data, traffic source and repeat purchase activity. Tell me whether the promo brought in good customers or just discounted sales. ### Compare campaign engagement with revenue: Use Spiffy to find orders from customers who bought during the campaign period. Then compare with email campaign opens, clicks and audience segments. Tell me which campaign or segment produced the most revenue. ### Find win-back opportunities: Use Spiffy to find customers with cancelled subscriptions or old successful purchases but no recent orders. Then compare with email engagement and support history. Suggest which customers look worth targeting with a win-back offer. ## Affiliate Insights ### Find affiliates who drive quality customers: Use Spiffy to show affiliate-attributed orders from the last 90 days. Then compare those customers with refunds, failed payments, subscription status and repeat purchases. Tell me which affiliates appear to bring the highest-quality customers. ### Compare affiliate clicks with paid customers: Use Spiffy to show affiliate orders and commissions. Then compare with analytics or campaign data for affiliate traffic. Tell me which affiliates drive traffic that actually converts into paid orders. ### Investigate a disputed commission: Use Spiffy to show the affiliate, related order, customer and commission details. Then compare with CRM notes, analytics data and support history. Summarize whether the commission appears valid. ## Product Insights ### Find best-performing products: Use Spiffy to show recent orders by product. Then compare with website analytics, support tickets and refund data. Tell me which products perform best commercially and which create the most support burden. ### Find products with strong demand but poor retention: Use Spiffy to compare products by initial orders, subscription continuation, cancellations and refunds. Then compare with support tickets or customer feedback. Tell me which products sell well but may have retention issues. ### Find upgrade opportunities: Use Spiffy to find customers who bought lower-tier products or active subscriptions. Then compare with CRM notes, product usage or support history. Show customers who may be good candidates for an upgrade. ## Support Insights ### Find support issues that affect revenue: Use Spiffy to find failed payments, refunds and cancelled subscriptions from the last 30 days. Then compare with support tickets from the same customers. Tell me which support issues appear to be costing the most revenue. ### Spot repeated customer problems: Use Spiffy to find customers with multiple failed payments, refunds or subscription changes. Then compare with support history. Show patterns where customers repeatedly run into the same issue. ### Prioritize support work by revenue impact Use Spiffy to find open customer issues that involve high-value orders, active subscriptions, failed payments or payment plans. Then compare with support tickets. Tell me which tickets should be prioritized based on commercial impact. ## Payment Insights ### Understand failed payment patterns: Use Spiffy to find failed payments from the last 30 days. Then compare with payment processor details, country, currency, gateway and customer history. Summarize any patterns in failed payments. ### Find payment plans at risk: Use Spiffy to find active payment plans and recent failed or delayed payments. Then compare with support tickets and customer notes. Tell me which payment plans look most at risk. ### Compare refund behavior by product: Use Spiffy to find refunded payments and related products from the last 90 days. Then compare with support tickets and product pages. Tell me which products have the highest refund risk and why. ## Integration Insights ### Find broken automation patterns: Use Spiffy to show failed webhook events from the last 7 days. Then compare with app logs, integration logs and affected orders. Group the failures by likely cause and business impact. ### Understand missing access issues: Use Spiffy to find customers who paid successfully but contacted support about missing access. Then compare with membership platform access logs and webhook events. Tell me whether the issue is payment, fulfilment or integration related. ### Check whether integrations affect revenue: Use Spiffy to find orders, failed webhooks and refunds from the same period. Then compare with support tickets about access or fulfilment. Tell me whether integration problems appear to be causing refunds or cancellations. ## Good Insight Prompt Pattern Use this pattern when combining Spiffy MCP with other tools: Use Spiffy to find the commercial outcome → use another tool to find the surrounding context → compare both → explain the pattern. **Examples:** - Use Spiffy to find refunded orders from the last 60 days, then compare with support tickets and tell me the main refund reasons. - Use Spiffy to find affiliate-attributed customers, then compare with refunds and subscription status to show which affiliates bring the best customers. - Use Spiffy to find orders by product, then compare with website analytics to show which products get traffic but do not convert. - Use Spiffy to find cancelled subscriptions, then compare with CRM notes and support tickets to summarize likely churn reasons.