Sold by: MphasisÂ
Deployed on AWS
This solution predicts Return on Advertising Spend (ROAS) for media channels based on previous data using Bayesian Marketing Mix solution.
Overview
Marketing Mix Models help understand the efficiency of marketing spend on sales via different advertising channels like print media, social media, radio, television, etc. This solution quantifies the impact of advertising spend based on historical data. The insights generated help advertisers identify the right marketing channels and make informed budget allocations.
Highlights
- This solution takes weekly marketing spends on multiple media channels as input. It attributes the direct effect of channel on revenue or sales. This solution models the impact of advertising by considering media spend saturation and lagged effects. Developed on bayesian methodology, the model processes the markerting and non-marketing activites to calculate the return on advertising spend (ROAS).
- This solution accounts for non-marketing activites and quantifies the effect of marketing inputs. The solution performs time series modeling to understand patterns such as trend and seasonality of sales or returns. Given the total spend and spend share across media channels, the solution outputs effective contribution towards revenue or sales and ROAS for each advertising channel.
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!
Details
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Delivery method
Latest version
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.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $0.00 |
ml.m5.large Training Recommended | Algorithm training on the ml.m5.large instance type | $16.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $0.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $0.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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Vendor terms and conditions
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the 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
This is version 3.1
Additional details
Inputs
- Summary
input_zip.zip contains the input data. The file contains two .csv files:
- input_variables.csv : This file contains four columns: 1. Direct control variables 2. Media variables 3. Start analysis index 4. End analysis index
- weekly_advertisement_data : The file contains the data for the variables defined in the input_variables.csv file.
- Limitations for input type
- 1. Input should be in zip format and name should be input_zip.zip. 2. input_zip.zip should contain 2 .csv files. Name of the csv files should be input_variables.csv and weekly_advertisement.csv.
- Input MIME type
- text/csv, application/zip, application/gzip
https://github.com/Mphasis-ML-Marketplace/Return-on-Advertising-Spend-calculator/blob/main/input/input_zip.zip
https://github.com/Mphasis-ML-Marketplace/Return-on-Advertising-Spend-calculator/blob/main/input/input_zip.zip
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
weekly_advertisement_data.csv | weekly_advertisement_data.csv must contain two mandatory columns: target KPI (revenue or sales) and date column. Other columns in weekly_advertisement_data.csv depends on the number of input variables defined in input_variables.csv file | Type: Continuous | Yes |
Resources
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