Big data analysis, machine learning and data mining is almost contemporary, and many companies aspire profitable outcomes. However, collecting and describing might not be sufficient , and one must analyse and interpret the mass of information. Often this data is not meant for mining, and therefore great hope is with modern algorithms to find underlying, non-trivial patterns. However, most of these methods remain correlative and may be un-theoretical. Thus, big data analysis need thorough and comprehensible theory too, to interpret and understand its results. The study at hand has assumed human decision-irrationalities (e.g. asymmetrical domination and attraction) to be given, and has analysed real data made available by EuTrade. For theoretical, practical and, in particular, quantifiable interpretation of mutual influence of products arranged in shelves, a new key indicator has been introduced-the averaged impact factor (AIF) per product. Analysis have permitted indication for independence from a product's own revenue, and its corresponding family (and shelves, respectively). Furthermore, depending on the presence of a product in shelf, interacting with AIF has been associated with the daily product-family revenue. Based on current data, it has been pushed either by items with high impact present in shelf or by products with low impact out-of-shelf, and it has been reduced by products with a high AIF not present, and one with a low impact in shelf, respectively. Thus, with this new key indicator, the perspectives on product classification, replacement, shelves-arrangement or further purpose might be renewed. The study at hand has been explorative but, might give new possibilities for companies and research. Possible applications in practice, adaptions, adjustments, extensions and propositions for future research, are discussed.