The paper sheds light on the use of a self-learning GNG neural network for identification and exploration of the purchasing behaviour patterns. The test has been conducted on the data collected from consumers aged 60 years and over, with regard to three product purchases. The primary data used to explore the purchasing behaviour patterns was collected during a survey carried out among the elderly students at the Universities of Third Age in Slovenia, the Czech Republic and Poland, in the years 2017–2018. Finally, a total of six different types of purchasing patterns have been identified, namely the ‘thoughtful decision’, the ‘sensitive to recommendation’, the ‘beneficiary, the ‘short thoughtful decision’, the ‘habitual decision’ and ‘multiple’ patterns. The most significant differences in the purchasing patterns of the three national samples have been identified with regard to the process of purchasing a smartphone, while the most repetitive patterns have been identified with regard to the purchasing of a new product. The results significantly support the GNG network’s validity for identification of consumer behaviour patterns. The application of this method allowed quick and effective to identify and segment consumers groups as well as facilitated the mapping of the differences among these groups and to compare the consumption behaviour expressed by consumers on different markets. The identified consumer purchase patterns may play a basic role for marketers to understand consumer behaviour and then propose tailored strategies in international marketing.

本文阐明了使用自学习GNG神经网络来识别和探索购买行为模式的方法。该测试是根据从60岁以上的消费者那里收集的有关三项产品购买的数据进行的。在2017-2018年间，对斯洛文尼亚，捷克共和国和波兰的第三年龄大学的老年学生进行的一项调查中收集了用于探索购买行为模式的主要数据。最后，总共确定了六种不同的购买模式，即“周到的决定”，“对推荐敏感”，“受益人，“短暂的周到决定”，“习惯性决定”和“多种”购买方式。在购买智能手机的过程中，确定了三个国家样本的购买模式中最显着的差异，而在购买新产品时，确定了最具重复性的模式。结果大大支持了GNG网络对消费者行为模式识别的有效性。这种方法的应用可以快速有效地识别和细分消费者群体，并有助于映射这些群体之间的差异，并比较消费者在不同市场上表现出的消费行为。确定的消费者购买模式可能对营销人员起着基本作用，即了解其消费者行为，然后在国际营销中提出量身定制的策略。