Detect Coupon Code Abuse and Receive Slack Alerts

This trigger checks your "Shopify Orders" MESA Data table every hour for new records using the schedule. When new order data is detected, the workflow activates and begins the discount analysis process. Critical requirement: You must have a MESA Data table named "Shopify Orders" that contains order information including "Created At", "Total Price", and "Total Discount" columns. This table is typically populated by another workflow that syncs Shopify orders to MESA Data storage.

This step runs a SQL query against your "Shopify Orders" table that groups orders by day and calculates the total sales, total discounts, and discount percentage for each day. The query uses TO_CHAR("Created At", 'yyyy-mm-dd') to group by date, SUM("Total Price") for daily sales totals, SUM("Total Discount") for daily discount amounts, and ROUND(SUM("Total Discount") / SUM("Total Price"), 2) to calculate the discount percentage. The results are returned as {{data_1}} and ordered chronologically by date, providing a time-series view of discount activity.

This custom JavaScript step processes the raw SQL query results from {{data_1}} and formats them into a structured JSON object stored as {{custom.json_results}}. The script organizes the daily order totals, discount amounts, and percentages into a format optimized for AI analysis. This formatting step ensures the data is properly structured before being sent to the AI prompt, preventing parsing errors and improving analysis accuracy. Custom code: const Mesa = require('vendor/Mesa.js'); /** * A MESA Script exports a class with a script() method. */ module.exports = new (class { /** * MESA Script * * @param {object} prevResponse The response from the previous step * @param {object} context Additional context about this task */ script = (prevResponse, context) => { // Retrieve the Variables Available to this step const vars = context.steps; let newPayload = {}; newPayload.json_results = JSON.stringify(prevResponse); // Call the next step in this workflow // response will be the Variables Available from this step Mesa.output.next(newPayload); }; })();

This step sends the formatted discount data from {{custom.json_results}} to AI with a prompt asking it to compare the discount rate against the usual 10% average and determine if abuse is occurring. The AI analyzes the daily discount percentages to identify unusual spikes or sustained high discount rates that indicate potential coupon code exploitation. The AI responds with a simple "Yes" or "No" stored as {{ai.response}}, making it easy to trigger alerts. Customization option: You can modify the prompt to change the baseline percentage (currently 10%) to match your typical discount rate, or add specific rules for what constitutes abuse in your business.

This filter step checks if {{ai.response}} equals "Yes", meaning the AI detected abnormal discount rates. If the condition is true, the workflow proceeds to send a Slack alert. If the AI response is "No" (normal discount activity), the workflow stops here without sending any notification. This ensures you only receive alerts when there's actually something worth investigating.

This step sends a Slack message to your specified channel when the filter determines abuse has been detected. You need to configure two fields: the channel where the alert should be sent (you must invite the MESA Slack app to your channel first by typing @MESA and clicking Invite), and the message content (default is "Coupon code abuse detected" but you can customize it to include more context). Best practice: Include actionable information in your message like instructions to review the discount report or check recent high-discount orders.