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Wapikit provides comprehensive analytics to help you measure, understand, and optimize your WhatsApp marketing and customer engagement efforts. These insights enable data-driven decisions to improve your campaigns and customer interactions.

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Analytics Dashboard

The Wapikit Analytics Dashboard gives you a centralized view of your performance metrics across campaigns, conversations, and customer engagement. The dashboard is divided into several key sections:

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Overview Metrics

The overview section provides high-level performance indicators:
  • Active Contacts: Total number of contacts who have interacted with your business
  • Message Volume: Total messages sent and received
  • Response Rate: Percentage of your messages that receive replies
  • Average Response Time: How quickly customers respond to your messages
  • Conversation Completion Rate: Percentage of conversations that reach resolution

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Campaign Analytics

Track the performance of your WhatsApp campaigns:
  • Delivery Metrics: Sent, delivered, read, and failed message counts
  • Engagement Metrics: Reply rates, click-through rates, and conversion rates
  • Campaign Comparison: Side-by-side performance analysis of different campaigns
  • Time-based Analysis: Performance trends over hours, days, weeks, or months
  • Template Performance: Effectiveness of different message templates

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Conversation Analytics

Measure the efficiency and effectiveness of your customer conversations:
  • Volume Trends: Conversation patterns by time and day
  • Resolution Time: Average time to resolve customer inquiries
  • Team Performance: Response times and resolution rates by team member
  • Topic Analysis: Common conversation topics and issues
  • Customer Satisfaction: Sentiment analysis and explicit feedback metrics

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Contact Analytics

Understand your audience better:
  • Growth Metrics: Contact list growth over time
  • Engagement Segments: Categorization of contacts by engagement level
  • Demographic Insights: Analysis based on available contact attributes
  • Retention Analysis: Contact activity and churn patterns
  • Conversion Tracking: Progress through marketing and sales funnels

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AI-Powered Insights

Wapikit’s AI analyzes your data to provide actionable insights:

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Automated Recommendations

The AI generates specific recommendations to improve performance:
  • Campaign Optimization: Suggestions for improving message content, timing, or targeting
  • Conversation Improvements: Recommendations for enhancing response quality or efficiency
  • Contact Segmentation: Insights for better audience targeting
  • Resource Allocation: Guidance on team scheduling based on conversation patterns

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Predictive Analytics

Wapikit’s AI can forecast future trends:
  • Contact Growth Projections: Estimated list growth based on current trends
  • Campaign Performance Predictions: Expected outcomes for planned campaigns
  • Conversation Volume Forecasts: Anticipated customer inquiry patterns
  • Churn Risk Identification: Contacts at risk of disengagement

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Anomaly Detection

The AI automatically identifies unusual patterns that may require attention:
  • Sudden Changes in Engagement: Unexpected drops or spikes in message responses
  • Delivery Issues: Abnormal message delivery failure rates
  • Response Time Anomalies: Unusual delays in team or customer responses
  • Sentiment Shifts: Significant changes in conversation sentiment

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Custom Reports

Create tailored reports for specific business needs:
  1. Navigate to Analytics > Custom Reports
  2. Select metrics and dimensions to include
  3. Choose visualization types (charts, tables, etc.)
  4. Set the time period for analysis
  5. Save the report for future reference or schedule regular delivery

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Data Export

Export analytics data for external analysis:
  • CSV Export: Download data in spreadsheet format
  • API Access: Programmatically retrieve analytics via the Analytics API
  • Integration Options: Connect with business intelligence tools

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Best Practices

  • Set clear KPIs: Define specific metrics that align with your business objectives
  • Analyze trends, not just numbers: Look for patterns and changes over time
  • Compare related metrics: Understand relationships between different data points
  • Act on insights: Implement changes based on analytics findings
  • Test and measure: Use A/B testing to validate optimization strategies
  • Review regularly: Schedule weekly or monthly analytics review sessions

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Next Steps

After understanding your analytics, you can:
  1. Optimize your campaigns based on performance data
  2. Refine your contact segmentation using engagement insights
  3. Improve conversation handling with response time and satisfaction metrics
  4. Set up AI automations to address patterns identified in your analytics