Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer is Walmart Grocery Shopper
4.3
Overview
Prosper Insights & Analytics' propensity model predicts the probability that a U.S. adult consumer shops at a specific retailer. Based on a set of basic demographics, the model identifies individuals who are likely to shop at that retailer. The model was trained with data from Prosper's large Media Behaviors & Influence (MBI) study (N=16,619).
Highlights
- Enhances digital and offline targeting by identifying individuals likely to shop at a specific retailer.
- 100% Privacy Compliant Models. No PII Used.
- Based on unique large sample consumer survey data (N=16,619).
Details
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Delivery method
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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 | $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 each machine you run this predictive model on. Pricing splits into two modes. Batch inference scores large sets of consumer data in scheduled runs. Real-time inference returns scores on demand. Within each mode, you pick an AWS SageMaker instance type. Options span general-purpose (m4, m5), compute-optimized (c4, c5), and GPU-accelerated (p2, p3) families, in sizes from large up to 24xlarge. Larger and GPU instances add processing power for heavier workloads. Your total cost depends on which instance you choose and how many hours it runs.
Top-of-mind questions for buyers
One HostHrs unit is one hour that a chosen SageMaker instance runs your model. Charges accrue per running hour. Batch jobs meter only while a scheduled run executes. Real-time endpoints meter the whole time the instance stays provisioned, even between requests, so idle real-time hours still count.
Batch inference scores large data sets in scheduled runs. You pay for the hours a job runs, then it stops. Real-time inference keeps an endpoint running to return scores on demand. It bills continuously while provisioned. Batch suits periodic scoring; real-time suits ongoing, on-demand requests.
Two factors combine: which instance type you pick and how many hours it runs. The instance type sets the hourly rate, and hours multiply that rate. GPU families (p2, p3) and larger sizes add processing power for heavier workloads. Choosing a matching instance and limiting run time controls cost.
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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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Customer reviews
Gloria S.
Convenient, Easy-to-Use Walmart Grocery Shopper
Reviewed on May 05, 2026
Review provided by G2
What do you like best about the product?
The Walmart grocery shopper is convient and easy to use.
What do you dislike about the product?
There isn't anything I dislike about the Walmart grocery shopper.
What problems is the product solving and how is that benefiting you?
The Walmart grocery shopper gets the shopping done for when I am to busy to shop myself.
Information Technology and Services
Walmart sells everything that peoples ned
Reviewed on Jul 30, 2022
Review provided by G2
What do you like best about the product?
One stop solution for everything.no need to get out and buy grocery
What do you dislike about the product?
We cannot see actual quality of products
What problems is the product solving and how is that benefiting you?
Time Saving and delivery at door step
Eduard G.
Logistics Manager
Reviewed on May 24, 2022
Review provided by G2
What do you like best about the product?
Not a huge fan of this software i dont really know
What do you dislike about the product?
lack of quality goods and thatws about it really
What problems is the product solving and how is that benefiting you?
none really im a pretty good problem solver
Recommendations to others considering the product:
Its a ok software
Lakeysha R.
Very Convenient
Reviewed on Apr 26, 2022
Review provided by G2
What do you like best about the product?
I love the convenience of the service because it saves time and is helpful.
What do you dislike about the product?
The delays that we have as customers and shoppers.
What problems is the product solving and how is that benefiting you?
Time-saving, flexibility
Melissa F.
Shopping on the mend.
Reviewed on Jan 05, 2022
Review provided by G2
What do you like best about the product?
What I like best about this software is how easy it is to use to find everything you need right there your fingertips. While I was getting better after being sick this last month for the first time I took full advantage of doing all of my shopping online usually I buy one or two things here one or two things there but it made my life so much easier while I got better I'm still getting better it's just it's made my life so much easier.
What do you dislike about the product?
The worst part about it is while searching an item that could be listed under several different categories or different names can be difficult to find that item on the website kind of like searching the aisles when you don't know where some things at lol.
What problems is the product solving and how is that benefiting you?
The main problem I was originally solving with keeping everyone else safe when I was sick and keeping me safe and my family safe by not having to go to the grocery store and I realized it really is beneficial to have people who are willing and able to deliver to you and an app that is so reliable
Recommendations to others considering the product:
Enjoy