Data plays a central role in e-commerce. In order to optimize the sales process, online stores strive to collect and analyze user data. Using analysis tools, figures and indicators can reveal the purchasing behavior of consumers, ranging from the products they place in their online shopping cart to the products viewed. But a mountain of data alone is of no interest: to be able to provide added value, its information must be analyzed. The data should be reviewed, especially when the objective is to optimize its sales methods. One of these methods of analysis is called data mining.
What is data mining?
In general, data mining is associated with Big Data. Big Data is all the data that can no longer be managed manually, because of their size. The processing and analysis of data must be performed using computerized methods. Data mining is considered a sub-step of the process called Knowledge Discovery in Databases (KDD). There are the following processes:
- The choice of database
- Preprocessing, in order to initiate data cleaning
- Their transformation into the form suitable for their treatment
- The mathematical analysis process (data mining)
- Interpretation of the analysis results
The knowledge that could have been acquired with KDD is an integral part of the strategic positioning of any online business model, as well as the marketing decisions that result from it. The fields of application are characterized by their multiplicity.
Data mining offers the opportunity to scientifically optimize e-commerce sites . The large databases which must be processed in the field of online commerce can thus serve as a basis for forecasts. Once this data has been worked out and statistics have been established, online store administrators can draw up a list of key success factors to exploit and implement different strategies. Thus, data mining aims to:
- Segment the markets
- Analyze the contents of the baskets
- Develop typical buyer profiles
- Calculate product prices
- Determine the duration of contracts
- Analyze the request
- Identify errors in the sales process
The different methods of data mining
To be able to extract the data relevant to a company among their abundance, different methods are implemented. These techniques are based on the identification of logical links between different patterns and trends, in order to establish statistics.
- Outlier Detection: in the field of statistics, outliers are observations or values that are qualified as “distant”. That is to say that different observations which have been made concerning the same phenomenon will strongly contrast with the values measured beforehand. In data mining, the detection of outliers is a common method of trying to identify credit card fraud or other fraudulent transactions.
- Analysis of typologies: typologies are clusters, that is to say a grouping. This method consists in segmenting a group of people. Thus, it is possible subsequently to group them by types of individuals. The objective of this type of analysis is to segment unstructured data. For this, algorithms are applied. The algorithms review the amounts of data, find structural similarities, and thus identify different clusters. If some data cannot be classified, it may fall into the category of outliers. Cluster analysis is mainly used in order to be able to determine the different typical profiles of site visitors, particularly in e-commerce.
- Classification: while the analysis of typologies primarily allows the identification of new groups, classification is an excellent means for categorizing predefined groups. Their distribution is based on different overlapping specificities. The most common method for automatically classifying data is to use a decision tree. Thus, a specificity will be identified for each data node.
- Association technique: this method aims to identify coherent sets in a specific dataset. In the field of e-commerce, this data mining method is applied in order to discover the correlations between different products in types of baskets. For example: “if product A is purchased, there will be interest in product B”. This technique therefore makes it possible to make relevant product recommendations to site visitors.
- Regression analysis: regression is a set of statistical methods. This model aims to explain a random variable using different non-random variables. The most well-known regression model is the linear regression model, which makes it possible, for example, to predict the sales of a product by correlating the price of the product in question with the median income of e-site customers. trade.
The limits of data mining
Of course, statistics come into play in data mining, and their objective analysis makes it possible to establish an analysis of existing data. But the different choices of the analytical methods implemented are nevertheless subjective, which may skew the results. The same goes for the choices applied to algorithms and parameters. The most effective way to ensure relevance and to ensure that the results are not biased is to use an external provider specialized in data mining. T
he consistency and relevance of the data analyzed is also a determining criterion for ensuring the quality of the results obtained through data mining. If the results of the analysis are unconvincing, there is a good chance that this is linked to an unqualified database. This is why it is often necessary in data mining to sort and work the data beforehand., so as not to bias the results by taking into account superfluous data.
Finally, it is important to take into account the fact that data mining results are made up of patterns and connections. The answers can only be made when thinking has been brought and that the objectives have been identified.