Amazonco-purchasing networks.pptx
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The Network Analysis of Amazon Co-purchasing was done to produce high profits for vendors. It is a smart technique to identify the sale of those products, which eventually leads to the co-purchase of other related products. Amazon used its customer's profile, buying history, and browsing history to examine their purchasing behavior. The project was done using Gephi Software to analyze the data.
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Amazonco-purchasing networks.pptx
- 1. Amazon Co- Purchasing Network Team 8 Disha Bhatnagar Padma Shneha Swati Lathwal University of Central Florida
- 2. Summary Purpose: • The purpose of this network analysis is to educate ourselves about the buying pattern of Amazon online store users. • We will be able to predict the central entities and group of such entities from the network to recommend how promoting some of the co- purchased items may increase the sales of the selected items
- 4. Original Graph • This is the network of items on Amazon that have been mentioned by amazon’s “people who bought X also bought Y” function. • This view is very cluttered. The nodes are too close to each other. • Node are the products and edges mean co purchase. • The network has no multiple edges. • If an edge starts from source node to sink node , then the product that denotes the source node, is independently bought and item at sink node is co-purchased. • The frequency of co purchase is not available . Hence , the graph is unweighted.
- 5. Frutchermann Reingold • It is a force directed algorithm so all the nodes are attracted towards the center. • Degree of a node in this network depicts the extent of an item’s Co – purchasability. • Red color nodes are the nodes with extent of co-purchase degree as one . Means they are only co purchased with on item. • Green are the ones which are co purchased few times .
- 6. Yifan hu Layout • In this network , the red color nodes are the nodes with maximum degree. Around 10 or 11 , which means that there are few such nodes with that degree and most nodes are the ones with degree one to five. • A node with higher out degree shows that when that item was bought , many items were co purchased , which could be the accessories of the original product. • A node with higher indegree shows that the item is co purchased with many other items.
- 7. Open Ord • The bigger nodes are the nodes which are connected to a lot of other nodes. • The big clusters are few and mostly there are small clusters which means higher number of co- purchased patterns are few . • The gap shows that there are no edges between the clusters. so there’s no co purchasing link between them. • In this network , there can be many such incidental nodes which are bought together but there isn’t any pattern of co purchasing between them.
- 8. Modularity • Most popular products form communities within themselves. • Modularity: 0.995 • Number of Communities: 1135
- 9. Clustering Coefficient • Popular products tend to be co-purchased with other popular products forming clusters • Average Clustering Coefficient: 0.051
- 10. Average Degree • Degree of a node represents an item's co-purchasability. • The significance of in-degree and out-degree of a node would be different. • A node with higher out-degree means that when an item is bought many other items are co- purchased. • A node with higher in-degree means that the item is co- purchased with many other items.
- 11. Eigenvector Centrality • It is the influence a node has on a network. • Popular products tend to have a higher eigenvector centrality. • Purple= 0.94, with highest eigenvector centrality.
- 12. Closeness Centrality • Node with highest centrality is the closest to all other node • Green nodes represent highest closeness centrality • Higher closeness centrality means a popular product will be bought with many other products
- 13. Filters : Giant component
- 14. Sub-filter : Degree Range
- 15. Filter : In Degree Range
- 16. Filter : Out Degree Range
- 17. Filter : Out Degree Range
- 18. Overall findings: The Directed Graph of this Network depicts that there are nodes with large number of out- degree and less number of in-degree , which means that when any item was bought , many items were co purchased. The Network turned out to be a Bus Network if we remove the highest degree node the entire network will fall apart.
- 19. Overall findings • Openord Layout helped us know that there are incidental purchases in the graph which is why most of the nodes have less number of degrees in the network.
- 20. Contemplation • We have analyzed the data to understand the significance of high in degree and out degree nodes and how such products with high co purchasing degree can help us know the patterns of users who co purchase products from amazon.
- 21. Contemplation cont.. • These patterns can be very useful in marketing and customising ads according to the products so the sales can go up.There are some incidental • shopping cases also but if we look at the frequency of the co purchase then data can be analysed • better to help make the marketing of co purchase products which can definitely affect the sales of