Aggregations

Aggregations are calculated facts derived from a profile’s transaction history. They provide a real-time picture of customer value and behaviour without requiring custom queries against raw data. Aggregations are updated automatically whenever new transactions are recorded against a profile. They are stored as a separate record linked to the profile and returned alongside profile data in API responses and event contexts.

Spend

All spend values are net figures (returns and exchanges are subtracted).

Field	Description
`spend_12m`	Net spend in the rolling 12-month window, measured back from the profile’s most recent transaction date
`spend_12m_now`	Net spend in the rolling 12-month window, measured back from today
`spend_all`	Lifetime net spend
`spend_atv_12m`	Average net transaction value in the last 12 months
`spend_atv_all`	Average net transaction value lifetime
`spend_first_date`	Date of the profile’s first transaction
`spend_last_date`	Date of the profile’s most recent transaction

Shop dates and timing

These fields track the timing of shopping behaviour across unique transaction dates.

Field	Type	Description
`first_shop_date`	Date	Date of the first transaction
`first_shop_days`	Integer	Days between join date and first shop date. Can be zero.
`second_shop_date`	Date	Date of the next transaction on a unique date after the first
`second_shop_days`	Integer	Days between first and second shop date. Minimum 1.
`average_shop_days`	Integer	Average days between unique shopping dates: (latest minus first date) divided by count of unique transaction dates. Set on the third shop and recalculated on each subsequent shop.
`total_shop_days`	Integer	Total count of unique shopping days
`previous_shop_date`	Date	The prior value of `latest_shop_date`, set when a new latest shop date is established
`latest_shop_date`	Date	Date of the most recent transaction on a unique date
`latest_shop_days`	Integer	Days between `previous_shop_date` and `latest_shop_date`. Minimum 1.
`at_risk_date`	Date	`latest_shop_date` plus (`average_shop_days` multiplied by 3). Signals when a customer is overdue for their next visit.

Purchase behaviour

Field	Description
`shop_count`	Total number of unique shopping days where the net of transactions is greater than zero. Pure exchange or return days are excluded.
`shop_days`	Average number of days between transactions. Only calculated for profiles with three or more transactions.

Dimensions

Omneo calculates four behaviour dimensions from transaction history. Each uses a defined set of codes. dimension_recency: how recently the profile last shopped:

Code	Meaning
`R0`	Never shopped
`RR`	Retired (last shop more than 5 years ago)
`RL`	Lapsed (last shop 3 to 5 years ago)
`RI`	Inactive (last shop 1 to 2 years ago)
`RA`	Annual active (last shop 6 to 12 months ago)
`RS`	Seasonal active (last shop 3 to 6 months ago)
`RC`	Current active (last shop within 3 months)

dimension_frequency: how frequently the profile shops:

Code	Meaning
`F0`	Never shopped
`F1`	1 transaction ever
`F2`	2 transactions ever
`FL`	Low frequency (average shop days greater than 90)
`FM`	Medium frequency (average shop days 30 to 90)
`FH`	High frequency (average shop days less than 30)

dimension_channel: the channels the profile has shopped across:

Code	Meaning
`CC`	Concession only
`CF`	Branded only (store or outlet)
`CD`	Branded or concession only, not online
`CO`	Online only
`CS`	Outlet only
`CM`	Multichannel (branded, concession, or outlet AND online)

dimension_joined: the profile’s join lifecycle stage:

Code	Meaning
`JE`	Established (joined more than 12 months ago)
`JC`	Current (joined 3 to 12 months ago)
`JN`	New (joined within 3 months)

Preferences

Field	Description
`most_transacted_location`	Location object for the profile’s most frequently visited store
`store_join`	Location object for the store where the profile was created
`likely_country`	Inferred country based on transaction data
`purchased_brands`	Array of brand or department names the profile has purchased from

Using aggregations

Aggregation fields are available in reaction filter conditions and target templates. For example, you can fire a reaction when aggregations.spend_12m crosses a threshold, or include at_risk_date in a win-back campaign payload.

{
  "aggregations": {
    "spend_12m": 2260.00,
    "spend_12m_now": 1840.00,
    "spend_all": 8450.50,
    "shop_count": 18,
    "average_shop_days": 32,
    "latest_shop_date": "2026-03-15",
    "at_risk_date": "2026-04-16",
    "dimension_recency": "RC",
    "dimension_frequency": "FM"
  }
}

Individual aggregation fields can be manually updated or fully recalculated via the API.

Platform

Profiles

Commerce

Incentives

Visits

Lists

Feedback

Organisations

Extensions

Platform surfaces

Groups

Automation

Spend

Shop dates and timing

Purchase behaviour

Dimensions

Preferences

Using aggregations

​Spend

​Shop dates and timing

​Purchase behaviour

​Dimensions

​Preferences

​Using aggregations

​Related

Spend

Shop dates and timing

Purchase behaviour

Dimensions

Preferences

Using aggregations

Related