Data Mining

Data mining is an empirical method to not only collect large volumes of data such as Big Data with the help of algorithms, artificial intelligence, and statistics programs, but to evaluate it effectively. Various mathematical and statistical models are utilized.

A typical goal of data mining for e-commerce is to determine typical shopping carts to align to products accordingly. Data mining is therefore a way to optimize online trading on a scientific basis. The data can be displayed clearly after being generated and processed by means of information visualization.

Background

Data plays an increasingly important role in digital trade and in the optimization of sales processes. Each online shop can theoretically collect a lot of data about customers, customer behavior, the products, and purchase behavior. But the large amount of data alone does not ensure sales are increased or that sales methods can be optimized. Data mining is supposed to be the answer to this.

Similar to a mining company that surveys the soil to find valuable minerals, data mining programs sift through data to find important and relevant data. The aim is to draw necessary conclusions from the data which make the sale or visitor behavior more efficient.

In contrast to conventional controlling, data mining provides not only the ability to determine the current situation of a company but also predictions for future situations. This can be determined for example with NeuroBayes software. Because of the immensely large amount of data, these forecasts are not based on experience, but solely on empiricism, artificial intelligence, and statistics. Filtering of detailed information for data analysis is usually based on drill-down functions.

Implementation

Various methods are used in data mining which are briefly described below:

• Analysis based on models:

First hypotheses are established and a special solution environment for these assumptions. Rules for data analysis can finally be derived from these prerequisites. These may be simple conditions such as “if ... then” or complex sequences of various conditions that can range up to neural networks.

• Access to databases:

Before starting the data mining process, access to existing databases must be secured. This can be conveniently done using interfaces. At the same time, the existing data is segmented and integrated into their own databases. Data mining by this approach can be done, for example, with Google Analytics.

• The search procedure:

If data mining generates solutions, it is the task of these programs to search these solutions for the best possible solutions using appropriate methods.

• Determining of interest:

Each pattern found must be analyzed and classified in terms of its relevance for the respective business processes in data mining. A method to measure the level of interest, for example, is to examine results that differ from the norm.

Areas of application

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 explain certain facts.

Explanatory models are often used for the analysis of shopping carts for conversion optimization. Moreover, these models offer the possibility to identify factors in the success of an online shop or a website.

Further application objectives are:

• Creating of buyer profiles for affiliate marketing
• Market segmentation
• Forecast contract periods
• Forecast of product prices
• Predicting the demand of a product
• Fault diagnosis in sales processes

Limitations

Although the data examined as part of data mining provides many different approaches, that is precisely where the difficulty often lies. It is first important to set relevant and realistic targets to actually receive data results that will ensure greater efficiency.

For example, an online store can determine which products are most often bought in combination, but this does not determine if the shop requires a new cross-selling strategy for the long-term, possibly because the period in which the data was collected and evaluated is too short and there were seasonal preferences in product choice.

In principle, data mining is a very objective way to take advantage of data analysis. However, this is often regarded as a weakness, since algorithms and statistical models have to be defined by people first. At this point, individual ideas and desires may falsify the empirical objective result. That’s why it would be advisable to use external agencies or employees who are not directly related to the company for data mining.

Benefit for search engine optimization

Data mining can also be used for daily SEO work. With tools like keyword planners, keyword relevant data can be used to adjust content. In this case, Google data would be used to select the right keywords based on a favorable prognosis (traffic, competition). Thus, many possible conversions could be achieved if the website in question is ranked accordingly. Of course, web analytics tools work with data mining techniques as well. Thus, this method is closely related to website analysis.