This post originally published July 29, 2008.

In Charles Heflin's "The Social Marketing Blueprint Formula", he talks about Type 1 and Type 2 markets. He defines them as follows:

1. TYPE 1: Markets where people socialize (internet marketing, movies, music, sports, medical, weddings, reunions, politics, religion, spirituality, etc, etc, etc.)

2. TYPE 2: Markets where socializing is limited or non existent (cardboard boxes, socks, desks, office chairs, satellite tv, shoes, poker chips, etc, etc, etc.)

This is a critical step that Charles leaves to your discretion. As I am frequently asked about the application of Social Influence Marketing by my clients, I thought it would be a good use of my time to identify industries that are appearing naturally in social media conversations. First, I will explain my methodology. Then, I will share my findings (which were surprising).

I used the SIC Division Structure as a basis for the industries. You will see that I applied common names where I felt it would be more representative (this could have introduced bias). As an example, rather than use "Food and Kindred Products", I substituted "Food". Then, I identified the industries that appeared on the surface to have merit in Social Influence Marketing (again - possibility of bias).  Of the 99 SIC divisions, I culled the list to 33 (dropping groups like "Pipelines (except Natural Gas)"). Here is my list:

Agriculture, Apparel, Art, Auto Repair, Business Services, Chemicals, Construction, Crime Prevention, Education, Employment, Entertainment, Environment, Exercise, Family, Food, Forestry, Furniture, Health, Hotels, Housing, Insurance, Legal, Lumber, Movies, Oil & Gas, Paper, Real Estate, Sex, Sleep, Tobacco, Transportation, Travel and Trucking

Next, I searched for each term (as listed above) across four social search engines: LinkedIn Groups (LG), Google Groups (GG), Google Blogs (GB) and Steve Rubel's Custom Google Search for Twitter (GT). I simply recorded the total number of results for each term (bias alert: I did not scrutinize each result to ensure it was on point). Then, I prepared an index with the following weighting:

Index = (LG + (GG / 100) + (GB / 1,000,000) + GT) / 4

Here are the results - divided into five industries per graph:

I've explained that this may not be the most scientific approach. However, it does provide some guidance as to which industries are likely to experience Social Marketing success with more ease. Please use with caution, and let me know if you see any flaws or things I've missed.