Using Search Engine Optimization For Intelligence Analysis

SEO Analysis Now - A Site For Using SEO in Intelligence Analysis
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As a final requirement of this course, I had to research and report findings on an analytical technique of my choosing. In addition, I had to test-out and apply the technique in order to reveal its true strengths and shortcomings; as well as come up with a how-to guide to use the technique. For my project, I chose to conduct a Search Engine Optimization (SEO) Analysis on the Websites of two popular coffee shops, Starbucks (which is well established) and Caribou Coffee (which is slowly gaining popularity) and reflect on how this process can be applied to Competitive Intelligence (CI), to Law Enforcement Intelligence (LEI), or to the National Security sector. I chose SEO not only because it is an emerging analytical process and can be useful to intelligence analysts, but also because I found it quite intriguing and fun.

SEO provides a way to gain insight into a Website’s audience.

If we know who an audience is (age, gender, ethnicity, education, affluence, etc), how they behave (what other Websites, or types of Websites, they visit), from where they log on, when they visit, and what other sites direct their traffic (as well as what their general interests are), then we can make assessments and predictions about how to either promote their behavior (steering them toward a particular site – useful for CI & marketing purposes) or how to counter that behavior (keeping them away from sites – useful for LEI & N’tl Sec purposes).

For more detailed information about how SEO can be used for Intel analysis, please check out my project:

SEO Analysis Now - A Site For Using SEO in Intelligence Analysis

(The image below is a geographical search index comparison of online users searching for Caribou Coffee [left] and Starbucks [right]. Images provided by Google Insights for Search)

An introduction to decision tree modeling

http://www.udel.edu/chemo/SDB/~pdf_papers/JChemo_18_275_2004.pdf

Summary:

The article offers a critique of decision tree mapping as a technique used to generalize data sets.

"In its simplest description, decision tree analysis is a divide-and-conquer approach to classification. Decision trees can be used to discover features and extract patterns in large databases that are important for discrimination and predictive modeling." It most common use is when using exploratory data and predictive modeling applications.

Decision mapping's advantages include recognizing the interpretability of the constructed model, as well as determining inter-dataset relationships. It takes the form of a hierarchical model formed by decision rules represented by nodes. The first node is reffered to as the branch node, with subsequent nodes reffered to as leaf nodes.

The general consesus is to overgrow a decision tree by incorporating all relevent criterion. The tree can then be "pruned" to reduce complexity. Generalizability is enhanced by incorporating ensemble methods such as bagging (randomly selecting samples) or boosting (reweighting criterion).