Sold by: ElectrifAi LLCÂ
Deployed on AWS
An approach to optimize prices and monetize unused pricing power, the model identifies SKUs and stores to grow margins with price increases.
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
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.
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.p2.16xlarge Inference (Real-Time) Recommended | Model inference on the ml.p2.16xlarge instance type, real-time mode | $0.00 |
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge 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 |
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 |
Vendor refund policy
This product is offered for free. If there are any questions, please contact us for further clarifications.
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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-2021-3177 ) has been resolved in version 1.0.1.
Additional details
Inputs
- 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/json
https://github.com/ElectrifAi/model-aws-pricing-optimization/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-pricing-optimization/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: A zip file with 5 comma separated csv files (4 required | target_week.csv (required)
sales_summary.csv (required)
store_summary.csv (required)
promotion_summary.csv (required)
competitor_pricing.csv (optional) | Type: FreeText | Yes |
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) | Type: FreeText | Yes |
Resources
Vendor resources
Support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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