How can I gain insights from customer chats?

Conversation‑tags let you organise chats by topic as they happen. Chatref can auto‑tag based on the content of each chat, and you can add custom tags manually – for example, “failed payment”, “KYC rejection”, “app navigation”, or “missing statement”. For a neobank, this means every time a user asks about an instant transfer delay, that conversation gets tagged, and you instantly see how often that problem surfaces. Tags stay attached in the shared inbox, so your support team always knows what the chat is really about before they even open it.

The insights feature synthesises all those tagged conversations into clear, actionable reports. It uses an LLM to detect emerging themes – say, a sudden spike in “card not added” chats after a mobile app update – and sends digest emails on a schedule you set. Instead of scanning hundreds of chat logs, you get a regular summary: top question categories, rising pain points, and even suggestions on which gaps to fix next. That makes customer chat analytics a continuous feedback loop rather than a one‑off audit.

The shared inbox connects your live agents directly to every AI‑handled chat. When a conversation needs a person – for example, a sensitive fraud dispute – an agent can take over the same thread with full context and tags visible. The human notes and resolutions become part of the insight data, too. In a neobank, the shared inbox brings operations, compliance, and product teams into the same view, turning isolated support chats into a centralised source of neobank customer feedback.

Insights from support chats mean nothing if they stay in a dashboard. With Chatref, each digest becomes a decision‑making tool. A recurring tag like “inactivity fee confusion” tells you to update your help centre and maybe tweak the UI. A sudden jump in “unable to verify identity” flags a potential KYC flow defect. By closing this feedback loop – tag, analyse, act – you shorten the time between a customer’s frustration and a product fix, and you measurably reduce repeat tickets.

What kind of insights can I expect from customer chats?

You’ll see the top question themes (e.g., “transfer delays”, “account closure”), frequency trends over time, and new topics that appear after product changes. Chatref’s AI‑generated digest emails include an LLM summary highlighting emerging issues – for instance, a spike in “where’s my referral bonus?” – so you know exactly what customers are asking about and can prioritise fixes.

How do I set up chat analytics?

Chat analytics are built into every Chatref agent. Once your widget is live, conversation‑tags start auto‑categorising chats by topic; you can also add manual tags from the shared inbox. To receive insights, go to the Insights tab in your dashboard and configure how often you want digest emails. No additional integration or coding is required.

Can I track customer satisfaction through the chat widget?

Chatref doesn’t include a built‑in CSAT survey or star rating in the widget. However, you can manually tag conversations as positive or negative to build a satisfaction picture over time, and use custom actions to link to an external survey after a chat. For most teams, the strongest satisfaction proxy is the trend data from insights – a drop in “problem resolved” tags or a rise in a specific complaint theme often tells you more than a standalone score.