Machine Learning, Data Mining, Big Data, Predictive Analytics …. not just slogans, but the craft of our e-commerce data science. Our data scientists are using their know-how to optimize our Recommendation Engine every day to generate relevant recommendations. Theories for the online shop and the e-mail marketing are developed, tested and implemented.
Data Mining
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Data mining is about explorative data analysis. We use statistical methods to gain valuable insights from the data (big data).
Machine Learning
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In the field of machine learning, artificial intelligence is at home. Methods and techniques from Supervised and Reinforcement Learning are used to make our Recommendation Engine self-learning.
Clasical statistics
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This is a quantitative data evaluation. It is analyzed the occurrence of a quantity, under certain conditions, such as, the average amount of sales. Thus, we check our theories within the A/B tests for randomness or pattern.
Supervised Learning
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An input variable and a target variable are defined. The algorithm must rise from the input variable to the target size. To this end, he is given examples to learn how to achieve this goal.
Bayessche statistic
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Here, the probability is calculated between two elements, for example, the probability that it will rain when a black cloud occurs (computation of compound probabilities). In other words, when someone buys product A, how likely is it that he is buying product B? Thus, we check our theories within the A/B tests for randomness or pattern.
Reinforcement Learning
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Learning by doing with subsequent adjustment, based on the result. This is how the algorithm learns what works best.
The right combination of techniques and methods in the field of data mining and machine learning, combined with a high degree of consumer psychology, enable us to make valuable predictions for the future (Predictive Analytics). As a result, our e-commerce data science team is continually developing our recommendation engine.
The data are obtained in real time from the click and purchase behavior in the online shop and the e-mail. However, further data can also be incorporated, for example from:
The Data Science team is closely connected to the Customer Care team. Even if you do not speak directly to the Data Science team, they follow the tickets and help with advice and action. The expertise flows into general settings and the customizing.
Professor
CPO & Founder
Zahlenschubser
Data Scientist
Spyder Man
Data Scientist