Thousands of customer requests a day, a support team that could not scale with the catalogue, and customers in time zones the team never covered. That was the starting point of one of our e-commerce deployments. Six months later the numbers were clear: response time down 85%, customer satisfaction up 40%, support costs down 60% and interactions handled up 300%.
Numbers like these are only interesting if you know what produced them.
It was not a generic bot
The assistant was trained on the merchant's own product data, policies and tone. It answered product questions and gave recommendations based on purchase history, tracked orders in real time and proactively warned about delays. Returns and complaints ran through automated flows instead of a queue.
Integration did most of the work
The model was the smaller part. The larger part was connecting the catalogue, the order system, the shipping carriers and the help desk, so the assistant could act rather than just talk. If it had only answered FAQs, the cost figure would not have moved.
Humans stayed in the loop
Complex cases escalate to a person with the full context attached. The team stopped answering "where is my order?" two hundred times a day and started handling the cases that deserve a human.
What we would check first for your store
- Do you have clean product and order data with an API?
- Which three questions make up most of your tickets?
- What is your current response time, measured, not guessed?
Bring those answers to a twenty-minute call and we will tell you honestly whether a chatbot is your first project or your third.