Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer is Comfortable Going to Beauty Salons & Barber Shops
Overview
This Pandemic Propensity Model determines the probability that a US adult is Comfortable Going to Beauty Salons & Barber Shops. Lift over Random 1.32
This Pandemic Propensity model is one of a series of consumer classification models based on data from over 31,000 US adults surveyed between April to July 2020 from Prosper's US Monthly Consumer survey. Survey data was collected during the Covid-19 Coronavirus Pandemic, capturing behavior changes and preferences during the National Emergency. 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 31,000 US adults surveyed between April to July 2020 from Prosper's US Monthly Consumer survey.
- 100% Privacy Compliant Models. No PII Used.
- Lift over Random 1.32
Details
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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.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $350.00 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $0.28 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $350.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $350.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $350.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $350.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $350.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $350.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $350.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $350.00 |
Dimensions summary
You pay by the hour for the compute instance that runs this consumer propensity model. Pricing splits into two usage modes: Batch, which scores data in scheduled runs, and Real-Time, which returns predictions on demand. Each mode offers the same set of instance types across four families. The m4 and m5 general-purpose instances suit balanced workloads. The c4 and c5 compute-optimized instances suit processing-heavy jobs. The p2 and p3 GPU instances suit accelerated work. Within each family, larger sizes (xlarge up to 24xlarge) add more capacity and cost more per hour. You choose the size to match your workload.
Top-of-mind questions for buyers
One HostHrs unit is one hour of running the chosen instance type. Charges accrue for each hour the instance is active while running the model. You are billed per instance-hour, so total cost equals your hourly rate multiplied by the hours the instance runs.
Batch mode scores data in scheduled runs, so instance-hours accrue only while a batch job runs. Real-Time mode keeps an instance running to return predictions on demand, so hours accrue continuously while the endpoint stays active. Batch suits periodic scoring; Real-Time suits always-on prediction needs.
Software charges meter running instance-hours only. A stopped or inactive instance does not accrue software charges. For Batch mode, hours stop once the job finishes. For Real-Time mode, hours keep accruing until you stop the endpoint. Underlying AWS storage or resource fees may still apply separately.
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Delivery details
Amazon SageMaker model
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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 | 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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Similar products
Zip Propensity Data is derived from Prosper's US Monthly Consumer survey. The survey data is modeled to Zip Code level. Source data was collected in July 2020, during the Covid-19 Coronavirus Pandemic, capturing behavior changes and preferences of over 7,800 adults during the National Emergency. Zero PII. CCPA and HIPAA Compliant. Data file lists the Percentage Propensity by Zip for the answer "Comfortable Going to Beauty salons/Barber shops."
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Propensity model that determines the probability that a US adult is Comfortable Going to Concerts
Zip Propensity Data is derived from Prosper's US Monthly Consumer survey. The survey data is modeled to Zip Code level. Source data was collected in July 2020, during the Covid-19 Coronavirus Pandemic, capturing behavior changes and preferences of over 7,800 adults during the National Emergency. Zero PII. CCPA and HIPAA Compliant. Data file lists the Percentage Propensity by Zip for the answer "Comfortable Going to Concerts."
Zip Propensity Data is derived from Prosper's US Monthly Consumer survey. The survey data is modeled to Zip Code level. Source data was collected in July 2020, during the Covid-19 Coronavirus Pandemic, capturing behavior changes and preferences of over 7,800 adults during the National Emergency. Zero PII. CCPA and HIPAA Compliant. Data file lists the Percentage Propensity by Zip for the answer "Comfortable Going to the Gym."
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