Integration Specifications
This article will help you learn about how Daasity replicates data from RetailNext, limitations to the data we can extract and where the data is stored in the RetailNext schema
Integration Overview
RetailNext provides information on visitors to your brick & mortar stores so that you can understand foot traffic and in-store conversion.
The Daasity RetailNext integration provides data on the flow of customers in and out of retail locations.
Integration Availability
This integration is available for:
Enterprise
API Endpoints
The Daasity RetailNext extractor is built based on this RetailNext API documentation. The following endpoints are used by Daasity to replicate data from RetailNext:
Entity Relationship Diagram (ERD)
Click here to view the ERD for the Daasity Retail Next integration illustrating the different tables and keys to join across tables
Retail Next Schema
The Daasity RetailNext extractor creates these tables using the endpoints and replication methods listed. The data is mapped from source API endpoint to the table based on the mapping logic outlined in each table.
Locations
Endpoint: Location
Update Method: TRUNCSERT
Table Name: [
retailnext.locations]
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
source_id
__source_id
MD5(id + source_id)
__sync_key
Daasity: timestamp when loaded into DB
__synced_at
Traffic
Endpoint: Datamine
Update Method: UPSERT
Table Name: [
retailnext.traffic]
MD5(location_id + start_date + traffic_in::group::start)
traffic_id
location_id
location_id
date_ranges::first_day
start_date
date_ranges::last_day
end_date
traffic_in::group::start
hour_begin
traffic_in::group::finish
hour_end
traffic_in::value
traffic_in
traffic_out::value
traffic_out
source_id
__source_id
MD5(location_id + start_date + traffic_in::group::start + source_id)
__sync_key
Daasity: timestamp when loaded into DB
__synced_at
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