Google Suggest isn’t exactly a tool, but I’ve found that it can be useful for identifying potential keywords. As you type a query on, Google Suggest recommends search queries based on other users’ search activities. These searches are algorithmically determined based on a number of purely objective factors (including popularity of search terms) without human intervention. The Suggest dataset is updated frequently to offer search queries that seem to be trending upwards. This feature is largely one of the reasons that you may see repeat traffic of seemingly long tail keywords. By identifying these long tail keywords and optimizing for them, marketers can capitalize on seemingly obscure keywords with little competition.
Depending on your topic / vertical and your geographic location the search engines may have vastly different search volumes. The tool can only possibly offer approximations. Exact search volumes are hard to find due to vanity searches, click bots, rank checkers, and other forms of automated traffic. Exceptionally valuable search terms may show far greater volume than they actually have due to various competitive commercial forces inflating search volumes due to automated search traffic.
The local data that is returned is a 12 month average of all search queries in the United States using the Google Search Engine and affiliated Google search properties. Using the sidebar on the left, marketers are easily able to specify the keyword matching type (Broad, Exact, Phrase), change the category, and refine their results to contain specific terms.
Customize the data that LTP is going to fetch with pre-filters like “Suggested Bid” (or Cost per Click [CPC], i.e. the amount of money an advertiser is willing to pay for a click on their AdWords ad that is targeting that specific keyword), “Local Search Volume” (average number of monthly searches for a specific keyword), “Advertiser Competition” (the amount of advertisers competing for a specific keyword) and “Number (of) Words” (very important when you’re trying to find long-tail keywords).
Within the results, Long Tail Pro inserted several more keywords based on that suggestion and automatically calculated all relevant stats (including KC Scores). It only took me a few seconds to scan those results and see a great long tail keyword – legitimate work from home jobs with no startup fee. This exact keyword gets 210 monthly searches and has a KC Score of only 25! Rosemarie could easily write an article for her Busy Budgeter site that is optimized for this exact keyword and she should be able to rank in Google somewhat easily.

The final sales price was based on a multiple of trailing 12 months net income (i.e. The average net income over the most recent 12 months).  I feel like I got a very good multiple.  I had talked to the brokers at FEinternational, Quiet Light Brokerage, and had viewed sale history of other similar companies, so I know the price I received was very competitive.