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. |
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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)
Amazon Web Services (AWS) Inbound Connector
The AWS inbound connector allows you to import data from an AWS S3 bucket into Oracle Unity.
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.
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.