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Attribution Models - Touch-based, Correlation and Regression, Shapley-based, and Randomized field experiments

Attribution Models: Touch-based, Correlation and Regression, Shapley-based, and Randomized field experiments

This workflow is based on the book chapter "Attribution Modelling" by de Haan (2022). It replicates the analysis of the book chapter using KNIME Analytics Platform and the nodes of the R integration. After a brief overview of the data with descriptive statistics, the workflow follows the aforementioned book chapter by implementing different attribution models: from the simplest ones (i.e. first channel attribution) to more complex models like the Shapley values-based attribution method. The workflow is then completed with an interactive dashboard reporting all the visualizations produced.

Note: In order to correctly execute the workflow it is required to install R on your local machine.


Attribution ModellingThis workflow is based on the book chapter "Attribution Modelling" by de Haan (2022). It replicates the analysis of the book chapter using KNIME Analytics Platform and the nodes of the R integration. After a brief overview of the data with descriptive statistics, the workflow follows the aforementioned book chapter by running through again the different attribution models analyzed in this work, from the simplest ones (i.e.first channel attribution) to more complex models like the Shapley values-based attribution method. The workflow is then completed with an interactive dashboard reporting all the visualizations produced. Attribution modeling is an important field of research for marketers, as it allows them to give the correct credit to the contribution made by each channel/touchpoint along a customer journey. It improves marketers' ability tooutline what the most decisive channels/touchpoints are in a customer journey that concludes in a conversion (i.e., a purchase, a click, a subscription), making the budget allocation process more efficient. Descriptivestatistics Basic touch-based attribution models Correlation and Regressionattribution models Shapley valuesmodel Randomized field experiments Dashboard Data Excel Reader Shapley Value Logistic regressionmodel 3-4-5-6 Logistic regresssionmodel 1-2 Average channelattribution First channelattribution Descriptivestatistics Last Channelattribution Dashboard Visualization Linear Correlation Attribution ModellingThis workflow is based on the book chapter "Attribution Modelling" by de Haan (2022). It replicates the analysis of the book chapter using KNIME Analytics Platform and the nodes of the R integration. After a brief overview of the data with descriptive statistics, the workflow follows the aforementioned book chapter by running through again the different attribution models analyzed in this work, from the simplest ones (i.e.first channel attribution) to more complex models like the Shapley values-based attribution method. The workflow is then completed with an interactive dashboard reporting all the visualizations produced. Attribution modeling is an important field of research for marketers, as it allows them to give the correct credit to the contribution made by each channel/touchpoint along a customer journey. It improves marketers' ability tooutline what the most decisive channels/touchpoints are in a customer journey that concludes in a conversion (i.e., a purchase, a click, a subscription), making the budget allocation process more efficient. Descriptivestatistics Basic touch-based attribution models Correlation and Regressionattribution models Shapley valuesmodel Randomized field experiments Dashboard Data Excel Reader Shapley Value Logistic regressionmodel 3-4-5-6 Logistic regresssionmodel 1-2 Average channelattribution First channelattribution Descriptivestatistics Last Channelattribution Dashboard Visualization Linear Correlation

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: lenovo
Created at: 2022-08-09
On NodePit since: 2024-11-20
Last update: 2024-12-21
Created with KNIME version: v4.7.1
Tags: Marketing AnalyticsAttribution ModelsLogistic RegressionLinear CorrelationDescriptive StatisticsShapley

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