Cloud Readiness / Oracle Unity Customer Data Platform Cloud
New Feature Summary
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  1. October 2021 Update
  1. Revision History
  2. Overview
  3. Unity Customer Data Platform
    1. Data Management
        1. Additional Data Transformations for Ingest Jobs
    2. Enabling Customers
        1. Amazon Web Services (AWS) Inbound Connector
    3. Intelligence at Scale
        1. Campaign Attribution (Non-Revenue Type) Data Science Model
        2. Channel Recommender Data Science Model
        3. Parameters for Data Science Models
        4. Recency, Frequency and Monetary Data Science Model
    4. Profile Unification
        1. Japanese Language Support in ID Resolution

October 2021 Update

Revision History

This document will continue to evolve as existing sections change and new information is added. All updates appear in the following table:

Date Product Feature Notes
25 OCT 2021     Created initial document.

Overview

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Unity Customer Data Platform

Data Management

Additional Data Transformations for Ingest Jobs

Additional ready-to-use data transformations are available when configuring ingest jobs. These data transformations format imported data before being stored in the Oracle Unity data model. 

The following data transformations are now available:

  • Substring
  • Replace
  • Padding
  • Trim
  • Prefix-Suffix
  • Regex Advanced (enables regular expression data transformations)

Enabling Customers

Amazon Web Services (AWS) Inbound Connector

The AWS inbound connector allows you to import data from an AWS S3 bucket into Oracle Unity. 

Intelligence at Scale

Campaign Attribution (Non-Revenue Type) Data Science Model

The ready-to-use Campaign Attribution (Non-Revenue type) model measures the effectiveness of campaigns by assigning a percentage attribution value to each campaign. The model calculates the 'Attribution Percentage' as a percentage value of campaigns converted to total conversions for each individual campaign. All touch points that contributed to the conversion of the campaign are considered.

Channel Recommender Data Science Model

The ready-to-use Channel recommender data science model ranks engagement channels for every customer in any instance based on the likelihood of conversions.

Parameters for Data Science Models

When creating ready-to-use data science models, you can define parameters that allow you to customize the model algorithm.

These parameters include the following depending on the algorithm you choose:

  • Next best action catalogs for the Next best action model
  • Next best offer catalogs for the Next best offer model
  • Campaign attribution types (multi-touch Revenue and multi-touch Non-Revenue) for the Campaign attribution model
  • Lookback window for the Lead score, Product propensity, and Campaign attribution models

Recency, Frequency and Monetary Data Science Model

The ready-to-use Recency, Frequency and Monetary (RFM) data science model categorizes user profiles into different personas and generates RFM scores based on the recency, frequency, and monetary values of their engagement.

Profile Unification

Japanese Language Support in ID Resolution

Oracle Unity now supports Japanese characters when performing identity resolution on data. This includes support for normalizing across different forms of Japanese Kana, as well as normalizing of wide and narrow Japanese characters.