skills/asgard-ai-platform/skills/ecom-conversational

ecom-conversational

Installation
SKILL.md

Conversational Commerce

Framework

IRON LAW: Conversation First, Commerce Second

Conversational commerce works because it meets customers WHERE THEY
ALREADY ARE (messaging apps). Forcing a sales pitch into a chat channel
kills trust. The conversation must provide genuine value (answering questions,
solving problems) BEFORE introducing products or purchases.

The sequence: Help → Trust → Recommend → Convert

Platform Comparison (Asia-Pacific Focus)

Platform Users (Taiwan) Commerce Features Best For
LINE 21M+ (95% penetration) LINE Shopping, Rich Menu, LIFF, payment Taiwan, Japan, Thailand primary channel
Instagram DM ~10M Shop tags, quick replies, product stickers Visual products, younger demographic
Facebook Messenger ~18M Shops integration, automated responses Broad reach, older demographic
WhatsApp Limited in Taiwan Catalog, cart, payment (select markets) SEA, India, Brazil
WeChat ~1M (Taiwan) Mini Programs, WeChat Pay China-connected businesses

Conversation Flow Design

1. Entry Points — How customers start the conversation

  • QR code in store, ad click-to-message, website chat widget, social media link

2. Welcome Flow — First 3 messages

  • Greet warmly, set expectations, offer navigation options
  • Rich menu (LINE) or quick reply buttons — don't ask open-ended questions early

3. Product Discovery — Help them find what they need

  • Guided questions: "What are you looking for?" → category → product
  • Product cards with image, price, and "Buy" button
  • AI-powered recommendations based on conversation context

4. Purchase Flow — Minimize friction

  • In-chat checkout where possible (LINE Pay, in-app payment)
  • If redirecting to website, deep-link to the specific product (not homepage)
  • Order confirmation message with tracking

5. Post-Purchase — Retain and upsell

  • Shipping updates via message
  • Follow-up: "How's the product?" (7 days after delivery)
  • Personalized recommendations based on purchase history

Chatbot vs Human Handoff

Scenario Handle with Bot Hand off to Human
FAQ (hours, shipping, returns)
Product recommendations (simple)
Complex product questions
Complaints / issues ✓ (immediately)
High-value purchases Bot assists → human closes

Key metric: Bot containment rate (% resolved without human) — target 60-70% for mature bots.

Output Format

# Conversational Commerce Plan: {Business}

## Channel Selection
- Primary: {platform} — rationale: {why}
- Entry points: {QR / ad / social / website}

## Conversation Flow
1. Welcome: {message template}
2. Discovery: {question flow}
3. Product Card: {template}
4. Checkout: {in-chat / redirect}
5. Post-purchase: {follow-up sequence}

## Bot vs Human Split
| Scenario | Handler | SLA |
|----------|---------|-----|
| {scenario} | Bot/Human | {response time} |

## KPIs
| Metric | Target |
|--------|--------|
| Response time | < {X} seconds (bot) / < {X} minutes (human) |
| Containment rate | > 60% |
| Conversation-to-purchase rate | > {X%} |
| Customer satisfaction (CSAT) | > 4.0/5 |

Gotchas

  • LINE Official Account tiers matter: Free tier limits monthly messages. If you exceed, messages are throttled. Budget for premium tier if customer base > 500.
  • Don't over-automate: A bot that can't understand the question and loops "I didn't understand, please choose from the menu" destroys trust faster than no bot at all. Always offer a human escalation path.
  • Messaging is asynchronous: Unlike phone calls, customers expect to message and come back later. Design flows that work with interruptions — save cart state, remember context.
  • Privacy in messaging: Chat history is personal. Don't share conversations internally without consent, and be transparent about data usage.
  • Social commerce is exploding in SEA/Taiwan: LINE Shopping, Instagram Shopping, TikTok Shop — these blur the line between social and commerce. Treat them as primary channels, not add-ons.

References

  • For LINE Official Account setup, see references/line-oa-setup.md
  • For chatbot NLU design patterns, see references/chatbot-design.md
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