20.4.11

Making Buying Decisions via Social Network Analysis


I have an interest in the recent financial crisis (a.k.a. mortgage meltdown) so I am constantly looking for good reading material about the topic. This morning I was wondering... "What should I read next?" "Which one book will cover most of the angles of this topic?"

Rather than spend time reading reviews of the popular books on the topic, I followed my own suggestion. Using social network analysis [SNA], I created a book network around the topic of "financial crisis". The data was gathered from Amazon and the map of the most frequently mentioned "also bought" books is displayed above. The map does not rank books by sales volume, though it does show the most popular book on the topic of financial crisis.

The network map above shows how books were bought together and by the same customers: A-->B means that people who bought book A also bought book B. This book network helps us see which books are most influential and most integrated in this topic area. I am looking for ONE book to read on the subject, so I will be examining the "integration" scores of each book in the network.

After gathering the data and putting into my software, the network self organizes into two clusters. The cluster on the left contains mostly economic perspectives on the financial crisis and the books in the small cluster on the right contain more of a political perspective on the crisis. I am more interested in the economic dynamics of the financial crisis so I will focus on the cluster on the left.

Two books emerge at the top of the list of network integration scores [the nodes in the network map are sized according to relative integration scores] -- Too Big to Fail and All the Devils Are Here. They both have many similar connections to other books, so they play similar position in the network -- I could choose either one, and be happy.

Update: Thanks to Laura C. Tisdel, Editor of Too Big to Fail for sending a copy of the book after reading the above!

4 comments:

Tutor Mentor Connections said...

Valdis,

This is another great example of how someone with SNA skills and the ability to write articles that make sense of the information can serve in an intermediary role to help a wider world of people find information that they might value.

I continue to seek out volunteers/partners/sponsors to help me apply this thinking in the work I do in Chicago.

Thank you.

Dan Bassill
Tutor/Mentor Connection

rick davies said...

What are the "relative integration scores" mentioned here? Do they have another name? Are they calculable using UCINET or some other SNA package?

Valdis Krebs said...

Rick,

The Integration measure is one I developed from my 20+ years of doing SNA. It looks at more than just geodesics(shortest paths), which is what most academic measures are limited to. No other network software calculates this measure as far as I know.

My clients have access to it. I use it along with some of the basic network metrics from academia (found in UCINET, InFlow, etc.).

I am working on several network metrics, that IMHO, improve upon the std. academic set based on my experience with a large number and variety of SNA projects I have participated in.

Valdis

MichaƂ said...

Nice idea. I wonder though to what extent the data you collected is a result of a *personalized* recommendation Amazon gave you. I can imagine that these recommendations are based also on the types of items you bought yourself on Amazon. Moreover, I'm afraid that even opening Amazon's website without logging in Amazon personalizes the suggestions based on location from where you are connecting to the Internet etc. In a similar flavor Google search is personalized based on something like 50 characteristics even if you are not logged in. See this http://www.youtube.com/watch?v=SG4BA7b6ORo and also this http://www.wired.com/magazine/2010/02/ff_google_algorithm/all/1