Conversation Analytics

We start by agreeing the questions you want answered. Are customers finding what they need? Is the bot reducing staff workload? Which topics should become new answers? Where do hot leads slip away? These questions shape what we measure. We then check what data exists. Most chatbot platforms store messages, timestamps, menu choices, fallback events and hand-off flags, and some allow exports. We confirm that you are allowed to analyse the chats, that your privacy notice mentions the bot and that the data is held securely; see opt-in and opt-out management for consent practices. We extract a period of chats, such as the last two to three months, and clean it: removing test messages, merging duplicate sessions, masking personal details and tagging language. Next we measure flow. We build a funnel of each journey, for example greeting, menu choice, question answered, lead form completed and handed to sales, and calculate completion and drop-off at each step. We review average conversation length, time to first answer, repeat contacts and the share of conversations that end without a clear outcome. We analyse content. We group what customers typed into topics, using manual reading supported by text-grouping tools, and count each topic. We list intents such as pricing, availability, location, returns, appointments, complaints and delivery status. We study fallbacks: messages the bot did not recognise, with examples, so that you can see the real words customers use, including Hinglish spellings and short forms. We review hand-offs to see why a person was needed, whether the reason was a missing answer, a complex case, a complaint or an explicit request, and how long customers waited. We read a sample of full conversations to catch problems numbers miss, such as confusing wording, repeated loops or tone issues. We connect to outcomes by matching chats with CRM leads, bookings and orders; see lead conversion analysis. This shows which flows bring value and which only create traffic. We then write recommendations in priority order: add or reword answers, shorten a menu, change the first message, add a quick-reply button, train staff on a frequent hand-off or retire an unused flow. For each, we estimate effort and expected benefit without promising results. We help you carry out changes in the bot; see customer support automation. We repeat the analysis after changes and monthly thereafter, comparing with earlier periods. We cannot see conversations that happen outside the platform, and small samples can mislead, but regular review makes a bot more useful each month.