WhatsApp Customer Support Automation

We begin by analysing your support requests. We categorise a sample of recent chats, emails and calls, and measure which requests are most frequent and which depend on information you hold in a system. From this we pick the first use cases, usually those that are high volume, low risk and data-driven, such as order status, appointment confirmation or ticket status. Next we review your systems: the order management or e-commerce platform, help desk, CRM or booking tool, and whether they offer an API or webhooks. If they do not, we explore alternatives such as a spreadsheet or lightweight database, or plan an integration project. We then design each automated journey. For order status, the bot asks for an order number, verifies it against the phone number, looks up the status and replies with the stage, expected date and tracking link, then asks whether the customer needs anything else. For tickets, it collects a short description, category and optional photo, creates a ticket in your help desk, replies with a reference number and later sends status updates through approved templates. For feedback, it sends a short rating request after closure and flags low scores to a manager. For each journey we write messages in a warm, plain style and define failure cases: no record found, system down, customer unsure, repeated misunderstanding. In all of these the customer is offered a person. We set up the escalation path: the bot passes the conversation to the shared inbox with a summary of what has been asked, the customer data and the ticket link, so the customer does not repeat themselves. We use the WhatsApp Business Platform through a provider that fits your volume, and connect the bot using an automation tool or custom code, depending on complexity. We prepare the message templates for notifications that go outside the service window, following approved template message setup. We test with real scenarios, including wrong order numbers, duplicate messages and attempts to view someone else’s data. We run a soft launch with a small share of customers, review the transcripts, and then widen. After launch we report on automated resolution, escalations, wait times and customer ratings, and expand to new use cases. For AI-driven conversation on your website, see customer support chatbot. We cannot promise a fixed percentage of tickets deflected, since customer needs vary, but automating data-based requests reduces repetitive work and shortens waiting for simple answers.