Sold by: MphasisÂ
Deployed on AWS
This solution outputs optimal allocation of marketing spend across media channels based on past performance.
Overview
This Solution suggests optimal allocation of marketing budget across channels like print media, social media, radio, television, etc. based on their past performance using bayesian based market budget allocation method. Alongside, the solution accounts for the effect of non-marketing attributes- such as timeseries and contextual factors- on sales/revenue while recommending optimal budget allocation. The solution compares pre and post budget allocation of each marketing channel and computes predicted target KPI (revenue/ sales).
Highlights
- This solution helps organizations measure advertising effectiveness and suggest budget allocation across marketing channels to maximize the return on investment. This inturn helps to recommend optimal budget allocation for each media channel. The solution accounts the impact of time series factors such as trend and seasonality of sales while performing budget optimisation.
- The solution understands the effect of advertising efforts as two components: First, Carryover effect that accounts for delayed consumer response. Second, Diminishing return effect to account for the saturation of advertizing spend on a media channel. The output chart compares the revenue impact of budget allocation: Average past spendings in each channel as current budget allocation Vs. Optimised Budget allocation.
- 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. time_period (in weeks) 4. Budget (in currency)
- weekly_advertisement_data.csv : 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, text/plain, image/png
https://github.com/Mphasis-ML-Marketplace/Optimal-Media-Mix-Budget-Allocation/blob/main/input/input_zip.zip
https://github.com/Mphasis-ML-Marketplace/Optimal-Media-Mix-Budget-Allocation/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
Vendor resources
Support
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