Integration Overview
Retail Next provides information on visitors to your brick & morter stores so that you can understand foot traffic and in-store conversion.
The Daasity<>Retail Next integration provides data on customers moving throughout a retail location to provide information on user flow in and out of retail locations. Data is reported at an hourly level.
ERD
ENDPOINTS
| Locations API | https://<susbcription>.api.retailnext.net/v1/location |
| Datamine API (Traffic) | https://<subscription>.api.retailnext.net/v1/datamine |
LOCATIONS
|
JSON Element |
Database Column |
|
id |
location_id |
|
name |
name |
|
type |
type |
|
parent_id |
parent_id |
|
timezone |
timezone |
|
store_id |
store_id |
|
address.street_address |
address |
|
comp_start_date |
start_date |
Incremental data extraction
|
Business Rule |
Value |
|
Load type |
TRUNCATE & REPLACE |
|
Extraction Frequency |
Daily |
Sync Key definition:
MD5 Hash of the following fields
- location_id
- __source_id
TRAFFIC TABLE
|
JSON Element |
Database Column |
|
MD5(location_id ||’:’||start_date ||’:’||hour_begin) |
traffic_id |
|
locations (location_id being passed in the API request) |
location_id |
|
date_ranges.first_day (first day being passed in the API request) |
start_date |
|
date_ranges.last_day (last day being passed in the API request) |
end_date |
|
data.group.start |
hour_begin |
|
data.group.finish |
hour_end |
|
data.value |
traffic |
Incremental data extraction
|
Business Rule |
Value |
|
Load type |
Upsert |
|
Extraction Frequency |
Daily |
|
API Example |
{"locations": ["location_id"], "metrics": ["traffic_in"], "date_ranges": [{"first_day": "2020-01-15", "last_day": "2020-01-15"}], "time_ranges": [{"type": "store_hours"}], "group_bys": [ {"group": "time", "value": 1, "unit": "hours"} ]} |
Sync Key definition:
MD5 Hash of the following fields
- traffic_id
- __source_id
Copyright Daasity, Inc 2020