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The basis of the modern marketing of a business entity is to know the behavior of its customers. Advanced artificial intelligence methods, such as data mining and machine learning methods, penetrate data analysis. The application of these methods is most appropriate in the case of online sales of any goods in large quantities and various industries. They are very often used in the sale of electronics, PCs or clothes. However, it is also possible to apply them to the agricultural industry, not only in B2C, but also in B2B in the sale of seeds, agricultural products, or agricultural machinery. Appropriate combinations of offers and knowledge of customers can bring the selling entity higher profits or competitive advantages. The main goal of our study is to design a CRISP-DM process model that will enable small businesses to analyze online customers' behavior. To reach the main goal we perform a data analysis of the online sales data by using machine learning methods as clustering, decision tree and association rules mining. After evaluating the proposed model, we discuss its use of the proposed model in the field of internet sales in the agricultural sector.

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Purpose This study aims to analyze the impact of newly created brand awareness on customer’s buying behavior in online environment. Design/methodology/approach The authors analyzed more than 280,000 online customer journeys from four e-commerce stores based in Slovakia. Within the results of the interaction analysis of individual customer journeys, the authors determined three criteria based on the level of theoretical brand awareness. The purpose was to determine their occurrence in real-world data. Findings It was found that each of the specified criteria accounts for the significant share of the company’s revenues. Based on these criteria and the level of their occurrence, the authors introduced the term direct traffic effect. Research limitations/implications Because of the available Web analytics tools, the data might be imprecise because of data collection issues. There is also ambiguity in the interpretation of the customer journey. Practical implications The company can build awareness among prospective customers by offering them a positive customer experience during the first interactions online. Data proved that customer will not only repeatedly visit the website from the direct traffic source but also his customer journey will end with the purchase of the company’s products. Originality/value This paper fulfills the need for further research on the impact of multi-channel marketing on brand awareness and consumer behavior, respectively.

The knowledge of high-value customers provides the possibility to make decisions ensuring profitability of the company. By analyzing and optimizing a buyer's journey, companies can better understand their customers and optimize marketing costs in the way that will generate a higher return on investment. The primary objective of this paper is to define the current state of multichannel attribution and, based on the literature, study and analyze the data regarding the buyer's journey of high-and low-value customers of selected e-commerce business. To accomplish the main objective of our study, we retrieved and analyzed top conversion paths from Google Merchandise Store, the e-commerce website selling goods branded by Google, with the use of Markov chains and heuristic models. A difference between high-and low-value customers regarding the acquisition by marketing channels before the purchase was found. Moreover, it was found that high-value customers' journeys consist of more interactions compared to those of low-value customers.

Let P 1 , . . . , P n be properties of graphs. A (P 1 , . . . , P n )-partition of a graph G is a partition of the vertex set V (G) into subsets V 1 , . . . , V n such that the subgraph G[V i ] induced by V i has property P i ; i = 1, . . . , n. A graph G is said to be uniquely (P 1 , . . . , P n )-partitionable if G has exactly one (P 1 , . . . , P n )-partition. A property P is called hereditary if every subgraph of every graph with property P also has property P. If every graph that is a disjoint union of two graphs that have property P also has property P, then we say that P is additive. A property P is called degenerate if there exists a bipartite graph that does not have property P. In this paper, we prove that if P 1 , . . . , P n are degenerate, additive, hereditary properties of graphs, then there exists a uniquely (P 1 , . . . , P n )-partitionable graph.

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