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AroniSmartIntelligence™: Optimized Bayesian A/B Testing Capabilities in the New Version 15.x

AroniSmartIntelligence™: A/B Bayesian Test Capabilities

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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. To support Hypothesis Testing, Design and Analysis of experiments, Marketing Campaign and Digital Marketing Assets Analyses,  and Marketing Mix  analysis, a new capability has been added to the new version 15.x of AroniSmartIntelligence ™: A/B Bayesian Test. Hence, the new AroniSmartIntelligence version will also include the critical capability. complementing the usual  frequentist hypothesis testing  and statistical inference  tools. 

A/B testing has become a key analytical methodology in machine learning  and data science across industries, in the technology in general, given its effectiveness to help test hypotheses and  get reliable analytics to support better informed decision making and drawing conclusions about any numerical data driven hypotheses. 

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™  A/B Test Capability for Machine Learning, Statistics, and Data Science.

Machine Learning, Data Science, and Statistical Inference Models have continued to fastly improve and to become the center of advanced analytics, business intelligence, decision making, management science, data science, and other areas in which sound informed and data driven decisions are needed, such as Marketing Science, including Marketing Mix Analytics, and Econometrics.  

 A/B Test Analysis  has been one of the most frequently used capabilities to address different digital marketing problems.  A/B testing, also known as split testing or bucket testing, is a statistical, data science or machine learning  method used to compares two versions of a content to determine which one performs better.  

A/B Testing leverages statistical hypothesis testing by comparing  two versions of an experiment, a recipe, an action,  a variable, a model, an algorithm, or a feature set, to determine which one performs better, under the assumptions that  all other variables or features are held constant for the two experiments, recipes or variations when performing an A/B test. In Marketing Science, the performance metrics may be increased reach, better engagement, conversion rates, increased sales.

Hence, AroniSmart™ team has been improving and optimizing the machine learning and data science techniques implemented in AroniSmartIntelligence™ (aka AroniSmartLytics ™)to  organize, search, analyze,  order, test and  find the meaningful analytical approaches to A/B Testing  in order to generate actionable insights.  The team has implemented A/B Bayesian Test capability  in version 15.x available on Apple's App Store® here.

A/B  Bayesian Test is a significant improvement on the Frequentist A/B Testing, which is close to the regular Hypothesis testing available in Statistical Inference Module of the previous versions of AroniSmartIntelligence™ (aka AroniSmartLytics ™).


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AroniSmartIntelligence, the leading tool for Advanced Analytics, Machine Learning & Data Science

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.


 AroniSmartIntelligence™ A/B Testing Approach.

With the advanced knowledge of statics probability distributions in Module 2 -  Parametric and Non Parametric Statistics and Module 3 -  Descriptive Statistics and Statistical Inference of AroniSmartIntelligence™, the statistician, researcher, business analyst, data miner, data scientist, student or Machine Learning practitioner is ready to leverage the capabilities by conducting A/B statistical testing. Using the previous modules, the user knows what probability distributions and paramenters are needed in this important area of measuring the performance of controlled experiments to identify the best option between two variations or recipes.

A/B Statistical Testing is a form of statistical hypothesis testing or a significance test, by analyzing and comparing two versions of in an experiment, two recipes,  or two items to figure out which performs better. 

A/B Statistical Testing has become important in measuring marketing campaigns or activities, especially in digital marketing or in any area where controlled experiments are needed to make informed decisions.  Given the required advanced knowledge of stastitics probability distributions,  A/B Bayesian Statistical Testing is included in AroniSmartIntelligence™ Module dedicated to Statistics Testing and Inference. However, it also requires understanding of Bayesian models, in Module 7.

In marketing science, A/B Statistical Testing is  a statistical method used to compare  two versions, or test and control labeled A and B, of marketing activities, such as  a website, app feature, e-mail, or other element. In the experiment  in which a metric is set,  users  are randomly assigned to the test or control sample.  

A/B Statistical testing is then used in analyzing which version  performs better  and by what magnitude, based on a chosen metric.  
The analysis allows to make data-driven decisions about which  Recipe or design is most effective or performs better.  Hence, in digital marketing, A/B Statistical test can be used to compare marketing metrics like visits, clicks, conversion rates, engagements, drop-off rate, time spent on website, and more.

For More on  AroniSmartIntelligence™  A/B Test Capabilities Objectives, check the next article  AroniSmartIntelligence™  A/B Test Capabilities Objectives, along with a few  examples of A/B Bayesian Test and Regular Frequentist Hypothesis Testing, executed in AroniSmartIntelligence™.

 The  new version 15.x of AroniSmartIntellligence™ app is available on Apple's AppStore®

The data requirements conditions for each model, approach, and module  are documented through messages, tool tips, and documentation. FAQs and other relevant documents can be found on the AroniSoft (http://www.aronisoft.com ) Website.

 


FOR QUICK DEMOS ON HANDBOOK and USER'S MANUALS,  PLEASE CLICK ON THE LINKS BELOW TO DOWNLOAD THE PDF FILES:


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What is next?

The new version of AroniSmartIntelligence™ that includes improved Advanced Analytics, Machine Learning and Data Science  is available on Apple's App Store®.

More detailed information on the capabilities of  AroniSmartIntelligence™ is available in the tool on Apple®'s App Store and on the AroniSoft web site(http://www.aronisoft.com).  

Check the Frequently Asked Questions (FAQ) here.

More detailed use cases, real life applications,  and AroniSmart Tools in actions  can be found on  on the AroniSmart web site (http://www.aronismart.com). Check the following tabs: ABOUT US, SOLUTIONS, SCITECH, MONEY


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