E-commerce data science is a field of data science that deals with extracting knowledge from data in the area of digital commerce. To do this, large amounts of data, also known as "big data," are analyzed and "smart data" is extracted from it (data mining). This relevant data is then used via artificial intelligence (machine learning) processes to derive recommendations for action in e-commerce. This usually involves predictions about purchasing behavior (predictive analytics) with regard to specific personalization measures. That is why our e-commerce data science is an important component of our e-commerce technology for the continuous development of personalization.
For our e-commerce data science, the click and purchase behavior of your digital commerce is essential , as is your product catalog. In addition, other data can also be included, e.g., from:
Our data science team analyzes daily events in digital commerce and familiarizes itself with the business areas and products.
Our data science team uses the knowledge gathered from the field of consumer psychology to develop new theories that lead to even better personalization.
Our data science team then translates the theories developed into the design of our AI engine. A specific goal is set, which is to be optimized within a roughly defined framework. This allows us to quickly determine which theory is best and which parameter settings to use.
We use A/B testing to verify the effectiveness of the theory developed with regard to the status quo. Here, we carry out statistical measures to achieve a high degree of decision-making accuracy.
The tested theory becomes part of our personalization platform and is available to you for the complete personalization of your online shop.
Data mining involves exploratory data analysis. We use statistical methods to gain valuable insights from the data. To do this, we use classical and Bayesian statistics.
This is a quantitative data analysis. It analyzes the occurrence of a variable under certain conditions, such as the average sales amount. This allows us to check our theories within the A/B tests for randomness or patterns.
Here, the probability between two elements is calculated, such as the probability of rain when a black cloud appears (calculation of composite probabilities). In other words, if someone buys product A, how likely is it that they will buy product B? This allows us to test our theories within A/B tests for randomness or patterns.
Artificial intelligence is at home in the field of machine learning. Methods and techniques from supervised and reinforcement learning are used to enable the AI engine to learn independently.
An input variable and a target variable are defined here. The algorithm must get from the input variable to the target variable. To do this, it is given examples to learn how to achieve this goal.
Learning by doing, followed by adjustment based on the result. This is how the algorithm learns what works best.
Our e-commerce data science uses predictive analytics to forecast future events based on historical data. To do this, a mathematical model is created that captures important trends. The right combination of data mining and machine learning techniques, combined with a high degree of consumer psychology, enables our data science team to make valuable predictions for the future using predictive analytics. This allows our data science team to continuously develop our AI engine.
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