Spend Journey
The Spend Journey dataset captures customer movement patterns relative to a primary Business Location. It identifies where customers traveled before and after visiting a location within a defined time window — enabling analysis of complementary shopping behavior, competitive traffic patterns, and customer journey paths.
All data is scoped to a Business Location and a specific reporting period, with journey connections determined by the window_hours parameter.
Schema
business_location_id
String
No
UUID of the primary Business Location
04212c19-3cc2-46ea-8c70-b11079bfd7fb
start_at
Date
No
Start date of the reporting period (inclusive)
2025-07-01
end_at
Date
No
End date of the reporting period (inclusive)
2025-07-31
window_hours
Integer
No
Time window in hours used to identify connected journey visits
24
previous_locations
Array of JSON
Yes
Locations visited before this location within the time window, ordered by transaction_pct descending
See below
next_locations
Array of JSON
Yes
Locations visited after this location within the time window, ordered by transaction_pct descending
See below
secondary_next_locations
Array of JSON
Yes
Alternative or secondary next locations representing additional journey paths
See below
Journey Location Object
Each entry in previous_locations, next_locations, and secondary_next_locations follows this structure:
business_location_id
String
UUID of the connected Business Location
470c139d-b5f5-40e8-be27-f51db4c4be5b
name
String
Name of the connected Business Location
H-E-B
street_address
String
Street address of the connected Business Location
10718 Potranco Rd
transaction_pct
Float
Share of the primary location's transactions from customers who made this journey connection (0.0–1.0)
0.0377
customer_pct
Float
Share of the primary location's unique customers who made this journey connection (0.0–1.0)
0.0377
revenue_pct
Float
Share of the primary location's revenue from customers who made this journey connection (0.0–1.0)
0.0377
Example
Notes
window_hours — Defines the time frame used to identify connected visits. For example, a value of 24 means any visit to another location within 24 hours before or after the primary location visit is considered part of the same journey. A shorter window captures tighter, same-trip behavior; a longer window captures broader shopping patterns.
previous_locations and next_locations — Both arrays are ordered by transaction_pct descending, so the highest-traffic journey paths appear first. Use these to identify the most common origin and destination locations for a Business Location's customers.
transaction_pct — Represents the share of the primary location's customers who also visited the connected location within the time window. A value of 0.0377 means approximately 3.8% of customers made that journey connection.
customer_pct — Represents the share of the primary location's unique customers who also visited the connected location within the time window. A value of 0.0377 means approximately 3.8% of unique customers made that journey connection.
revenue_pct — Represents the share of the primary location's revenue attributed to customers who also visited the connected location within the time window. A value of 0.0377 means approximately 3.8% of total revenue came from customers who followed that journey path.
Nested arrays — All three journey arrays are nested JSON and should be unnested or exploded when ingesting into a data warehouse for row-level analysis.
secondary_next_locations — Captures alternative or less direct next-location paths. This array may be empty for locations with limited downstream journey data.
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