Describe client-side events and properties to track Export to CSV usage

WHAT THIS TESTS: This question evaluates whether you can design a minimal but complete client-side instrumentation plan for an asynchronous user action. The interviewer cares about event taxonomy, property selection, and understanding the difference between intent signals and outcome signals. You are expected to think like a product engineer who enables data-driven decisions without over-instrumenting or leaking sensitive data.

A GOOD ANSWER COVERS: First, name three distinct custom events that map to the user journey. Suggest export_csv_click fired immediately when the user presses the button, export_csv_success fired after the browser receives the complete file response or blob, and export_csv_failure fired when the request times out, returns a non-200 status, or the generation logic throws. Second, list contextual properties attached to every event such as user_id, session_id, button_location, and timestamp. Third, add outcome-specific properties: on success include file_size_bytes, row_count, and latency_ms measured from click to download start; on failure include error_code, error_message truncated to a safe length, and failure_stage like generation versus download. Fourth, mention that these are custom events sent via gtag or an equivalent analytics SDK, and that you should avoid sending personally identifiable information or large payloads.

COMMON WRONG ANSWERS: A red flag is proposing a single click event and assuming the server log will tell the rest. Another mistake is firing the success event when the request is dispatched rather than when the file is actually received, which inflates success rates if the connection drops. Some candidates suggest tracking every mouse movement or hover near the button, which shows poor judgment about signal-to-noise ratio. Finally, omitting failure instrumentation entirely makes it impossible for the product manager to spot a broken export flow.

LIKELY FOLLOW-UPS: The interviewer may ask how you would calculate an end-to-end conversion funnel from click to success, how you would sample events if traffic is enormous, or how you would handle retries that generate multiple failure events for one user intent. They might also ask whether you would use Google Analytics recommended events versus custom events for this use case, or how you would validate that the instrumentation is correct before launch.

ONE CONCRETE EXAMPLE: Imagine a reporting dashboard with an Export to CSV button in the top-right toolbar. When the user clicks it, the client fires export_csv_click with properties button_location set to toolbar_top_right and report_type set to sales_summary. The frontend posts to an API, starts a timer, and shows a loading spinner. If the API returns a 200 response with the blob after 800 milliseconds, the client fires export_csv_success with latency_ms set to 800 and file_size_bytes set to the blob length. If the API returns a 500 error or the user is offline, the client fires export_csv_failure with error_code set to 500 or network_error and failure_stage set to api_response. The product manager can now build a funnel and diagnose whether exports fail during generation or delivery.

Source: developers.google.com