Analytics and Integrations

Analytics about your users, Income, Geo stats, Products Comparisons, App Usage, Adoption rates and AI insights.

Overview

The Analytics and Integrations section focuses on providing comprehensive insights and functionality to both Tuku Pay and its apps. This includes internal analytics to enhance operations and external analytics endpoints for apps to consume and implement.

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General Analytics

1. Customer Profiling (Internal)

   • Description: Analyze customer behavior across apps by linking identifiers like ID numbers or phone numbers.

   • Use Case: Understand customer trends, frequent transaction patterns, and cross-app behavior.

2. Income Analytics (External)

   • Description: Provides apps with tools to analyze income over specified dates. Supports graphical representation for better insight.

   • Use Case: Track revenue growth and identify high-performing periods.

3. Fraud Detection (Internal & External)

   • Description: Real-time fraud analytics based on transaction patterns, location mismatches, and device fingerprinting.

   • Use Case: Detect and prevent suspicious transactions or activities.

4. App Analytics by Industry (Internal)

   • Description: Categorize apps by industries (e.g., retail, education, religious organizations) and provide usage statistics.

   • Use Case: Identify industry trends and evaluate app performance.

5. Product Analytics (External)

   • Description: Analyze the performance of transaction products, including usage frequency, revenue contribution, and customer feedback.

   • Use Case: Optimize product offerings and identify high-demand features.

6. Server Analytics (Internal)

   • Description: Monitor server performance metrics such as response time, uptime, and transaction speed.

   • Use Case: Ensure reliable operations and minimize downtime.

7. Transaction Speed (Internal & External)

   • Description: Track the average processing time for transactions and identify bottlenecks.

   • Use Case: Enhance transaction efficiency and improve user experience.

8. User Demographics (External)

   • Description: Provides demographic insights about app users, such as age, gender, and location (aggregated data only).

   • Use Case: Tailor services and marketing efforts to target demographics.

9. Retention and Engagement Analytics (External)

   • Description: Track metrics like user retention rates, active user counts, and session durations.

   • Use Case: Improve user engagement and identify areas for feature improvements.

10. Transaction Heatmaps (Internal & External)

    • Description: Visualize transaction density by location over time.

    • Use Case: Identify geographical hotspots for services and optimize operations accordingly.

11. Integration with OpenAI for Predictive Analytics (Internal & External)

    • Description: Use AI models for predictive customer behavior, income trends, and product success.

    • Use Case: Make data-driven decisions with advanced forecasting tools.

12. KYC and Compliance Analytics (Internal)

    • Description: Monitor the completion and validity of Know Your Customer (KYC) data.

    • Use Case: Ensure regulatory compliance and reduce fraud risk.

13. Expense Analysis (External)

    • Description: Track app expenses such as transaction fees and operational costs.

    • Use Case: Optimize spending and manage profitability.

14. Real-Time Dashboard (Internal & External)

    • Description: Provides a live dashboard for monitoring key metrics like transactions per second, fraud attempts, and system alerts.

    • Use Case: Proactive issue resolution and operational oversight.

15. Cross-App Linking and Activity Summary (Internal)

    • Description: Identify customers or organizations using multiple apps and summarize their activity.

    • Use Case: Build unified customer profiles and cross-sell opportunities.

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Sample Endpoints for Analytics

1. Retrieve Income Analytics

Method	GET
URL	/api/analytics/income

Request Body:

{
  "appId": "app123",
  "startDate": "2024-01-01",
  "endDate": "2024-01-31"
}

Response:

{
  "status": "success",
  "incomeData": {
    "totalIncome": 50000,
    "dailyBreakdown": [
      { "date": "2024-01-01", "income": 2000 },
      { "date": "2024-01-02", "income": 2500 }
    ]
  }
}

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2. Fraud Detection Alerts

Method	GET
URL	/api/analytics/fraud-alerts

Request Body:

{
  "appId": "app123",
  "startDate": "2024-01-01",
  "endDate": "2024-01-31"
}

Response:

{
  "status": "success",
  "alerts": [
    {
      "transactionId": "txn001",
      "alertType": "Location Mismatch",
      "description": "Transaction from unexpected location."
    }
  ]
}

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3. Product Analytics

Method	GET
URL	/api/analytics/products

Request Body:

{
  "appId": "app123"
}

Response:

{
  "status": "success",
  "products": [
    {
      "productId": "prod123",
      "productName": "Bulk Pay",
      "usageCount": 500,
      "revenueGenerated": 20000
    }
  ]
}

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Additional Notes

1. Integration Possibilities:

   • OpenAI models can enhance customer profiling and fraud detection.

   • Predictive analytics can provide valuable forecasts for income and expenses.

2. Security Considerations:

   • Sensitive data like phone numbers or IDs is masked by default.

   • Access to raw data requires additional permissions.

3. Customizability:

   • Apps can request tailored analytics or additional insights via specialized endpoints.