Sold by: MphasisÂ
Deployed on AWS
The solution predicts which customers are more likely to subscribe to term deposit with a bank in response to a marketing campaign.
Overview
This solution identifies bank customers who are more likely to sign up for a term deposit with the bank in response to the bank’s marketing campaign. The solution consists of a pretrained model that analyzes a combination of campaign features and customer characteristics to make predictions about term deposits. The solution can be utilized by marketing departments at banks to analyze the effectiveness of their direct marketing campaigns.
Highlights
- This solution can be utilized to identify customers who are more likely to sign up for a term deposit in the future in response to the marketing campaign undertaken by the bank’s marketing department. The bank can then use this analysis to predict the effectiveness of new campaigns
- The solution can also help identify a section of the customers that are more likely to sign up for the term deposit which can be useful for the bank for future customers
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!
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.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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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
This is version 3.1.
Additional details
Inputs
- Summary
- Supported content type - csv
- The required columns are – age, marital, education, balance, housing, loan, contact, day, month, duration, campaign, pdays, previous, poutcome
- Input MIME type
- text/csv, text/plain, application/zip
https://github.com/Mphasis-ML-Marketplace/Bank-Marketing-Campaign-Analyzer/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Bank-Marketing-Campaign-Analyzer/tree/main/input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
age | age in years | Type: Integer | Yes |
marital | marital status | Type: Categorical
Allowed values: married, single, divorced | Yes |
education | education background | Type: Categorical
Allowed values: secondary, tertiary, primary, unknown | Yes |
balance | Balance held by the individual in the account with the bank | Type: Integer | Yes |
housing | housing loan flag | Type: Categorical
Allowed values: yes, no | Yes |
loan | personal loan flag | Type: Categorical
Allowed values: no, yes | Yes |
contact | contact communication type | Type: Categorical
Allowed values: unknown, cellular, telephone | Yes |
day | last contact day of the week | Type: Categorical
Allowed values: mon, tue, wed, thu, fri | Yes |
month | last contact month of year | Type: Categorical
Allowed values: may, jun, jul, aug, oct, nov, dec, jan, feb, mar, apr, sep | Yes |
duration | last contact duration, , in seconds | Type: Continuous | Yes |
Resources
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