How to Identify Ecommerce Process Bottlenecks with Python

How to Identify Ecommerce Process Bottlenecks with Python If you're running a small to mid-sized ecommerce business, you know that order delays and stockouts can silently destroy your bottom line. Without clear data on where your operations are failing, it’s easy to waste time chasing symptoms instead of fixing root causes. Identifying these issues manually is slow, error-prone, and often misses the bigger picture. The Manual Way (And Why It Breaks) Most developers and business owners try to spot bottlenecks by exporting data from platforms like Shopify or WooCommerce, then manually comparing spreadsheets or scanning through API results. They copy-paste order dates, match them with shipment times, and count refund requests. This approach works for tiny operations, but it quickly becomes unwieldy. You hit API rate limits, forget to filter for specific timeframes, and lose hours to repeated data entry tasks. The result? Inaccurate insights and wasted effort—especially when you have dozen