Sold by: Prosper Insights & Analytics
Deployed on AWS
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Propensity model that determines the probability that a consumer is Walmart Clothing Shopper
Overview
Prosper Insights & Analytics' propensity model predicts the probability that a U.S. adult consumer is a Walmart Clothing Shopper. Based on a set of basic demographics, the model identifies individuals likely to shop at Walmart for Clothing. The model was trained with data from Prosper's large database of U.S. adult consumer intentions and actions.
Highlights
- Enhances digital and offline targeting by identifying individuals likely to be a Walmart Clothing Shopper
- 100% Privacy Compliant Models. No PII Used.
- Based on unique large sample consumer survey data.
Details
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Try this product free for 5 days according to the free trial terms set by the vendor.
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.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $500.00 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $1.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $500.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $500.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $500.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $500.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $500.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $500.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $500.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $500.00 |
Dimensions summary
You pay by the hour for the compute instance that runs the model, billed per host-hour. Pricing splits into two modes. Batch mode scores a set of records in one run. Real-time mode serves live predictions on demand. Within each mode, you pick from a range of AWS machine learning instance types. These span general-purpose, compute-optimized, and GPU-accelerated families in sizes from large through 24xlarge. Larger instances offer more compute and cost more per hour. You choose the instance and mode that fit your workload, and charges accrue only while the instance runs.
Top-of-mind questions for buyers
One host-hour is one hour that a chosen instance runs the model. Charges accrue only while that instance is active. If you stop the instance, software charges stop. Underlying AWS infrastructure fees, such as storage, may apply separately based on AWS terms.
Both meter per host-hour on the same instance families. Batch mode scores a defined set of records in one run, so you pay for the hours that run takes. Real-time mode keeps an instance running to serve live predictions on demand, billing for the whole time it stays up.
The model scores U.S. consumers on their propensity to shop for Walmart clothing. It draws on more than two decades of monthly zero-party consumer survey data covering intent, spending plans, and shopping behavior. You pay for the instance-hours used to run this scoring, not per consumer scored.
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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
Minor fixes to the underlying software.
Additional details
Inputs
- Summary
The model provides propensity estimates based on gender, age range, income range, and zip code. See the sample notebook for details concerning input variables and mappings.
- Input MIME type
- text/csv
1,5,24,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
https://prosper-sample-batch.s3.us-east-2.amazonaws.com/batch_input_basic_geo.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Gender | Integer (0, 1)
0 = Female
1 = Male | Type: Categorical
Allowed values: 0,1 | Yes |
Age Range | (Integer, 1-6)
1 = 18-24
2 = 25-34
3 = 35-44
4 = 45-54
5 = 55-64
6 = 65+ | Type: Categorical
Allowed values: 1,2,3,4,5,6 | Yes |
Household Income | (Integer, 0-24)
0 = Less than 10,000
1 = 10,000-14,999
2 = 15,000-19,999
3 = 20,000-24,999
4 = 25,000-29,999
5 = 30,000-34,999
6 = 35,000-39,999
7 = 40,000-44,999
8 = 45,000-49,999
9 = 50,000-54,999
10 = 55,000-59,999
11 = 60,000-64,999
12 = 65,000-69,999
13 = 70,000-74,999
14 = 75,000-79,999
15 = 80,000-84,999
16 = 85,000-89,999
17 = 90,000-94,999
18 = 95,000-99,999
19 = 100,000-109,999
20 = 110,000-119,999
21 = 120,000-129,999
22 = 130,000-139,999
23 = 140,000-149,999
24 = 150,000 or more | Type: Categorical
Allowed values: 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 | Yes |
Zip Code | Five digit zip code as integer.
The model requires that the zip code be replaced by a set of 25 binary variables that represent special information regarding the zip. Prosper provides a file that maps every zip code into two integer values (division and cluster). These values are then converted into a set of binary values in a manner similar to one-hot encoding. The mapping file as well as the conversion routines are provided with the sample notebook. | Type: Integer | Yes |
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