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AI-generated from documented ETRM metadata — verify critical details on the linked pages.

Overview

AMS_DM_PARTY_ATTRIBUTES_V is a seeded database view owned by the APPS schema in Oracle E-Business Suite, delivered as part of the Oracle Marketing (AMS) product family. Its documented purpose is to serve as the data source for the seeded data mining functionality in Oracle Marketing, projecting analytical attributes for parties (customers, prospects, and organizations) defined within the Trading Community Architecture (TCA). The view consolidates financial, marketing-interaction, order, service-request, and contract-related measures into a single denormalized row per party, enabling Oracle Marketing's data mining and predictive scoring engines to evaluate and segment the party population without joining numerous operational tables at runtime.

Because it is a view rather than a table, no data is physically stored. The view is a read-only projection intended for consumption by the data mining framework and by custom reporting and extraction processes that require a consolidated party-level analytical profile. In ETRM 12.2.2 the object is documented as VALID and is registered in the APPS schema alongside the other marketing data mining objects.

Underlying Base Objects

Per the documented ETRM 12.2.2 metadata, the view is defined over two referenced base objects, both exposed as synonyms in the APPS schema:

• AMS_DM_PARTY_DETAILS (SYNONYM) — supplies the core party-level attributes, including the demographic, financial, marketing-history, order, service-request, and contract measures enumerated in the view text.
• AMS_DM_PARTY_DETAILS_TIME (SYNONYM) — supplies the time-relative attributes, such as days-since and rolling-period aggregates (for example, TIMES_TARGETED_3_MONTHS, TOT_ORDER_AMOUNT_6_MONTHS).

The view text joins the two sources on PARTY_ID (aliased D and SEEDED_TARGET) and produces one row per party, combining the static and time-windowed measures into a unified result set.

Key Columns

The view exposes a broad set of columns. Among the most significant:

• PARTY_ID — the primary TCA party identifier and the join key across the underlying objects.
• THIRD_PARTY_FLAG — indicates whether the party is classified as a third party; directly relevant to the user's search term and commonly used to filter or segment the party population.
• Financial attributes — GROSS_ANNUAL_INCOME, NET_WORTH, TOTAL_ASSETS, TOTAL_ASSETS, RETAINED_EARNINGS, NET_PROFIT, COGS, and related balance-sheet and income measures.
• Targeting history — NUM_TIMES_TARGETED, LAST_TARGETED_CHANNEL_CODE, TIMES_TARGETED_MONTH, TIMES_TARGETED_3/6/9/12_MONTHS, and channel-specific counts (EMAIL, TELEMKT, DIRECT).
• Order and sales measures — AVG_ORDER_AMOUNT, TOT_ORDER_AMOUNT_YEAR, TOT_NUM_ORDERS_3/6/9/12_MONTHS, RETURN_BY_VALUE, ONTIME_SHIP_PCT, PAYMENTS.
• Service-request measures — NUM_OF_SR_YEAR, OPEN_SRS, CLOSED_SRS, ESCALATED_SRS, REOPENED_SRS, PCT_FIRST_CLOSED_SRS, and average resolution times.
• Contract measures — CONTRACTS_CUML, CONTRACT_AMT, CONTRACT_DURATION, OPEN_CONTRACTS, RENEWED_CONTRACTS.

A NULL SEEDED_TARGET column is also selected, used internally to align the detail and time sources.

Common Use Cases and Queries

Typical usage involves extracting party profiles for data mining model training, segmentation, or ad hoc analysis. A common query filters on the third-party flag to isolate non-third-party parties:

• SELECT PARTY_ID, GROSS_ANNUAL_INCOME, NUM_TIMES_TARGETED, AVG_ORDER_AMOUNT FROM APPS.AMS_DM_PARTY_ATTRIBUTES_V WHERE THIRD_PARTY_FLAG = 'N';
• SELECT party_id, times_targeted_12_months, days_since_last_targeted FROM APPS.AMS_DM_PARTY_ATTRIBUTES_V WHERE num_times_targeted_12_months > 0 ORDER BY times_targeted_12_months DESC;
• SELECT party_id, tot_order_amount_year, num_of_sr_year, open_srs FROM APPS.AMS_DM_PARTY_ATTRIBUTES_V WHERE tot_order_amount_year > 0;

These queries support marketing analytics, campaign prioritization, and profile enrichment. Because the view aggregates across multiple operational domains, it is well suited to bulk extraction into analytical or data-mining environments rather than transactional reporting.