Abstract
The complementarity and substitutability between products are essential concepts in retail and marketing. Qualitatively, two products are said to be substitutable if a customer can replace one product by the other, while they are complementary if they tend to be bought together. In this article, we take a network perspective to help automatically identify complements and substitutes from sales transaction data. Starting from a bipartite product-purchase network representation, with both transaction nodes and product nodes, we develop appropriate null models to infer significant relations, either complements or substitutes, between products, and design measures based on random walks to quantify their importance. The resulting unipartite networks between products are then analysed with community detection methods, in order to find groups of similar products for the different types of relationships. The results are validated by combining observations from a real-world basket dataset with the existing product hierarchy, as well as a large-scale flavour compound and recipe dataset.
Highlights
Understanding the hidden relations existing between products is fundamental in both economics and marketing research as well as in retail [1]
We simulate a consumer population characterised by a set of rules in this world, and ask whether our null models capture the right relationship between each pair of products, whether our measures give the right degree between them, and whether our complement and substitute roles provide insights into the groups of complements, and the groups of substitutes, respectively
4.2 Sales data Hereafter, we use the variant of ER model as the underlying null model, since its assumptions are generally applicable in real-world purchases, and we only show the results from the original measure, because both have very similar behaviour; see Appendix B for the parameter calibration and Sect. 3 in Additional file 1 for the results from the randomised measure
Summary
Understanding the hidden relations existing between products is fundamental in both economics and marketing research as well as in retail [1]. Brick-and-mortar retailers seek to identify the best way to arrange the product layout in aisles and stock their shelves [4], and online retailers strive to optimise the grouping of products in their online shops [5]. They must decide which products to bundle or promote together. These assortment-related decisions have significant influence on customers’ choices, sales of products, and profits [2, 3, 6]
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