Conversation Summary

End-of-conversation summaries with top recommendations based on the discussion - perfect for providing contextual suggestions after meaningful interactions.

Description

Conversation Summary appears at natural conclusion points in conversations, providing a recap of the discussion along with relevant product recommendations. This format feels like a helpful assistant summarizing key points and suggesting useful tools based on what was discussed.

Key Features

• Natural conclusion: Appears at the end of conversations
• Contextual summary: Summarizes the conversation and provides relevant recommendations
• Top recommendations: Shows the most relevant products based on discussion
• Conversation flow: Maintains natural chat interface flow
• Intelligent timing: Appears at optimal conversation endpoints
• Personalized content: Tailored to specific conversation context

Implementation

Basic Usage

import { AdMeshConversationSummary } from 'admesh-ui-sdk';

<AdMeshConversationSummary
  recommendations={recommendations}
  conversationSummary="Based on our conversation about payment solutions..."
  showTopRecommendations={3}
  onRecommendationClick={(adId, link) => window.open(link)}
/>

Configuration Options

Prop	Type	Default	Description
recommendations	Recommendation[]	Required	Array of recommendation objects
conversationSummary	string	Required	Summary text of the conversation
showTopRecommendations	number	3	Number of top recommendations to display
showSummary	boolean	true	Display conversation summary text
showMatchScores	boolean	false	Show relevance scores for recommendations
onRecommendationClick	(adId: string, link: string) => void	Required	Click handler for recommendations

Display Variations

With Summary

<AdMeshConversationSummary
  conversationSummary="Based on our discussion about e-commerce solutions..."
  showTopRecommendations={3}
  showSummary={true}
/>

Recommendations Only

<AdMeshConversationSummary
  showTopRecommendations={5}
  showSummary={false}
  showMatchScores={true}
/>

Best For

• AI CRM Systems: Regie.ai, CoPilot AI, Nooks
• Chat Interfaces: Conversational AI platforms and chatbots
• Customer Service: Support platforms and help desk systems
• Sales Tools: Lead qualification and sales assistance platforms
• Consultation Platforms: Advisory and recommendation services

Platform Examples

AI CRM Systems

Perfect for sales and marketing tool recommendations:

Examples: Regie.ai, CoPilot AI, Nooks

<AdMeshConversationSummary
  conversationSummary="Based on your sales challenges, here are some recommended tools..."
  showTopRecommendations={3}
  onRecommendationClick={handleCRMToolClick}
/>

Customer Service Platforms

Ideal for support tool suggestions:

<AdMeshConversationSummary
  conversationSummary="To help resolve similar issues in the future..."
  showTopRecommendations={2}
  showMatchScores={false}
  onRecommendationClick={handleSupportToolClick}
/>

Consultation Platforms

Great for advisory service recommendations:

<AdMeshConversationSummary
  conversationSummary="Based on your business needs assessment..."
  showTopRecommendations={4}
  showMatchScores={true}
  onRecommendationClick={handleConsultingToolClick}
/>

Content Structure

A typical Conversation Summary includes:

1. Summary Header: "Based on our conversation..." or similar
2. Context Recap: Brief summary of key discussion points
3. Recommendation Introduction: Transition to suggested tools
4. Top Recommendations: List of most relevant products
5. Optional Scores: Match percentages for each recommendation

Timing and Triggers

Conversation Summary should appear when:

• Natural endpoints: User indicates conversation is concluding
• Problem resolution: After addressing user's main concern
• Information gathering: After collecting sufficient context
• Decision points: When user is ready to take action
• Session timeouts: After periods of inactivity

Styling and Customization

The component maintains conversational flow while clearly presenting recommendations:

CSS Classes

• .admesh-conversation-summary: Main container
• .admesh-summary-text: Conversation summary content
• .admesh-recommendations-list: Recommendations container
• .admesh-recommendation-item: Individual recommendation
• .admesh-match-score: Relevance score display

Visual Design

• Conversational tone: Maintains chat interface aesthetics
• Clear separation: Distinguishes summary from recommendations
• Scannable format: Easy-to-read recommendation list
• Consistent spacing: Proper visual hierarchy

Analytics and Tracking

Conversation Summary provides comprehensive analytics:

• Summary impressions: When summaries are displayed
• Recommendation views: Which recommendations are seen
• Click patterns: Most engaging recommendation positions
• Conversation context: What topics drive recommendations
• Conversion attribution: Revenue from conversation-based recommendations

Performance Metrics

Conversation Summaries typically achieve:

• High relevance: 85%+ contextual accuracy
• Good CTR: 4-10% click-through rates
• Strong conversions: Higher purchase intent from context
• User satisfaction: Positive feedback on helpfulness