> ## Documentation Index
> Fetch the complete documentation index at: https://developer.lulacommerce.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Get Orders History

> Retrieve comprehensive historical order data with advanced analytics, reporting capabilities, and detailed performance metrics.

This endpoint provides access to comprehensive historical order data with powerful analytics capabilities. Ideal for business intelligence, performance reporting, trend analysis, and strategic planning.

<Info>
  This endpoint includes advanced analytics features like trend calculations, performance metrics, and comparative analysis across different time periods.
</Info>

### Query Parameters

<ParamField query="store_id" type="string" required>
  The unique identifier of the store whose order history you want to retrieve
</ParamField>

<ParamField query="date_from" type="string" required>
  Start date for historical data retrieval (ISO 8601 format: YYYY-MM-DDTHH:mm:ssZ)
</ParamField>

<ParamField query="date_to" type="string" required>
  End date for historical data retrieval (ISO 8601 format: YYYY-MM-DDTHH:mm:ssZ)
</ParamField>

<ParamField query="granularity" type="string" optional default="daily">
  Data aggregation level: "hourly", "daily", "weekly", "monthly"
</ParamField>

<ParamField query="include_analytics" type="boolean" optional default="true">
  Whether to include advanced analytics and performance metrics
</ParamField>

<ParamField query="compare_period" type="boolean" optional default="false">
  Include comparison with the previous equivalent period
</ParamField>

<ParamField query="partner" type="string" optional>
  Filter by specific delivery platform for focused analysis
</ParamField>

<ParamField query="include_items" type="boolean" optional default="false">
  Include detailed item-level analytics in the response
</ParamField>

### Response

<ResponseField name="period_summary" type="object">
  High-level summary for the requested time period

  <Expandable title="Period Summary">
    <ResponseField name="date_from" type="string">Start date of the period</ResponseField>
    <ResponseField name="date_to" type="string">End date of the period</ResponseField>
    <ResponseField name="total_orders" type="integer">Total number of orders</ResponseField>
    <ResponseField name="total_revenue" type="string">Total revenue generated</ResponseField>
    <ResponseField name="average_order_value" type="string">Average order value</ResponseField>
    <ResponseField name="completion_rate" type="number">Percentage of orders completed successfully</ResponseField>
    <ResponseField name="cancellation_rate" type="number">Percentage of orders canceled</ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="time_series_data" type="array">
  Data points broken down by the specified granularity

  <Expandable title="Time Series Point">
    <ResponseField name="period" type="string">Time period identifier</ResponseField>
    <ResponseField name="orders_count" type="integer">Number of orders in this period</ResponseField>
    <ResponseField name="revenue" type="string">Revenue for this period</ResponseField>
    <ResponseField name="average_order_value" type="string">Average order value for this period</ResponseField>
    <ResponseField name="completion_rate" type="number">Completion rate for this period</ResponseField>
    <ResponseField name="peak_hour" type="string">Busiest hour in this period (if granularity allows)</ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="partner_performance" type="object">
  Performance breakdown by delivery platform

  <Expandable title="Partner Performance">
    <ResponseField name="UberEats" type="object">Performance metrics for UberEats orders</ResponseField>
    <ResponseField name="DoorDash" type="object">Performance metrics for DoorDash orders</ResponseField>
    <ResponseField name="GrubHub" type="object">Performance metrics for GrubHub orders</ResponseField>
    <ResponseField name="LulaDirect" type="object">Performance metrics for LulaDirect orders</ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="analytics" type="object">
  Advanced analytics and insights (when include\_analytics=true)

  <Expandable title="Analytics Details">
    <ResponseField name="growth_trends" type="object">
      Order volume and revenue growth trends
    </ResponseField>

    <ResponseField name="seasonal_patterns" type="object">
      Identified seasonal patterns and recommendations
    </ResponseField>

    <ResponseField name="performance_indicators" type="object">
      Key performance indicators and benchmarks
    </ResponseField>

    <ResponseField name="recommendations" type="array">
      AI-generated insights and recommendations for improvement
    </ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="comparison" type="object">
  Comparison with previous period (when compare\_period=true)

  <Expandable title="Period Comparison">
    <ResponseField name="previous_period" type="object">Summary of the previous equivalent period</ResponseField>
    <ResponseField name="growth_metrics" type="object">Growth percentages and changes</ResponseField>
    <ResponseField name="trend_analysis" type="string">Analysis of trends between periods</ResponseField>
  </Expandable>
</ResponseField>

### Response Example

```json theme={null}
{
  "period_summary": {
    "date_from": "2024-01-01T00:00:00Z",
    "date_to": "2024-01-31T23:59:59Z",
    "total_orders": 1247,
    "total_revenue": "64,859.75",
    "average_order_value": "52.03",
    "completion_rate": 94.5,
    "cancellation_rate": 3.2
  },
  "time_series_data": [
    {
      "period": "2024-01-01",
      "orders_count": 45,
      "revenue": "2,341.25",
      "average_order_value": "52.03",
      "completion_rate": 96.0,
      "peak_hour": "19:00"
    },
    {
      "period": "2024-01-02",
      "orders_count": 38,
      "revenue": "1,977.50",
      "average_order_value": "52.04",
      "completion_rate": 95.0,
      "peak_hour": "18:30"
    }
  ],
  "partner_performance": {
    "UberEats": {
      "total_orders": 485,
      "total_revenue": "25,243.80",
      "average_order_value": "52.05",
      "completion_rate": 95.2,
      "average_prep_time": "18.5 minutes"
    },
    "DoorDash": {
      "total_orders": 412,
      "total_revenue": "21,424.20",
      "average_order_value": "52.01",
      "completion_rate": 94.8,
      "average_prep_time": "17.2 minutes"
    },
    "GrubHub": {
      "total_orders": 245,
      "total_revenue": "12,745.50",
      "average_order_value": "52.02",
      "completion_rate": 93.5,
      "average_prep_time": "19.8 minutes"
    },
    "LulaDirect": {
      "total_orders": 105,
      "total_revenue": "5,446.25",
      "average_order_value": "51.87",
      "completion_rate": 96.8,
      "average_prep_time": "15.3 minutes"
    }
  },
  "analytics": {
    "growth_trends": {
      "monthly_growth": "+12.5%",
      "revenue_trend": "increasing",
      "order_volume_trend": "stable_growth"
    },
    "seasonal_patterns": {
      "peak_days": ["Friday", "Saturday", "Sunday"],
      "peak_hours": ["17:00-21:00"],
      "seasonal_recommendations": "Consider extended hours on weekends"
    },
    "performance_indicators": {
      "efficiency_score": 87.5,
      "customer_satisfaction_proxy": 94.5,
      "revenue_per_hour": "$425.30"
    },
    "recommendations": [
      "Consider promoting during Tuesday-Thursday slow periods",
      "UberEats partnership showing strongest performance",
      "Weekend capacity could be increased to capture more demand"
    ]
  },
  "comparison": {
    "previous_period": {
      "total_orders": 1098,
      "total_revenue": "57,241.50",
      "average_order_value": "52.14"
    },
    "growth_metrics": {
      "order_growth": "+13.6%",
      "revenue_growth": "+13.3%",
      "avg_order_value_change": "-0.2%"
    },
    "trend_analysis": "Strong growth in order volume and revenue with stable order values, indicating successful customer acquisition and retention"
  }
}
```

<Accordion title="Granularity Examples">
  **Hourly Analysis (for operational optimization)**

  ```
  GET {{orders_api_base_url}}/orders/history/?store_id=449235c1-3d04-4519-998b-40d2a621e5e0&date_from=2024-01-15T00:00:00Z&date_to=2024-01-15T23:59:59Z&granularity=hourly
  ```

  **Weekly Trends (for strategic planning)**

  ```
  GET {{orders_api_base_url}}/orders/history/?store_id=449235c1-3d04-4519-998b-40d2a621e5e0&date_from=2024-01-01T00:00:00Z&date_to=2024-03-31T23:59:59Z&granularity=weekly&compare_period=true
  ```

  **Monthly Performance Review**

  ```
  GET {{orders_api_base_url}}/orders/history/?store_id=449235c1-3d04-4519-998b-40d2a621e5e0&date_from=2024-01-01T00:00:00Z&date_to=2024-12-31T23:59:59Z&granularity=monthly&include_analytics=true
  ```
</Accordion>

<Tip>
  **Business Intelligence**: Use the analytics data to identify growth opportunities, optimize operations, and make data-driven decisions about staffing, inventory, and marketing.
</Tip>

<Note>
  **Data Freshness**: Historical data is updated in near real-time. For the most current hour's data, allow 15-30 minutes for complete processing.
</Note>

<Warning>
  **Large Datasets**: When requesting large date ranges with hourly granularity, responses may be substantial. Consider using daily or weekly granularity for broad analysis.
</Warning>

### Use Cases

<Accordion title="Business Intelligence Applications">
  **Revenue Analysis**

  * Monthly/quarterly revenue trends
  * Platform performance comparison
  * Seasonal revenue patterns

  **Operational Optimization**

  * Identify peak hours for staffing
  * Optimize kitchen capacity planning
  * Improve order completion rates

  **Strategic Planning**

  * Market penetration analysis
  * Growth opportunity identification
  * Partnership performance evaluation

  **Performance Monitoring**

  * KPI tracking and benchmarking
  * Efficiency improvement initiatives
  * Customer satisfaction analysis
</Accordion>