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Inter-viewing the Amazon Web Salespersons: Trends , Complementarities and Competition

Inter-viewing the Amazon Web Salespersons: Trends , Complementarities and Competition. Michalis Vafopoulos ( with T. Theodoridis & D. Kontokostas ) vafopoulos.org 2/ 10/2011. Main issue. Identify purchasing patterns in Web retail using available public data. outline.

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Inter-viewing the Amazon Web Salespersons: Trends , Complementarities and Competition

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  1. Inter-viewing the Amazon Web Salespersons: Trends, Complementarities and Competition MichalisVafopoulos (with T. Theodoridis & D. Kontokostas ) vafopoulos.org 2/10/2011

  2. Main issue Identify purchasing patterns in Web retail using available public data

  3. outline • Before analysis • Data • Innovations • Main results • The Amazon co-purchasing network • Amazon: the book-based multi-store • Triad analysis: Winners in Product wars • Switching to best sellers • Hidden complementarity saves MS

  4. Before analysis • Why Amazon? the best proxy for Web retail • Algorithms: item-based top-N recommendations • Analysis? co-purchase directed graph • related literature? • Computer science (Collaborative filters, Karypis) • Economics (market basket analysis, Oestreicher-Singer) • Software? Gephi, iGraph, FANMOD, mysql, & xls(old habits!)

  5. Data • 226,238 items • 13,351,147 co-purchase connections • The crawler was started with an initial set of 300 items, which were the top 10 selling products in each of the thirty categories. • 61 recommendations per item (average) • 2nd level added 17,204 items • 3rd 208,740 items

  6. Innovations • Cross-category analysis • Broad graph (in dyads at least one from category) • Strict (both items from the same category) • All recommended items crawled (max 104) • Triads analysis • Community analysis

  7. Main results • Amazon has evolved into a book-based multi-store with strong cross-category connections. • Top selling products are important in the co-purchase network, acting as hubs, authorities & brokers. • Co-purchase links not only manifest complementary consumption, but also switching among competitive products (e.g. Kaspersky -> Norton). • competitive products consumed as complements because of the existence of compatibility and compatible products that facilitate their joint consumption.

  8. The Amazon co-purchasing network Item X co-purchased most frequently with products Y1, Y2,..

  9. Amazon: the book-based multi-store • The Amazon co-purchase network for all item categories

  10. Triad analysis: Winners in Product wars Analysis shows that co-purchase links not only manifest complementary consumption, but also reveal competitive relations among products that are perfect substitutes.

  11. Switching to best sellersthe case of Internet security market Ass: if products A, B & C are perfect substitutes (authority triad), then A has higher sales rank consumers who bought Internet security s/w, more often, also bought Norton Internet Security than related products

  12. hidden complementarity saves MS MS (purple) & Apple (orange) communities are “mediated” by compatibility like VMware Fusion, Parallels Desktop and compatible products like Office for Mac.

  13. overview • Amazon: the book-based multi-store • Triad analysis: Winners in Product wars • Switching to best sellers • Hidden complementarity saves MS Questions?

  14. supplement

  15. Descriptive statistics

  16. Amazon: the book-based multi-store Figure shows the interconnections among all different categories of products in the Amazon co-purchase network. A link from category A to category B is added if products from category B are present in the broad network of category A. The link intensity grows with the number of products that are present in that network and the size of a category denotes the number of products that belong to it. The stronger links are from Movies & TV to Books, from Kitchen & Dining to Books, from Toys & Games to Books and from Books back to Movies & TV. • The Amazon co-purchase network for all item categories

  17. hidden complementarity saves MS Fig. 2 shows a part of the Strict software co-purchase network, where different colors indicate different community membership. Different product communities have been identified based on the spin glass community detectionalgorithm and has been computed by the R package iGraph. It is interesting to observe that seemingly competitive products of Apple and Microsoft are in reality consumed as if they were complementary. Microsoft (nodes with red color) and Apple (nodes with blue color) product communities are “mediated” by compatibility like VMware Fusion, Parallels Desktop and compatible products like Office for Mac.

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