The data mining It is an empirical method, which, while compiling large volumes of data such as big data with the help of algorithms, artificial intelligence and statistical programs, effectively evaluates them. Various mathematical and statistical models are used.
A typical goal of data mining for e-commerce is to establish typical shopping carts to align accordingly with products. In this way, data mining is a way of make the most of online commerce on a scientific basis. Data can be clearly visualized after being generated and processed through information visualization.
Context
Data plays an increasingly important role in digital commerce and in the optimization of sales processes. In theory, each online store can compile a large amount of data about customers, customer behavior, products, and shopping behavior. But the large amount of data alone does not ensure that sales increase or that sales methods can be optimized. Data mining is supposed to is the solution to this.
Much like a mining company surveying the soil for valuable minerals, data mining programs sift through the data to find important and relevant data. The objective is to draw the essential conclusions from the data that make more efficient sales or visitor behavior.
Unlike conventional control, data mining provides not only the ability to establish the current situation of a company, but also the predictions for future situations. This can be set, for example, with the NeuroBayes software. Due to the vast amount of data, these forecasts are not based on experience, but solely on empiricism, artificial intelligence, and statistics. Filtering detailed information for data analysis is typically based on drilldown functions.
Implementation
Several methods are used in data mining which are briefly described below:
- Model-based analysis:
The first hypotheses and a special environment for these assumptions are established. The rules for data analysis can be derived in summary from these preconditions. These can be simple conditions such as "if ... then" or complex sequences of various conditions that can go all the way to neural networks.
- Access to databases:
Before starting the data mining procedure, access to existing databases must be secured. This can be done comfortably using interfaces. At the same time, the existing data is segmented and integrated into its own databases. Data mining by this approach can be done, for example, with Google Analytics.
- The search process:
If data mining generates solutions, it is the task of these programs to find the best possible solutions using the appropriate methods.
- Determination of interest:
Each pattern found must be analyzed and classified in terms of its relevance to the respective business processes in data mining. One method of measuring the level of interest, for example, is to evaluate results that differ from the rule.
Application areas
Data mining can have different purposes. On the one hand, model forecasts can be made using this method and, on the other, it also serves to describe or clarify certain facts.
Explanatory models are usually used for the analysis of shopping carts for conversion optimization. At the same time, these models offer the opportunity to identify the success factors of an online store or a website.
Other application objectives are:
- Buyer Profiling for Affiliate Marketing
- Market segmentation
- Forecast of hiring periods
- Product price forecast
- Predicting demand for a product
- Diagnosis of failures in sales processes
Limitations
While the data examined as part of data mining provides many different approaches, that is exactly where the difficulty usually lies. First of all, it is essential determine relevant and realistic objectives to simply receive data results that ensure greater efficiency.
For example, an online store can establish which products are most often bought in combination, but this does not determine if the store requires a new long-term cross-selling strategy, probably because the period in which the data was collected and evaluated is too long. short and there were seasonal preferences in the choice of product.
In principle, data mining is an objective form of take advantage of data analysis. However, this is usually considered a weakness, since algorithms and statistical models have to be defined by people first. At this point, individual ideas and wishes can falsify the empirical objective result. That is why it would be advisable to use external agencies or workers who are not directly related to the company for data mining.
SEO benefits
Data mining can also be used for daily SEO work. With tools like keyword planners, relevant keyword data can be used to fine-tune content. In this circumstance, Google data would be used to choose the correct keywords based on a favorable forecast (traffic, competition). In this way, they could achieve many conversions possible if the website in question is classified accordingly.[1] Definitely, web analytics tools also work with data mining techniques. In this way, this method is closely related to the analysis of the web portal.
