AI Models & Efficient LLM
We use advanced AI and ML technologies to drive insights and automation.
1. Natural Language Processing (NLP)
• Sentiment Analysis: Identifies emotional tones in community conversations.
• Topic Modeling: Discovers the most discussed topics and trends in conversations.
• Contextual Understanding: Fine-tuned on Web3-specific language for precise analysis of jargon, trends, and slang.
2. Machine Learning Algorithms
• Anomaly Detection: Identifies irregular patterns in metrics, such as sudden member drops.
• Clustering and Classification: Categorizes communities based on activity levels, growth patterns, or sentiment.
• Predictive Models: Provides forecasts for metrics like engagement or member growth over time.
3. Large Language Models (LLMs)
• The chatbot is built on a fine-tuned LLM like OpenAI’s GPT models, ensuring:
• Context-aware answers.
• Historical analysis up to 12 months.
• Deep integration with Cribble’s proprietary analytics.
Mapping KPIs to Problems and Solutions
Cribble's AI models map KPIs to potential community issues and suggest solutions:
Example Scenario:
Problem Detected: Increasing Churn Rate and declining Active Members.
AI Analysis:
Sentiment Analysis reveals growing negative sentiment.
Topic Modeling identifies frequent mentions of unresolved issues.
Suggested Solutions:
Actively Address Issues: Respond to common concerns promptly.
Enhance Engagement: Introduce new quests or contests.
Community Feedback: Conduct polls to gather member feedback.
High Churn Rate
Member dissatisfaction
Improve support, increase engagement efforts
Low Engagement Rate
Content not resonating with members
Diversify content, introduce interactive events
Negative Sentiment
Issues within the community
Address concerns, enhance moderation
Table 2: Mapping KPIs to Actions AI suggestions
Last updated