Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer is a Business Mobile App User.
Overview
This post-pandemic Propensity Model determines the probability that a US adult is a Business Mobile App User. Lift over Random 1.59. This post-pandemic Propensity model is one of a series of consumer classification models based on data from over 17,000 US adults surveyed in 2021 from Prosper's US Media Behaviors & Influence study. Survey data was collected 9 months after the National Covid-19 Coronavirus Emergency was declared, capturing consumer behavior changes and preferences. The survey is anonymous. Zero PII. CCPA and HIPAA Compliant.
Highlights
- Enhances digital and offline targeting by identifying an individual’s probability to engage in a specific behavior. Model is based on data from over 17,000 US adults surveyed in 2021 from Prosper's US Media Behaviors & Influence study.
- 100% Privacy Compliant Models. No PII Used.
- Lift over Random 1.59
Details
Sold by
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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 |
|---|---|---|
ml.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $500.00/host/hour |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $500.00/host/hour |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $500.00/host/hour |
ml.m5.24xlarge Inference (Batch) | Model inference on the ml.m5.24xlarge instance type, batch mode | $500.00/host/hour |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $500.00/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $0.02/request |
Dimensions summary
You pay based on usage, not a fixed subscription. Five dimensions bill by host hours for batch inference, where the model scores your data in bulk. Each maps to a different compute instance size, from ml.m4.xlarge up through ml.m5.24xlarge. Larger instances offer more processing capacity per hour. You pick the instance that fits your workload and pay for the hours it runs. A sixth dimension bills per inference request through inference.count.m.i.c, charging by the number of scoring calls made rather than by time. These two approaches let you match cost to how you run the model.
Top-of-mind questions for buyers
One host hour is one hour that a chosen compute instance runs a batch scoring job. The model processes your data in bulk during that time. You are billed per hour the instance stays active, whether it is an ml.m4.xlarge or an ml.m5.24xlarge.
The five host-hour instance dimensions and the per-request inference.count.m.i.c dimension bill independently. Batch host hours accrue by running time. Per-request charges accrue by the number of scoring calls made. You are charged only for the mode you actually run, and both can appear together if you use both.
Batch host-hour dimensions score large datasets in one run, so cost tracks how long the instance runs. The per-request dimension charges by individual scoring calls, which suits scoring records on demand. Pick host hours for bulk jobs and per-request for smaller, ongoing calls.
www.prospermodelfactory.com+1
Helpful?
Vendor refund policy
No refunds
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
None
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 |
Support
Vendor support
Prosper Model Factory & SageMaker
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.
Similar products
Identify and Engage Consumers Focused on Fitness, Nutrition, and Preventive Wellness Using AI-Modeled Propensities.
Data Axle's Borrowers, Spenders & Investors Audiences combine verified PII with AI-modeled financial behaviors to identify individuals likely to borrow, spend, or invest. Scored 0-1 for intent, these audiences support precise targeting across credit, banking, investment, and fintech use cases. Prebuilt and custom segments available.
Propensity model that determines the probability that a consumer is a Business Owner.
Propensity model that determines the probability that a consumer is a Business Purchaser Influencer.
Identify telecom customers likely to churn and mitigate customer churn by contacting customers for each marketing campaign.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.