Sold by: ElectrifAi LLCÂ
Deployed on AWS
Predict the customer's remaining lifetime value (in months) based on their status in the customer lifecycle.
Overview
Predict the customer's remaining lifetime value (in months) based on their status in the customer lifecycle. The model recommends the best action to maximize the customer lifetime value. Previous usage of this model generated $100MM annual incremental revenue. Using the data input of customer information (including account profile data, account status data, billing/payments data, customer service data, and rate plan classification data), the model outputs an estimated customer's remaining lifetime value ($) given each action. 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: CLTVO-PS-TLC-AWS-001
Highlights
- Predict the customer's remaining lifetime value (in months) based on their status in the customer lifecycle. The model recommends the best action to maximize the customer lifetime value.
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
Input: A zip file with the following comma separated csv files. Reference file: sample.zip account_profile.csv (required) service1_usage.csv (required) serviceN_usage.csv (optional, N=2,3,4,5...) payment.csv (required) billing.csv (required) subscription.csv (required) cost.csv (required)
- Input MIME type
- application/json
https://github.com/ElectrifAi/model-aws-customer-lifetime-value/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-customer-lifetime-value/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 comma separated csv files. Reference file: sample.zip | account_profile.csv (required)
service1_usage.csv (required)
serviceN_usage.csv (optional, N=2,3,4,5...)
payment.csv (required)
billing.csv (required)
subscription.csv (required)
cost.csv (required) | Type: FreeText | Yes |
Resources
Vendor resources
Support
AWS infrastructure support
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