Sold by: ElectrifAi LLCÂ
Deployed on AWS
Improve home loan conversion via direct mail channel, including purchase and refinance transactions and conversion likelihood.
Overview
Improve home loan conversions via direct mail channels, including purchase and refinance transactions and conversion likelihood at 5 different stages of cross-sell lifecycle. Those stages include response to solicitation, authorization of credit pull, application submission, submission of all documents needed for processing, application completion and funding. Drivers that lead to drop-out are identified. Customer technology/channel preferences from external data integrated to form more holistic view of prospect behavior. To preview our machine learning models, please Continue to Subscribe. To preview our sample Output Data, you will be prompted to add suggested Input Data. Sample Data is representative of the Output Data but does not actually consider the Input Data. Our machine learning models return actual Output Data and are available through a private offer. Please contact info@electrifai.net for subscription service pricing. SKU: DCOPT-PS-CCC-AWS-001
Highlights
- Improve home loan conversions via direct mail channels, including purchase and refinance transactions and conversion likelihood at 5 different stages of cross-sell lifecycle.
- New insights applied to guide enhanced targeting strategy across different segments. A more robust machine learning channel response model powers enhanced acquisition program which drove $9.5 million in savings for top US credit card issuers.
Details
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Delivery method
Latest version
Deployed on AWS
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Vulnerability CVE-2021-3177 (i.e. https://nvd.nist.gov/vuln/detail/CVE-2021-3177 ) has been resolved in version 1.0.1.
Additional details
Inputs
- Summary
A zip file with the following comma separated csv files. Reference file: sample.zip Bureau.csv (REQUIRED) PNL.csv (REQUIRED) Campaign_Mail.csv (REQUIRED) Infobase.csv (OPTIONAL) Transaction.csv (OPTIONAL)
- Input MIME type
- application/json
https://github.com/ElectrifAi/model-aws-direct-mail-acquisition/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-direct-mail-acquisition/blob/main/sample.zip
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
A zip file with the following comma separated csv files. Reference file: sample.zip | Bureau.csv (REQUIRED)
PNL.csv (REQUIRED)
Campaign_Mail.csv (REQUIRED)
Infobase.csv (OPTIONAL)
Transaction.csv (OPTIONAL) | Type: FreeText | Yes |
Resources
Vendor resources
Support
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