1. Building Mini-Categories in Product Networks Dmitry Zinoviev, Mathematics & Computer Science Zhen Zhu, Marketing Kate Li, Information Systems & Operations Management Suffolk University, Boston 2. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 2 Outline Project objectives Data set Network construction Core products Tiles Future work 3. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 3 Objectives Long-Term Objective: Identify and predict consumer projects, based on the purchase data collected by a Fortune 500 Specialty Retailer (the Retailer) A consumer project is a collection of consumption and co-creative actions that use multiple products and services provided by stores to meet their idiosyncratic life purposes. Short-Term Objectives: Use product network approach to identify product groups (tiles) that could serve as material lists and be used as building blocks for consumer projects 4. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 4 Data Set Purchase data collected by the Retailer over two years in 20122014 Products: 111,000 material items, 351 non-material items; 15 groups, 235 classes, 1,778 subcategories Purchases: ~12 mln sales, 545 thousand returns Include household id, register id, date/time, location, price, quantity, etc. 5. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 5 Product Network (1) Nodes represent products Edges represent co-purchases Purchase pattern: 6. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 6 Product Network (2) Two products are considered co-purchased, if they were purchased: by the members of the same household within 4 weeks (to cover at least several weekends) at least 7 times (to build confidence) 7. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 7 Network Metrics Metric Value Complete Network Number of nodes 18,788 Number of edges 154,968 Number of isolated pairs 427 Number of communities 643 Modularity 0.49 GCC Number of nodes 17,294 Number of edges 153,854 Number of communities 80 Modularity 0.48 Potentially good structure! 8. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 8 One of the Components Scale-free network! Structural formations 9. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 9 Core Products & Staples Long-tail distribution of purchase volumes (staple products at the tail) Long-tail distribution of number of links in the network (core products at the tail) Staples hurt modularity, complicate clustering Eliminate them! Threshold: degree64. Staples = core products! 10. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 10 Core Product Statistics Original network The Core Network w/o Core Nodes 18,788 875 17,913 Edges 154,968 56,859 25,316 Density 8.810-4 0.149 9.910-4 Nodes in GCC 17,293 875 9,947 Isolated Pairs 427 0 763 The core products form a connected network of their own that deserves a separate study Top 10 core products: plastic bucket, wood stud, soda, seal tape, plastic tape, diet soda, insulating foam sealant, painting tape, drinking water, flat brush 11. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 11 Structural Formations Tiles: Fully-Connected Cliques (mini-projects) Spoke-and-Hub Stars (mini-categories) Chains and Pendants Reflect consumers view on the product hierarchy Do not match the Retailer's product hierarchy 12. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 12 Cliques Each product is co-purchased with all other products in the clique Cliques represent topical complementary groups 22,148 cliques (some cliques overlap) Clique sizes have a power-law distribution (average size=4) 13. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 13 Shelving Clique 14. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 14 Stars The hub is the lead product and does not belong to the star The lead product is frequently purchased with the leave products, but the leaves are not purchased together Stars represent a topical group of substitutesmini- categories 2,321 stars (some stars overlap) Star sizes have a power-law distribution (average size=3.8) 15. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 15 Masonry Star 16. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 16 Chains & Pendants Linear structures connected to the main component at one (pendants) or both (chains) ends Represent products that are not purchased all together, but are often purchased pairwisepossibly because of consumer's uncertainty (substitutes by ignorance) or as a part of a learning/exploratory process. 768 chains/pendants, each not longer than 4 edges Average size=3.2 17. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 17 Screw Chain Note that the neighbors differ by one size step. 18. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 18 Conclusion We built a product network from the purchase data provided by a Fortune 500 Specialty Retailer We identified core products and related them to the staplesfrequently purchased items We extracted structural network tiles: stars, cliques, and chains/pendantsand related them to the retailing classes of substitutes and complements We believe that the tiles reflect the consumer view on the retail product hierarchy and could be used as building blocks for automated identification of customer projects 19. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 19 Next Steps Study the role of core products and their attribution to the Retailer's product hierarchy Discover tile hierarchies (such as stars of cliques and cliques of stars) Use tiles as building blocks for consumers' projects 20. March 2015 CompleNet'15 NYZinoviev/Zhu/Li 20 Acknowledgment The authors would like to thank Wharton Customer Analytics Initiative (WCAI) for the provided data set that made this research possible.