Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Customer Lifetime Value Model
By:
Latest Version:
1.0.1
Predict the customer's remaining lifetime value (in months) based on their status in the customer lifecycle.
Product 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
Key Data
Version
Type
Model Package
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.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$0.00/hr
running on ml.p2.16xlarge
Model Batch Transform$0.00/hr
running on ml.m5.2xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$16.56/host/hr
running on ml.p2.16xlarge
SageMaker Batch Transform$0.461/host/hr
running on ml.m5.2xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.p2.xlarge | $0.00 | |
ml.p2.16xlarge Vendor Recommended | $0.00 | |
ml.p3.16xlarge | $0.00 |
Usage Information
Model input and output details
Input
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/jsonSample input data
Output
Summary
A list of JSON objects containing the fields listed below. Reference file: sample.zip.out cust_needstate: Customer segmentation status_change: Action segmentation scoring_month: scoring month lifetime_value: predicted lifetime value
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
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Support Information
AWS Infrastructure
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