AroniSmartIntelligence ™ is one of the most advanced analytics tools with Machine Leaning, Data Science, Statistics, Econometrics, NLP Text and Sentiment analysis, Neural Network, and Support Vector Machine capabilities. Many users have been asking questions on how to use the tool for some key modeling needs. One of the increasing predictive modeling needs focus Marketing Mix Analytics (MMx) and Multi Touch Attribution (MTA).
The following sections covers the approach to use AroniSmartIntelligence to build the two types of models: MMx and MTA.
AroniSmartIntelligence ™, available on Apple's App Store®, is intended, very useful, and handy for experts, professionals, and college and graduate students in Statistics, Machine Learning, Quantitative Business Analytics, Data Science, Econometrics, STEM, Operations Research, and other related fields. It includes a detailed Handbook and a well crafted and instructive User's Manual. It is a cutting-edge analytical tool for advanced analytics and developing actionable insights without the hassle of coding. For more visit here AroniSmartIntelligence™: Optimized Machine Learning, Advanced Analytics, and Data Science Capabilities Including Econometrics, Bayesian Models, Neural Network Models, Marketing Mix Models, and NLP Analytics
AroniSmartIntelligence™ Marketing Mix and Attribution Predictive Analytics?
Machine Learning and Data Science Models are fastly improving and becoming the center of advanced analytics, business intelligence, econometrics, decision making, management science, data science, and other areas in which sound informed decisions are needed. Among the critical applications advances in modelling are Marketing Mix Analytics and Multitouch Attribution models.
AroniSmartIntelligence has a dedicated module and advanced capabilities to address key requirements and meet the needs for more reliable Marketing Mix Analytics and Multitouch Attribution models. The two types of modeling approaches are a combination of multivariate regressions and econometrics models, critical for evaluating the impact of various marketing tactics on sales and conversions and to forecast the impact of the future sets of tactics on marketing performance and sales.
AroniSmartIntelligence™ Marketing Mix Models and Multitouch Attribution Capabilities.
Marketing Mix Analytics (MMx) as a tool in measuring the business impact and ROI (DPC) of marketing drivers and campaigns, enables more informed resource allocation decision making and inform forecasting. In general, several statistical methods and tools are used during the Marketing Mix Models and Multitouch Attribution modeling process. AroniSmartIntelligence has modules and capabilities to account for all the required steps.
Some models, especially those related to Multitouch Attribution, require data with identify resolution and using other AroniSmartIntelligence modules and capabilities to account for additional marketing and non-marketing drivers, including both addressable and non addressable marketing factors (for some information on identity resolution solutions, see here https://www.g2.com/categories/identity-resolution, here https://www.nielsen.com/news-center/2021/nielsen-rolls-out-id-resolution-system-for-global-attribution-measurement or here https://www.transunion.com/blog/what-is-identity-resolution).
Econometrics Models controller contains a series of multivariate regression models used in econometrics and predictive analytics. Econometrics Models include 2 types: Linear Models and Non Linear Models.
The linear econometrics models included are: Count Data (Poisson, Negative Binomial), Logistic Regression, Logit, Probit, Tobit, Interval, Weighted Least Squares, Ordinary Least Squares (OLS).
Some Econometrics and Predictive Models, such as Logistic Regression, and OLS may be run outside Econometrics Models module. Each Econometrics or predictive model may have its own options that should be set before running the model.
Same for Probit where the user needs to choose among the following distributions: Binary, Ordered, and Random Effects, or Logit where the choices are: Binary, Ordered, and Multinomial.
The non linear econometrics models included are: General Non Linear Models, Generalized Logit Models, Generalized Quadratic Models, Multiplicative Models and LogLog Models.
Econometrics Models module also includes a data transformation capability.
With the capability it is possible to transform both marketing variables having carry over effects and other variables, to get variables more suitable to modeling and capturing econometrics dynamics and assumptions. Customized transformation, including log, common statistics such as mean, mode, median, standardized variables, etc. or based on customized expression may be done before running the models.
During the transformation of marketing variables, set up some parameters, including decay rate, build weeks, saturation and threshold. A configuration file is created and can be saved for a manual edit. The edited configuration file needs then to be loaded, before running the actual file transformation.
Econometrics and Marketing Mix Models DataTransformation and Model Running Steps
- Load a dataset in Aroni format, through the Welcome Module
- Navigate to Regression, Econometrics & Time Series
- Select Econometrics sub module
- Select Variables Transformation.
- Select variables (Dependent, Time, Media and Other Marketing, Non Marketing and Cross-Section
- Select the parameters
- Click on Create Configuration, to create the configuration file. The configuration file shows in the output browser
- Click Save the Configuration, to save the configuration file
- Open and manually Edit the Configuration file, as needed.
- Click Load Configuration, then Set Parameters.
- Click Run Transformation to create the transformed data set. Save the dataset for further Econometrics modeling.
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Statisticians, Data Scientists, Business and Financial Analysts, Savvy Investors, Engineers, Researchers, Students, Teachers, Economists, Political Analysts, and most of the practitioners use Advanced Analytics to answer questions, to support informed decision making or to learn.
AroniSmartIntelligence™ is a leading Advanced Analytics, Machine Learning and Data Science tool, with optimized cutting edge Statistics models, Econometrics, Big Data and Text Analytics.
AronismartIntelligence™ includes modules covering Machine Learning, Big Data mining, Bayesian Statistics, Neural Network Models, Unstructured Text Analysis, Sentiment and Emotion Analytics, and other advanced analytics.
Econometrics and Marketing Mix Model Running Steps
- Load the transformed dataset in Aroni format, through the Welcome Module
- Navigate to Regression, Econometrics & Time Series
- Select Econometrics sub module
- Run the model: Once the model is setup, click “Run Model” button to run the model. For Hierarchical Mixed Effects Models, click Ok button in the Mixed Effects Model Specifications window.
- Output Viewer. The text viewer that occupies the right center is where all the statistics produced in the sub-module are output. The data shown in the output may include ANOVA tables, data, predictions, the statistical tests, including covariance, F-test, AIC/BIC values, R-Squared, MAPE, p-values, etc. The Output Viewer is editable and can be cleaned by highlighting and deleting a small or large portion. The users may also add their own comments. The controls on the right allow easy navigation.
Econometrics and Marketing MultiTouch Attribution Models Running Steps.
For Marketing MultiTouch Attribution (MTA), like the Marketing Mix Models (MMx), it is required to perform the transformation of the marketing variables, setting up some parameters, including decay rate, build weeks, saturation and threshold. Then execute the following steps:
- Once the configuration file is created, save it and manually edit it as needed
- Load the edited configuration file and run the actual file transformation.
- Save the transformated file.
- Load the transformed file in the Welcome view.
- Run the MTA model as needed, usually in Logistic Regression module.
See Examples in the figures below:
Fig 1: Marketing Mix Data Transformation.
Fig 2: Marketing Mix Configuration File.
Fig 3: Load Marketing Mix Transformed Data.
Fig 4: View Marketing Mix Transformed Data.
Fig 5: Select Marketing Mix Model.
Fig 6: Setting up Marketing Mix Transformed Data - Warning on the number of variables for Model Sets Analysis.
Fig 7: Setting up Marketing Mix Model
Fig 8: Marketing Mix Model Results - Data.
Fig 9: Marketing Mix Model Results - Model.
Fig 10: Marketing Mix Model Results - Initial Coefficients.
Fig 11: Marketing Mix Model Results - Solution Coefficients.
Fig 12: Marketing Mix Model Results - Final Results Coefficients.
Fig 13: Marketing Mix Model Results - Final Elasticities.
Fig 14: Marketing Mix Model Results - Parameters Coefficients.
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