Customer Network Visualization Erik Feng Dawei Shen Kwan Hong Lee Dr. Marko Popovic

Concept Use a dynamic graph to identify groups of consumers based on spending pattern A graph is a network consisting of points or nodes connected by links. Our project analyzes customer purchases at their top five restaurants. Of course, the scope of the research is not limited to just restaurants, but can be applied to any industry. Food is an essential item that everybody will purchase, which would make data more readily available.

My Demo: Basics Retrieves data from a data file stored online Creates a graph based on the data Nodes represent customers. Links indicate that two customers visit the same restaurant. node link

Existing Graph Visualizations Graph structure has to be manually constructed to look nice. Many intersecting links and nodes look messy.

Our Demo: Features Uses physics concepts to dynamically rearrange structure. Links act like springs that stretch and shrink. Nodes act like charged particles that repel each other to avoid overlapping and obscuring data. Ability to see only the links of one node at a time

Screenshots Initial arrangement – nodes are placed randomly in a circle. Node size indicates how much each customer spends in total. Node color indicates how well connected it is. The more eye- catching yellow is used for nodes with the most connections, followed by red, magenta, and blue.

Screenshots After calculating the equilibrium forces, the graph settles down. Node repulsion follows the (r -2 ) law while the springs follow Hooke’s Law. The length of each link is determined by the percentage of the total spending that the two corresponding customers spend at their shared restaurants.

Screenshots Mousing over a node highlights its links and makes extraneous links disappear. The restaurants of that node appear in the textbox above.

Screenshots Graph is interactive: users can physically drag nodes around if they wish to customize the arrangement.

Try It Out:

How Banks Can Use This Behavioral analysis ◦ Customers  A smaller node linked closely with several large nodes can be a warning that a relatively poorer person is spending a significant proportion of his money at fancy stores ◦ Businesses  We can redefine nodes as businesses and links as shared patrons. Businesses with declining number of patrons may present a greater risk if they wish to borrow money to make investments.

How Banks Can Use This Network of Financial Services ◦ A bank can construct its own graph with different financial services (such as demand deposit account, savings account, credit card, etc.) as nodes and links if there are customers that use both services. The idea of a physical equilibrium can help a bank determine which services could use innovative development and which (if any) are obsolete.

Future Research With more data collected for a period of time, we can construct a graph that changes over time. We would be able to observe changes in behavior, spending, or tastes. We could develop a social network similar to Facebook that connects people based on similar restaurant preferences, musical tastes, etc.