Multidimensional Attribution by Peer Comparison
1. API Description
The multidimensional attribution peer-comparison API compares attribution between two peer objects within the same time range. For example:
-
Hangzhou Store vs Nanjing Store
-
East China Region vs South China Region
-
Self-operated channel vs agency channel
Specify the following:
-
Metric to compare
-
Dimensions participating in the analysis, such as channel
-
Dimension that identifies the comparison objects, such as store
-
Filters and time ranges for the current and comparison objects
The API returns:
-
Metric values for the current and comparison objects
-
Growth and growth rate
-
Overall and relative contribution rates for each dimension combination.
2. API URL
3. Common Request Headers
| parameter Name | Type | required | Max Length | Description |
|---|---|---|---|---|
| tenant-id | String | Yes | 32 | Tenant ID used to specify the workspace where the metric resides |
| auth-type | String | Yes | 32 | Authentication method. Supports UID, TOKEN, ACCOUNT, and APIKEY. |
| auth-value | String | Yes | 32 | Authentication value corresponding to auth-type. |
How to Obtain Common parameters
In Aloudata CAN, select Metric Applications in the top navigation bar, then select API Integration in the left navigation menu. You can obtain tenant-id on the API Integration page. Set auth-value according to auth-type.
4. Request parameters
4.1 Body Structure
| Field Name | Type | required | Description |
|---|---|---|---|
| metric | String | Yes | Name of the metric participating in attribution analysis. This can be a temporary metric name |
| metricDefinitions | Map |
No | Temporary metric definitions, such as adding filters to an original metric |
| dimensions | Array |
Yes | List of dimensions participating in attribution, for example ["channel"] |
| attribution | Object | Yes | Attribution-analysis configuration, including comparison method, objects, and time ranges |
4.2 metricDefinitions Structure (Optional)
If you use temporary metrics, such as adding a region filter to an original metric, define them through metricDefinitions:
| Field Name | Type | Description |
|---|---|---|
| refMetric | String | Referenced original metric code |
| filters | Array | Filter expressions applied to the temporary metric |
Example：
"metricDefinitions": {
"order_count_temp1": {
"refMetric": "order_count",
"filters": [
"IN([province],\"Zhejiang\",\"Jiangsu\")"
]
}
}
4.3 attribution Structure (required)
| Field Name | Type | required | Description |
|---|---|---|---|
| attributionCalculateType | String | Yes | Attribution calculation method. AVG means average; more methods may be added later |
| compareDimension | String | Yes | Dimension that identifies the comparison objects, such as "store" or "province" |
| filters | Array | No | Global filters applied to the full comparison scope (currentObject + compareObject) |
| timeConstraint | String | No | Time filter for the full scope. Usually leave it empty and specify time ranges separately in currentObject and compareObject |
| currentObject | Object | Yes | Current object configuration, including filters and time range |
| compareObject | Object | Yes | Comparison object configuration, including filters and time range |
currentObject and compareObject use the same structure:
| Field Name | Type | required | Description |
|---|---|---|---|
| filters | Array | Yes | Filters for the current or comparison object, such as a store or region |
| timeConstraint | String | Yes | Time-range expression for the current or comparison object |
4.4 Request Example
{
"metric": "countDistinct_product_id",
"dimensions": [
"brand",
"price_band"
],
"attribution": {
"attributionCalculateType": "ALL",
"compareDimension": "store_type",
"timeConstraint": "DateTrunc([metric_time],\"DAY\") = DateTrunc(Cast(\"2025-07-03 00:00:00\",\"Timestamp\"),\"DAY\")",
"currentObject": {
"filters": [
"IN([store_type],\"self-operated\")"
]
},
"compareObject": {
"filters": [
"IN([store_type],\"franchise\")"
]
}
}
}
Description：
5. Response parameters
5.1 Top-Level Structure
| Field Name | Type | Description |
|---|---|---|
| tables | Array | List of multiple dimension combinations and attribution tables |
Each element has the following structure:
| Field Name | Type | Description |
|---|---|---|
| dimensions | String | Dimension combination corresponding to the current attribution data. An empty string indicates an overall summary |
| table | Object | Attribution result table for the current dimension combination |
5.2 Fields Inside table
| Field Name | Type | Description |
|---|---|---|
| Array | Value list for each dimension, such as "channel": ["online", "offline"] |
|
| @currentValue | Array |
Metric value of the current object |
| @compareValue | Array |
Metric value of the comparison object |
| @growth | Array |
Growth value (current value - comparison value) |
| @growthRate | Array |
Growth rate ((current value - comparison value) / comparison value) |
| @overallContributionRate | Array |
Overall contribution rate |
| @relativeContributionRate | Array |
Relative contribution rate. This can be empty |
5.3 Response Example
{
"data": {
"tables": [
{
"dimension": "brand",
"table": {
"@currentValue": [
6,
8,
13,
8,
7,
7,
11
],
"@compareValue": [
6,
8,
13,
8,
7,
7,
11
],
"@overallContributionRate": [
null,
null,
null,
null,
null,
null,
null
],
"@growthRate": [
"0",
"0",
"0",
"0",
"0",
"0",
"0"
],
"@growth": [
"0",
"0",
"0",
"0",
"0",
"0",
"0"
],
"brand": [
"Lepin",
"Kangyi",
"Baiwei",
"Youxuan",
"Dianfan",
"Qingquan",
"Fresh Choice"
]
},
"warning": null,
"empty": false
},
{
"dimension": "price_band",
"table": {
"@currentValue": [
23,
6,
31
],
"@compareValue": [
23,
6,
31
],
"@overallContributionRate": [
null,
null,
null
],
"price_band": [
"40-60",
"0-20",
"20-40"
],
"@growthRate": [
"0",
"0",
"0"
],
"@growth": [
"0",
"0",
"0"
]
},
"warning": null,
"empty": false
},
{
"dimension": null,
"table": {
"@currentValue": [
60
],
"@compareValue": [
60
],
"@growthRate": [
"0"
],
"@growth": [
"0"
]
},
"warning": null,
"empty": false
}
],
"warning": null
},
"success": true,
"code": "200",
"errorMsg": null,
"detailErrorMsg": null,
"traceId": "cced9e32f6aa4b3eb03905b51054ad4d.192.17780531304030053"
}
6.Call Example
Request Example
{
"metric": "countDistinct_product_id",
"dimensions": [
"brand",
"price_band"
],
"attribution": {
"attributionCalculateType": "ALL",
"compareDimension": "store_type",
"timeConstraint": "DateTrunc([metric_time],\"DAY\") = DateTrunc(Cast(\"2025-07-03 00:00:00\",\"Timestamp\"),\"DAY\")",
"currentObject": {
"filters": [
"IN([store_type],\"self-operated\")"
]
},
"compareObject": {
"filters": [
"IN([store_type],\"franchise\")"
]
}
}
}
Response Example
{
"data": {
"tables": [
{
"dimension": "brand",
"table": {
"@currentValue": [
6,
8,
13,
8,
7,
7,
11
],
"@compareValue": [
6,
8,
13,
8,
7,
7,
11
],
"@overallContributionRate": [
null,
null,
null,
null,
null,
null,
null
],
"@growthRate": [
"0",
"0",
"0",
"0",
"0",
"0",
"0"
],
"@growth": [
"0",
"0",
"0",
"0",
"0",
"0",
"0"
],
"brand": [
"Lepin",
"Kangyi",
"Baiwei",
"Youxuan",
"Dianfan",
"Qingquan",
"Fresh Choice"
]
},
"warning": null,
"empty": false
},
{
"dimension": "price_band",
"table": {
"@currentValue": [
23,
6,
31
],
"@compareValue": [
23,
6,
31
],
"@overallContributionRate": [
null,
null,
null
],
"price_band": [
"40-60",
"0-20",
"20-40"
],
"@growthRate": [
"0",
"0",
"0"
],
"@growth": [
"0",
"0",
"0"
]
},
"warning": null,
"empty": false
},
{
"dimension": null,
"table": {
"@currentValue": [
60
],
"@compareValue": [
60
],
"@growthRate": [
"0"
],
"@growth": [
"0"
]
},
"warning": null,
"empty": false
}
],
"warning": null
},
"success": true,
"code": "200",
"errorMsg": null,
"detailErrorMsg": null,
"traceId": "cced9e32f6aa4b3eb03905b51054ad4d.192.17780531304030053"
}
6.1 Implement Drill-Down
Query Request
curl --location --request POST 'http://127.0.0.1:8083/anymetrics/api/v1/attribution/dim_peer_compare' \
--header 'auth-type: UID' \
--header 'tenant-id: tn_19487697' \
--header 'auth-value: 463663891121963008' \
--header 'Accept-Language: zh-CN' \
--header 'Content-Type: application/json' \
--data-raw '{
"metric": "countDistinct_product_id",
"dimensions": [
"price_band"
],
"attribution": {
"attributionCalculateType": "ALL",
"compareDimension": "store_type",
"timeConstraint": "DateTrunc([metric_time],\"DAY\") = DateTrunc(Cast(\"2025-07-03 00:00:00\",\"Timestamp\"),\"DAY\")",
"filters": [
"IN([brand],\"Lepin\")"
],
"currentObject": {
"filters": [
"IN([store_type],\"self-operated\")"
]
},
"compareObject": {
"filters": [
"IN([store_type],\"franchise\")"
]
}
}
}'
Response Example
{
"data": {
"tables": [
{
"dimension": "price_band",
"table": {
"@currentValue": [
1,
3,
2
],
"@compareValue": [
1,
3,
2
],
"@overallContributionRate": [
null,
null,
null
],
"price_band": [
"0-20",
"40-60",
"20-40"
],
"@growthRate": [
"0",
"0",
"0"
],
"@growth": [
"0",
"0",
"0"
]
},
"warning": null,
"empty": false
},
{
"dimension": null,
"table": {
"@currentValue": [
6
],
"@compareValue": [
6
],
"@growthRate": [
"0"
],
"@growth": [
"0"
]
},
"warning": null,
"empty": false
}
],
"warning": null
},
"success": true,
"code": "200",
"errorMsg": null,
"detailErrorMsg": null,
"traceId": "2537fe72c3f84f51892047a537d3592a.187.17780530872390023"
}