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.
Pricing Optimization
By:
Latest Version:
1.0.1
An approach to optimize prices and monetize unused pricing power, the model identifies SKUs and stores to grow margins with price increases.
Product Overview
Optimize prices and monetize unused pricing power. The Continuous Price Recommender uses a three-pronged approach to identify inelastic SKUs and stores to grow margins with price increases. The Point of Sale Price Elasticity Model calculates the price sensitivity of each store, department and SKU as necessary. The Customer Behavior Analysis breaks down transaction-level basket contents, size and shopping patterns. Demographics and Competitor Intensity Analysis, Multidimensional Clustering and Price Recommendations contribute to the model's analysis. 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: PRCEO-PS-RET-AWS-001
Key Data
Version
Type
Model Package
Highlights
Optimize prices and monetize unused pricing power. The Continuous Price Recommender uses a three-pronged approach to identify inelastic SKUs and stores to grow margins with price increases.
The Demographics and Competitor Intensity Analysis and Multidimensional Clustering and Price Recommendations also contribute to the model's analysis. Previous use of this model has generated a $50MM incremental annual margin. It implemented a quantitatively rigorous, scalable pricing procedure. It created a better understanding of customer reactions to price changes. It also facilitated a deeper understanding of customer shopping behavior and local business environment for each store, which can inform other business decisions.
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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 5 comma separated csv files (4 required, 1 optional). Reference file: sample.zip target_week.csv (required) sales_summary.csv (required) store_summary.csv (required) promotion_summary.csv (required) competitor_pricing.csv (optional)
Input MIME type
application/jsonSample input data
Output
Summary
A list of JSON objects containing the fields listed below. Reference file: sample.zip.out fiscal_week_id: Week ID of the fiscal year store_id: Store id item_id: Item involved in the offer optimized_price: Suggested price of the item.
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
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Support Information
AWS Infrastructure
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