User behavior analysis

Basic Concepts of User Behavior Analysis

User behavior analysis refers to the process of collecting, processing, and analyzing user interaction data in digital products to understand user habits, preferences, and needs. In web applications, ECharts, as a powerful data visualization library, can transform complex user behavior data into intuitive charts. Common user behaviors include page views, clicks, scrolling, dwell time, etc. When aggregated and analyzed, this data can reveal user usage patterns.

Data Collection and Processing

User behavior data is typically collected through front-end tracking techniques. Below is a simple JavaScript code example for tracking user click behavior:

document.addEventListener('click', function(event) {
  const target = event.target;
  const data = {
    timestamp: new Date().toISOString(),
    element: target.tagName,
    id: target.id || 'none',
    class: target.className || 'none',
    x: event.clientX,
    y: event.clientY
  };
  
  // Send data to the backend
  fetch('/api/track', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json'
    },
    body: JSON.stringify(data)
  });
});

The collected raw data needs to be cleaned and transformed before analysis. Common data processing steps include:

1. Filtering invalid data (e.g., bot traffic)
2. Standardizing time formats
3. Categorizing similar behaviors
4. Calculating derived metrics (e.g., click-through rate, conversion rate)

Applications of ECharts in User Behavior Analysis

ECharts offers various chart types to visualize user behavior data. Below is an example of using ECharts to create a heatmap showing user visit time distribution:

// Initialize ECharts instance
const chart = echarts.init(document.getElementById('heatmap-container'));

// Prepare data
const hours = [];
for (let i = 0; i < 24; i++) {
  hours.push(i + ':00');
}
const days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'];

// Simulate data
const data = [];
for (let i = 0; i < 7; i++) {
  for (let j = 0; j < 24; j++) {
    data.push([j, i, Math.round(Math.random() * 1000)]);
  }
}

// Configuration options
const option = {
  tooltip: {
    position: 'top'
  },
  grid: {
    height: '50%',
    top: '10%'
  },
  xAxis: {
    type: 'category',
    data: hours,
    splitArea: {
      show: true
    }
  },
  yAxis: {
    type: 'category',
    data: days,
    splitArea: {
      show: true
    }
  },
  visualMap: {
    min: 0,
    max: 1000,
    calculable: true,
    orient: 'horizontal',
    left: 'center',
    bottom: '15%'
  },
  series: [{
    name: 'Visit Volume',
    type: 'heatmap',
    data: data,
    label: {
      show: false
    },
    emphasis: {
      itemStyle: {
        shadowBlur: 10,
        shadowColor: 'rgba(0, 0, 0, 0.5)'
      }
    }
  }]
};

// Apply configuration
chart.setOption(option);

Common Analysis Scenarios and Chart Selection

Different analysis purposes require different types of charts:

1. User Path Analysis: Sankey diagrams are suitable for showing user flow paths on a website.

option = {
  series: {
    type: 'sankey',
    data: [
      {name: 'Home'},
      {name: 'Product Page'},
      {name: 'Shopping Cart'},
      {name: 'Payment Page'},
      {name: 'Completion'}
    ],
    links: [
      {source: 'Home', target: 'Product Page', value: 100},
      {source: 'Product Page', target: 'Shopping Cart', value: 40},
      {source: 'Shopping Cart', target: 'Payment Page', value: 20},
      {source: 'Payment Page', target: 'Completion', value: 15}
    ]
  }
}

2. User Retention Analysis: Line charts clearly show changes in user retention rates over time.
3. Feature Usage Analysis: Pie or doughnut charts are suitable for displaying the proportion of usage across different features.
4. User Segmentation Comparison: Bar charts facilitate comparisons of behavior differences among user groups.

Advanced Analysis Techniques

1. Funnel Analysis: Identify drop-off points in the conversion process.

option = {
  series: [
    {
      type: 'funnel',
      data: [
        {value: 100, name: 'Visit'},
        {value: 80, name: 'Signup'},
        {value: 60, name: 'Order'},
        {value: 40, name: 'Payment'},
        {value: 20, name: 'Repurchase'}
      ]
    }
  ]
}

2. User Segmentation: Stratify users based on the RFM model (Recency, Frequency, Monetary).
3. Event Sequence Analysis: Identify common behavior patterns and sequences.
4. Cluster Analysis: Discover user groups with similar behaviors.

Performance Optimization and Big Data Processing

When dealing with large-scale user behavior data, consider the following optimization strategies:

1. Data Sampling: Perform reasonable sampling on massive datasets.
2. Incremental Rendering: Use ECharts' incremental rendering feature for large datasets.

option = {
  dataset: {
    source: largeDataSet
  },
  series: {
    type: 'line',
    progressive: 1000, // Incremental rendering threshold
    progressiveThreshold: 5000 // Data volume threshold to enable incremental rendering
  }
}

3. Web Workers: Process data in background threads.
4. Data Aggregation: Pre-aggregate data at the database level.

Implementing Interactive Features

Enhancing chart interactivity helps analysts explore data more deeply:

1. Data Drill-Down: Click chart elements to view more detailed data.

myChart.on('click', function(params) {
  if(params.componentType === 'series') {
    // Fetch more detailed data and update the chart
    fetchDetailData(params.name).then(data => {
      updateChart(data);
    });
  }
});

2. Dynamic Filtering: Link multiple charts for cross-analysis.
3. Time Range Selection: Allow users to customize analysis periods.
4. Data Export: Support exporting chart data as CSV or images.

Case Studies

An e-commerce platform uses ECharts to analyze user shopping behavior:

1. Homepage Heatmap: Identify frequently clicked areas.
2. Cart Abandonment Analysis: Track users who add items to the cart but do not complete the purchase.
3. Search Term Analysis: Visualize popular search terms and their conversion effects.
4. User Lifecycle: Analyze the long-term value of users from different acquisition channels.

// User Lifetime Value Analysis
option = {
  radar: {
    indicator: [
      {name: '7-Day Retention', max: 100},
      {name: '30-Day Retention', max: 100},
      {name: '90-Day Retention', max: 100},
      {name: 'ARPU', max: 500},
      {name: 'Purchase Frequency', max: 10}
    ]
  },
  series: [{
    type: 'radar',
    data: [
      {
        value: [85, 70, 50, 300, 4.5],
        name: 'Search Engine'
      },
      {
        value: [60, 40, 25, 150, 2.8],
        name: 'Social Media'
      }
    ]
  }]
}

Mobile Adaptation Strategies

Displaying user behavior analysis charts on mobile devices requires:

1. Responsive Design: Adjust chart sizes based on screen dimensions.

window.addEventListener('resize', function() {
  myChart.resize();
});

2. Touch Interaction: Optimize gesture-based interactions.
3. Performance Optimization: Reduce memory usage on mobile devices.
4. Offline Caching: Support offline viewing of recent analysis results.

Data Security and Privacy Protection

When collecting and analyzing user behavior data, it is essential to:

1. Anonymize personally identifiable information.
2. Comply with data protection regulations like GDPR.
3. Provide users with an opt-out mechanism for tracking.
4. Encrypt sensitive behavior data storage.

Future Trends

1. Real-Time Analysis: Use WebSocket for real-time visualization of behavior data.
2. AI Integration: Combine machine learning algorithms to automatically detect abnormal patterns.
3. Cross-Platform Analysis: Unified analysis of user behavior across web, mobile, and mini-programs.
4. Augmented Reality: Use AR technology to display 3D user behavior paths.