How Marketing Automation is transformed by AI and Data Science

This post was originally published on this site

You already know Machine Learning

Machine Learning is already used by many big companies. You might have experienced its potential when using Netflix. It provides you with movies recommendations based on Machine Learning itself. Facebook, Spotify, Google Maps or Uber are also taking advantage of this technology on a daily basis.


SALESmanago Copernicus Machine Leadrning& AI engine

SALESmanago Marketing Automation has developed its own AI engine – SALESmanago Copernicus Machine Learning&AI. Just now companies such as New Balance, Yves Rocher and Sizeer are using it to provide their customers with tailored and intelligently personalized content.


Copernicus is one of the most advanced recommendation engines across MA systems. It’s designed especially for eCommerce to guarantee real-time personalization and segmentation. It allows:


  • Big Data Analysis with most advanced machine learning frameworks and algorithms
  • Predictive product recommendations
  • Real-time omnichannel personalization
  • Gather and analyze all data in one platform
  • Report, analyze and improve results


Marketing Automation is now used to assign Lead Scoring, create Lead Nurturing campaigns, Workflows, Automation Rules, to run segmentation and in overall campaigns. The problem here is a huge amount of data to be analyzed.


The data analyzed in SALESmanago include:

  • Website visits
  • Products bought
  • Products added to cart
  • Conversion paths
  • Conversion sources
  • Buyers personal and demographical data (CRM)
  • Purchased products’ attributes
  • Reactions to direct marketing
  • Search terms used
  • Chat conversations
  • Products displayed
  • Cart value
  • Offline behavior


Today’s technology is based on following instructions, algorithms. It is a simple mechanism performing actions deriving from our primary input. Machine Learning is a completely new approach. Similarly to a human being its learning potential is coming from experience or to be exact from continuous gathering and analyzing data.


Types of Machine Learning

Supervised Machine Learning

This kind of learning is possible when inputs and outputs are clearly identified and algorithms are trained using labeled examples.

Regression – predicts the continous-response value.

Classification – predicts the categorical response value where the data can be separated into specific “classes”. It’s potential is based on learning from examples.


Unsupervised Machine Learning

Unlike Supervised Machine Learning, Unsupervised learning is used with data sets without historical data. An unsupervised learning algorithm explores surpassed data to find the structure.

Clusteting – grouping similar elements together.

Association – the goal is to identify rules that define large portions of the data.


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