Media Advertising, Omni-Channel and Attribution Analysis?

With the latest advances in high technology, marketing and advertising have been going through a revolution. Advertising to consumers has changed signifincantly. The touchpoints drive consumers to  compare and make purchase decisions based on the messages they receive on their smart TV, smartphones,  desktops, laptops, and tablets.

The shopping decisions are executed at digital omni-channels and  retailers, and translate into actual conversions and  sales. Marketers, agencies, and  companies have been investing in paid media advertising,  including digital display, online video, paid search, paid social, websites and earned media,  and emails  to increase engagement, downloads, signups, customer acquisition, retention and penetration, and ultimately conversions and sales.

Dominance Analysis and Attribution Models.

Increasing key questions have been around how to know which tactics and the consumer journey that  better drives purchasing decisions and sales. Answering the questions help marketers and media strategists to know  not only which marketing campaigns are the most effective, but also the synergies that drive sales. With that information,  marketers and decisions makers will have better insights on  where to focus marketing investments and drive brand and sales growth.

To get such insights, attribution modeling has been developed and used  to  measure the contribution of each user touch-point  in driving conversions or purchases. That is where advanced analytics and machine learning, especially advanced analytics tools, like AroniSmartLytics™ (AroniSmartIntelligence™),  come in.  For that purpose, AroniSmartLytics™ will soon include a module called Dominance Analysis to address Attribution modelling.

Attribution models, as implemented in AroniSmartLytics™,  help,  first, to identify which ad campaigns and marketing channels actually influenced consumers’ buying decision and second, to quantify their effectiveness and contribution, which marketing channels and ads are working and which ones are not leading to sales. This requires to follow the consumer journey across channels, devices or touchpoints and to understand the conversion funnel.

AroniSmartLytics, the leading tool for Attribution Models and Dominance Analysis.

AroniSmartLytics™ Attribution methodology is based on analyzing all the potential subsets of tactics, touchpoints, and channels to  understand all of the different paths a consumer may take to a conversion or a sale. This approach is known as Dominance Analysis

With the  Dominance Analysis approach, AroniSmartLytics™  helps uncover which tactic dominates other, partially, completely or generally.   A tactic and touchpoint or channel conditionally dominates others, and hence has more attributed sales or conversions if the average additional contribution within each model size is greater than that of the others',  

When a tactic and touchpoint or channel on average has higher contibution than others, it is said to generally dominate others.    One predictor, that is a tactic and touchpoint or channel is said to completely dominate another if its additional contribution to each of the subset models that form the basis for comparison is consistently greater than that of the other predictor.   

Attribution models or dominance analysis will be implemented in the Regression, Econometrics and Time Series module of AroniSmartLytics™ of the upcoming version, soon available to AroniSmartLytics ™ or AroniSmartIntelligence™ users.

Statisticians, Data Scientists, Marketers, 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.

AroniSmartLytics™ is a leading advanced analytics, machine learning and data science tool, with optimized cutting edge statistics models, Big Data and Text Analytics.

AroniSmartLytics™ includes modules covering machine learning and Big Data mining, Unstructured Text Analysis, Sentiment and Emotion Analytics, Bayesian Statistics and other advanced analytics.

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