Sold by: ElectrifAi LLC
Deployed on AWS
Identify customers most likely to purchase discounted upgrades to travel in premium cabins leading to decrease in flight revenue.
Overview
Identify customers most likely to purchase discounted upgrades to travel in premium cabins rather than purchasing premium cabins at full fare, leading to decrease in flight revenue. Block the sale of discounted upgrades to prevent/reverse dilutionary behavior by hiding the discounts that may be available to low-risk customers. Identifies customers most likely to complain after a disruption/service failure and proactively reach out to them, improving customer satisfaction and reducing the number of complaint-related calls made to the call center. 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: DILPV-PS-AIR-AWS-001
Highlights
- Identify customers most likely to purchase discounted upgrades to travel in premium cabins rather than purchasing premium cabins at full fare, leading to decrease in flight revenue, and block sale of discounted upgrades to such customers to prevent/reverse dilutionary behavior.
- Expand customer share of wallet by stimulating customers to attach flight (paid for seating) and non-flight ancillary products (hotels and cars) to every flight booking. Powered by personalized hotel and car recommendations determined in real-time and served through multiple booking channels (online and call center). Technical highlights include leveraging all historical customer information, such as booking and travel history and up-sell/cross-sell product purchases.
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Deployed on AWS
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.p2.16xlarge Inference (Real-Time) Recommended | Model inference on the ml.p2.16xlarge instance type, real-time mode | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.m5.large Inference (Batch) | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.p2.xlarge Inference (Real-Time) | Model inference on the ml.p2.xlarge instance type, real-time mode | $0.00 |
ml.p3.16xlarge Inference (Real-Time) | Model inference on the ml.p3.16xlarge instance type, real-time mode | $0.00 |
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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.
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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 containing comma separated csv files (3 Required , 1 Optional). Reference file: sample.zip passenger_info.csv (REQUIRED): flight_booking_history.csv (REQUIRED): upgrade_history.csv (REQUIRED): transaction_history.csv (OPTIONAL):
- Input MIME type
- application/json
https://github.com/ElectrifAi/model-aws-dilution-prevention-airlines/blob/master/sample.zip
https://github.com/ElectrifAi/model-aws-dilution-prevention-airlines/blob/master/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: A zip file containing comma separated csv files (3 Required | passenger_info.csv (REQUIRED):
flight_booking_history.csv (REQUIRED):
upgrade_history.csv (REQUIRED):
transaction_history.csv (OPTIONAL): | Type: FreeText | Yes |
1 Optional). Reference file: sample.zip | passenger_info.csv (REQUIRED):
flight_booking_history.csv (REQUIRED):
upgrade_history.csv (REQUIRED):
transaction_history.csv (OPTIONAL): | Type: FreeText | Yes |
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Marta S.
A Look into My Stake in the Startup World
Reviewed on Mar 04, 2024
Review provided by G2
What do you like best about the product?
The most effervescent facet of Dilution Prevention in my investment contracts occasionally is relinquishing the apprehension I feel. Having ownership equals with the tickets being run out by subsequent funding rounds to finance the business, it means I can be focused on the success of the organization and not bother about being relieved of control.
What do you dislike about the product?
One major disadvantage of DILUTION PREVENTION is that seeking out future investors can become more difficult. Among investors some would not be willing to commit their capital if they are worried that their voices are limited or even barred by pre-agreements. Ensuring the right degree of protection for the shareholders meanwhile ensuring a suitable degree of freedom for the investors can be a very tough business.
What problems is the product solving and how is that benefiting you?
Dilution Prevention eliminates one of the main challenges in the startup ecosystem and fosters an environment of security and predictability in the volatile world of early-stage businesses. It grants me the right to take up new and rising future business promotions with more assertion, with the assurance that the initial investment will be covered, in the dream of stronger returns due to the success of the company.