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        "title": "Website Chatbot",
        "slug": "chatbot",
        "source_url": "https://samriticreative.com/chatbot/",
        "modified": "2026-08-25T16:10:01",
        "excerpt_text": "I designed the storefront chatbot end to end - merchant setup, the widget, feedback, and the Service Cloud conversation history that closes the loop.",
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        "content_text": "Product overview\nProblem\nMerchant setup\nFeedback model\nLive widget\nOrder lookup\nAdmin\nProcess\n\nTry the chatbot\n\nService Cloud · Storefront chatbot\n\nFrom Figma concept to a production-ready chatbot specification\n\nI designed and specified the website chatbot end to end - merchant setup,\nconversation UX, suggested questions, message-level and session-level\nfeedback, admin conversation history, and handoff requirements for AI/API integration and production delivery.\n\nRoleLead UX / UI & Product Design\n\nPlatformWeb · desktop & mobile\n\nScopeSetup, widget, admin, feedback\n\nOutcomeDesigned for production\n\nThe chatbot had a face. It didn’t have a product.\n\nMerchants needed a support chatbot on their website that could answer\norder, shipping, and policy questions without waiting for an agent.\nThe Figma concept showed the widget. Production needed everything\naround it: a setup path merchants can finish without a developer,\nconversation logic, empty and error states, a way to collect\nfeedback, and a place in Service Cloud to read what actually happened.\n\nThe goal was not a prettier chat bubble. It was a reliable channel -\none that matches the design, works on desktop and mobile, connects\nto backend and AI services, and gives the team a closed loop to\nimprove answers over time.\n\nProduct outcome\n\nA storefront chatbot merchants can configure, shoppers can use, and operators can measure\n\nMerchants brand it, set hours, and publish. Customers get answers,\nsuggested questions, and a clear path to an agent. Operators see\nconversation history, star ratings, thumbs, and “what went wrong”\nreasons - so the next release is informed by real use, not guesswork.\n\nMerchants set it up themselves\n\nThe updated design file expands the merchant journey beyond branding. They create a chatbot from an empty list, name it,\ntheme it, set hours, write quick prompts, attach a knowledge base,\nchoose what the form captures, then generate a preview and publish.\nSave Changes is a draft. Publish Chatbot is live.\n\n01 · Chatbots\n\nCreate from empty\n\nEmpty state, then a list with a 20-bot cap. Create new chatbot is the only primary action.\n\n02 · General\n\nName the bot\n\nChatbot name and description - not a street address. Preview on the right updates as they type.\n\n03 · Appearance\n\nBrand the widget\n\nLogo (160×160 PNG/SVG), colour palette or custom hex, live widget preview, Save changes.\n\n04 · Hours\n\nWhen it is on\n\n24/7 availability or custom hours for each day. If the store is closed, the widget should not pretend an agent is there.\n\n05 · Quick prompts\n\nSuggested questions\n\nDefault chips like Track my order and Where is my order. Merchants edit the prompts shoppers see first.\n\n06 · Knowledge\n\nTeach the bot\n\nPaste a URL or upload a PDF, TXT, DOC, or DOCX file (up to 5 MB). This is how answers stay on-brand without a developer.\n\n07 · Forms\n\nWhat to capture\n\nEmail, name, phone - on or off. The bot only asks for what the merchant actually needs.\n\n08 · Preview\n\nThen publish\n\nGenerate Preview, Edit, or Publish Chatbot. They never commit to the storefront blind.\n\nChatbots\nList, 9 of 20 cap, Create new chatbot.\n\nGeneral\nChatbot name and description, with live preview.\n\nAppearance\nLogo, primary colour, live widget.\n\nBusiness hours\n24×7 or per-day from/to times.\n\nQuick prompts\nDefault questions shoppers tap to start.\n\nKnowledge base\nURL or PDF / TXT / Doc upload.\n\nForm settings\nCapture email, name, phone.\n\nLive preview\nGenerate, edit, or publish.\n\nTwo feedback jobs. Two controls.\n\nEarly drafts mixed thumbs and stars on the same moment. That creates\nnoisy data: a bad answer and a bad session are not the same thing.\nThe final interaction model separates these signals.\n\nOn the message\n\nThumbs up / thumbs down\n\nShown on bot replies that can be judged - not welcome, login,\nticket-created, or order-identification prompts. Thumbs down opens\na reason list (Didn’t solve my issue, Inaccurate information,\nConfusing response, Didn’t understand my issue, Other) so the\nteam knows why the answer failed.\n\nOn the conversation\n\nStar rating at close\n\nWhen the visitor ends the chat - “How was your chat with Sam?” -\nthey rate the whole session from 1–5, or skip. Stars live on the\nConversations list. Thumbs live on each reply and in the Chatbot\nFeedback table. Operators can scan quality, then drill into the\nexact message that went wrong.\n\nLive widget\n\nBuilt to the feedback design specification: header, bubbles, suggestion chips,\nthumbs, reason chips, Other + submit, and session CSAT on close.\nTry an order question, then thumbs-down a reply and close the chat.\n\njohny-style.store\n\nOrder #N1237\n\nDispatched 18 Jun · In transit\n\nNeed to change the delivery address, check a return, or ask when it arrives? The chatbot on the right is the same widget designed for the merchant site - not a separate admin tool.\n\nUse a suggested question, rate a bot reply with thumbs, then hit the × to leave a star rating for the session.\n\nJohny Style Store\n\n24/7 Support Chat\n\nStorefront states, designed in Figma\n\nThe Collect Feedback frames cover the customer path from a judged reply\nthrough reason capture and session rating. These are the source of truth\nthe live widget follows.\n\nMessage feedback\nThumbs on the bot reply. Down opens “What went wrong?” with selectable reasons.\n\nSession close\nClosing the widget asks for a star rating. Skip is always available - never trap the visitor.\n\nCSAT submitted\nStars are the conversation score. They appear on the admin Conversations list, not on every bubble.\n\nGuest order lookup, without leaking PII\n\nA guest can ask where an order is. I designed the lookup so it does not\nshare order details until the visitor logs in or verifies with a one-time\nemail code. These three frames are the overview: the gate, the code, then\nthe order answer.\n\nAuth required\nOrder lookup asks the visitor to log in or verify with a one-time email code.\n\nEnter OTP\nSix boxes. Progress shows how many digits are in.\n\nVerified\nOrder 10482. Shipped, AusPost, tracking, expected delivery. Next actions without asking again.\n\nThe loop closes in Service Cloud\n\nA storefront widget without an operator view is a black box. Conversations\nis the transcript: star rating on the session, thumbs on bot replies,\nand Create Ticket when the bot can’t finish the job.\n\nConversations -\nList + transcript. Star rating at conversation level. Thumbs on bot\nreplies in the thread. Create Ticket when the bot can’t finish the job.\n\nDesign to production\n\nThe work covered the full journey: understanding the Figma and user flow, defining behaviour, specifying the interface and service integrations, documenting edge cases, and preparing the product for implementation and ongoing improvement.\n\n01\n\nDesign\n\nReview Figma, map merchant setup and the conversation, specify every state - loading, empty, error, thumbs, CSAT, skip.\n\n02\n\nInterface\n\nResponsive widget: messages, chips, input, buttons, focus order, and touch targets that work on mobile.\n\n03\n\nIntegrate\n\nConversation logic, AI/backend, order and policy APIs, persistence for history and feedback.\n\n04\n\nShip & learn\n\nTest edges, harden security and reliability, deploy, then improve from thumbs, stars, and usage.\n\nWhat the product had to cover\n\nMerchant setup: create, name, appearance, hours, prompts, knowledge, forms, publish\n\nResponsive implementation specifications based on the Figma screens\n\nMessages, input, buttons, loading, errors, and interactions\n\nConversation flow and chatbot logic\n\nAI / backend services and required APIs or data sources\n\nConversation history, suggested questions, and feedback\n\nDesktop and mobile, including accessibility basics\n\nEdge cases, performance, security, and reliability\n\nProduction deployment, monitoring, and ongoing improvement\n\nStates that had to be real, not implied\n\nMerchant setup Create → name → brand → hours → prompts → knowledge base → forms → preview → publish.\n\nSuggested questions Recognition over recall - offer the three most likely intents before asking people to type.\n\nLoading Typing indicator while the model or order API responds. Never a frozen input.\n\nThumbs + reasons Diagnose the answer. “Other” reveals a short free-text field, then Submit.\n\nSession CSAT Rate the chat on close. Skip is equal to Submit in hierarchy - leaving is allowed.\n\nHistory Operators open the same thread, see stars and thumbs, and can create a ticket.\n\nGuest order lookup Log in or a one-time email code before any order details. Wrong codes count down. Three fails lock the lookup.\n\nAuth declined If they skip verify, no PII. Offer verify, log in, or an agent.\n\nErrors If the bot or an API fails, say so in plain language and keep the composer available.\n\nWhat I would look for after launch\n\nThe Figma file is the contract. The product is the loop: thumbs-down reasons\nfeeding answer quality, conversation stars feeding overall CSAT, and\nCreate Ticket catching the cases the bot should not fake. From there\nthe work is monitoring, not more chrome - which replies fail, which\nintents need better data, and where mobile still drops the composer.\n\nWebsite chatbot · Lead UX / UI & Product Design · Service Cloud Tickets",
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                "caption": "Session close Closing the widget asks for a star rating. Skip is always available - never trap the visitor.",
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                "caption": "Verified Order 10482. Shipped, AusPost, tracking, expected delivery. Next actions without asking again.",
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