Cloud Readiness / Oracle Unity Customer Data Platform Cloud
What's New
Expand All


  1. October 2021 Update
  1. Revision History
  2. Overview
  3. Feature Summary
  4. 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
28 OCT 2021     Created initial document.

Overview

This guide outlines the information you need to know about new or improved functionality in this update.

GIVE US FEEDBACK

We welcome your comments and suggestions to improve the content. Please send us your feedback at omcdocumentation_ca@oracle.com.

Feature Summary

Column Definitions:

Report = New or modified, Oracle-delivered, ready to run reports.

UI or Process-Based: Small Scale = These UI or process-based features are typically comprised of minor field, validation, or program changes. Therefore, the potential impact to users is minimal.

UI or Process-Based: Larger Scale* = These UI or process-based features have more complex designs. Therefore, the potential impact to users is higher.

Features Delivered Disabled = Action is needed BEFORE these features can be used by END USERS. These features are delivered disabled and you choose if and when to enable them. For example, a) new or expanded BI subject areas need to first be incorporated into reports, b) Integration is required to utilize new web services, or c) features must be assigned to user roles before they can be accessed.

Ready for Use by End Users
(Feature Delivered Enabled)

Reports plus Small Scale UI or Process-Based new features will have minimal user impact after an update. Therefore, customer acceptance testing should focus on the Larger Scale UI or Process-Based* new features.

Customer Must Take Action before Use by End Users
(Feature Delivered Disabled)

Not disruptive as action is required to make these features ready to use. As you selectively choose to leverage, you set your test and roll out timing.

Feature

Report

UI or
Process-Based:
Small Scale

UI or
Process-Based:
Larger Scale*

Unity Customer Data Platform

Data Management

Additional Data Transformations for Ingest Jobs

Enabling Customers

Amazon Web Services (AWS) Inbound Connector

Intelligence at Scale

Campaign Attribution (Non-Revenue Type) Data Science Model

Channel Recommender Data Science Model

Parameters for Data Science Models

Recency, Frequency and Monetary Data Science Model

Profile Unification

Japanese Language Support in ID Resolution

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)

These additional data transformations give you more flexibility when configuring imported data in the desired format. 

Steps to Enable

You don't need to do anything to enable this feature.

Key Resources

Role Information

This feature is available to Data engineers. 

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.

The connector provides another option for transferring data between platforms. 

Steps to Enable

You don't need to do anything to enable this feature.

Key Resources

Role Information

This feature is available to Data engineers. 

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.

This model helps you measure the effectiveness of campaigns through a non-revenue attribute, allowing you to identify the best performing campaigns so that you can drive more conversions from these campaigns.

Steps to Enable

To enable this feature you need to log a Service Request (SR).

Key Resources

Role Information

This feature is available to Instance admins. 

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.

The channel recommendations from this model helps you identify the set of best channels through which you could engage with your customers, thereby increasing your customers' chances of conversion.

Steps to Enable

To enable this feature you need to log a Service Request (SR).

Key Resources

Role Information

This feature is available to Instance admins.

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

This allows you to make configurations to data science models so that they meet the specific needs of your organization. 

Steps to Enable

To enable this feature you need to log a Service Request (SR).

Key Resources

Role Information

This feature is available to Instance admins. 

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.

The recency, frequency, and monetary scores generated from this model help you optimize your segmentation and drive better marketing efforts. 

Steps to Enable

To enable this feature you need to log a Service Request (SR).

Key Resources

Role Information

This feature is available to Instance admins. 

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.

You can now leverage the profile unification capabilities of Oracle Unity to store data with Japanese characters in master entities. 

Steps to Enable

You don't need to do anything to enable this feature.

Role Information

This feature is available to Instance admins.