Sold by: ElectrifAi LLCÂ
Deployed on AWS
Identify customers likely to add a new line, take a MBB device, add a feature to their plans, buy a new phone, etc.
Overview
Businesses succeed when customers upgrade their accounts. This suite of propensity models identifies those customers more likely to add a new line, take a MBB device, add a feature to their plans, buy a new phone, etc., and actively engage those customers by pushing offers. After analyzing the responses, net revenue gain is likely to gain momentum.
Our machine learning models are available through a Private Offer. Please contact info@electrifai.net for subscription service pricing.
SKU: PROMO-PS-TLC-AWS-001
Highlights
- Identify customers more likely to upgrade their account and advertise to those customers accordingly.
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.4xlarge Inference (Batch) Recommended | Model inference on the ml.m5.4xlarge instance type, batch mode | $700.00 |
ml.m5.4xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.4xlarge instance type, real-time mode | $700.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $900.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $500.00 |
ml.m5.12xlarge Inference (Real-Time) | Model inference on the ml.m5.12xlarge instance type, real-time mode | $900.00 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $500.00 |
Vendor refund policy
According to contract.
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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-2021-3177Â ) has been resolved in version 1.0.1.
Additional details
Inputs
- Summary
Input: A zip file with comma separated csv files (depending on how many service usage the users are provided). Reference file: sample.zip
- Input MIME type
- multipart/form-data
https://github.com/ElectrifAi/model-aws-promotion-propensity/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-promotion-propensity/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 |
|---|---|---|---|
Input zip file contains csv files. | Details of each csv: scoring_date.csv (required), profile.csv (required), contact_records.csv (required), bill_records.csv (required), payment_records.csv (required), service1_usage_records.csv (required)**, serviceN_usage_records.csv (optional, N=2,3,4,5...**), subscription_records.csv (optional). ** service1 means the primary service provided to customer, **N=2,3,4,5,6,7...., can add any number of service usage table, follow the same schema of service1_usage_records.csv
| Type: FreeText | Yes |
Resources
Vendor resources
Support
AWS infrastructure support
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