Deployed on AWS
Machine Learning based solution that predicts whether a visitor to an online store will make a purchase or not.
Overview
This sophisticated machine-learning solution is designed to predict whether a user will complete a purchase during an online session. This model leverages advanced ensemble learning techniques to provide highly accurate predictions by analyzing user behavior and session data. This allows businesses to make informed decisions and optimize their strategies effectively. With its high accuracy, real-time predictions, and scalability, the model provides valuable insights into user behavior, enabling businesses to make smarter decisions and enhance the customer experience. The model particularly benefits e-commerce platforms looking to enhance customer experience and increase sales. By predicting purchasing intentions, businesses can proactively engage with users who are likely to make a purchase, offering personalized recommendations, discounts, or support to convert potential customers.
Highlights
- This model helps in boosting sales conversions by predicting which visitors are most likely to make a purchase, allowing you to tailor your marketing efforts and special offers effectively.
- It enhances customer experience by understanding visitor's behavior to provide personalized shopping experiences, improving customer satisfaction and loyalty and it integrates seamlessly with your existing systems, ensuring quick and smooth implementation without disrupting your operations.
- Need more machine learning, deep learning, NLP and Quantum Computing solutions. Reach out to us at Harman DTS.
Details
Categories
Delivery method
Latest version
Deployed on AWS
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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/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $200.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $5.00 |
ml.m6g.xlarge Inference (Real-Time) | Model inference on the ml.m6g.xlarge instance type, real-time mode | $5.00 |
ml.c6g.2xlarge Inference (Real-Time) | Model inference on the ml.c6g.2xlarge instance type, real-time mode | $5.00 |
ml.m6g.large Inference (Real-Time) | Model inference on the ml.m6g.large instance type, real-time mode | $5.00 |
ml.r5.large Inference (Real-Time) | Model inference on the ml.r5.large instance type, real-time mode | $5.00 |
ml.r5.4xlarge Inference (Real-Time) | Model inference on the ml.r5.4xlarge instance type, real-time mode | $5.00 |
ml.c6g.xlarge Inference (Real-Time) | Model inference on the ml.c6g.xlarge instance type, real-time mode | $5.00 |
ml.c5.large Inference (Real-Time) | Model inference on the ml.c5.large instance type, real-time mode | $5.00 |
ml.m6g.2xlarge Inference (Real-Time) | Model inference on the ml.m6g.2xlarge instance type, real-time mode | $5.00 |
Vendor refund policy
We do not provide any usage-related refunds at this time.
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
Feature updates and bug fixes
Additional details
Inputs
- Summary
The model accepts JSON data as input, which includes features like Administrative,Informational,ProductRelated,Administrative_Duration,Informational_Duration,ProductRelated_Duration,BounceRates,ExitRates,PageValues,SpecialDay,Month,OperatingSystems,Browser,Region,TrafficType,VisitorType,Weekend
- Input MIME type
- application/json
https://github.com/HDTS-user/online-purchase-intention-prediction/tree/main/input
https://github.com/HDTS-user/online-purchase-intention-prediction/tree/main/input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Administrative | Number of pages visited by the visitor about account management
| Type: Integer
Minimum: 0 | Yes |
Informational | Number of pages visited by the visitor about Web site, communication and address information of the shopping site
| Type: Integer
Minimum: 0 | Yes |
ProductRelated | Number of pages visited by visitor about product related pages
| Type: Integer
Minimum: 0 | Yes |
Administrative_Duration | Total amount of time (in seconds) spent by the visitor on account management-related pages | Type: Continuous
Minimum: 0 | Yes |
Informational_Duration | The total amount of time (in seconds) spent by the visitor on informational pages
| Type: Continuous
Minimum: 0 | Yes |
ProductRelated_Duration | The total amount of time (in seconds) spent by the visitor on product-related pages
| Type: Continuous
Minimum: 0 | Yes |
BounceRates | Average bounce rate value of the pages visited by the visitor | Type: Continuous
Minimum: 0 | Yes |
ExitRates | Average exit rate value of the pages visited by the visitor | Type: Continuous
Minimum: 0 | Yes |
PageValues | Average page value of the pages visited by the visitor
| Type: Continuous
Minimum: 0 | Yes |
SpecialDay | The "Special Day" feature indicates the closeness of the site visiting time to a specific special day. The value of this attribute is determined by considering the dynamics of e-commerce such as the duration between the order date and delivery date. e.g. for Valentine’s day, this value takes a nonzero value between February 2 and February 12, zero before and after this date unless it is close to another special day, and its maximum value of 1 on February 8. | Type: Continuous
Minimum: 0 | Yes |
Resources
Support
Vendor support
Business hours email support marketplaceSupp@harman.com
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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