At the core of Keyword-Driven Marketing (KDM) is the idea that a keyword represents a customer. In the online space, if you trace back the steps of a customer all the way to their initial interaction with a search engine, you’ll see that the purchasing cycle begins with the entering of a keyword into a search box. The difficult part, of course, is determining what keywords future customers will use so that you can target those keywords in your organic and paid search efforts.

Many tools abound to help you do just that. The best of them not only give you data on the keywords you’ve specified, but also provide alternatives to consider. The reality is that customers will likely have done many searches as they learn and refine in their own minds what it is they really want to buy. So if your goal is to find related clusters of keywords, I recommend looking at the following tools:

Microsoft adCenter Labs Keyword Group Detection

Microsoft may be struggling with capturing market share in the search space, but of the three major search engines, they lead the way in providing the most creative tools. I learned of one such tool, Keyword Group Detection, indirectly through an article by Ann Smarty about keyword visualization tools. The group detection tool takes a keyword input and then generates a list of related keywords based on actual search data. The results are missing any indication of search volume. so it’s a good idea to take the data and drop it into Google’s keyword tool (discussed below).

Google AdWords Keyword Tool

Much has already been written about Google’s keyword tool so I won’t dwell on it. Rather, I just want to identify that the key component of this tool – when it comes to finding related keywords – is to enable the synonym match (on by default) while also looking at broad and phrase match results.

Trellian’s Keyword Discovery

Of the three tools in this roundup, Keyword Discovery is the only one that isn’t free. My use of Keyword Discovery has decreased significantly over the last year and a half as I’ve found the tools from the search engines themselves to be more useful. However, a recent update from Trellian prompted me to take another look. In particular, the “also searched for” feature sounded a lot like the holy grail of query chains. As stated by Trellian, the new feature aims to show what search terms people used during a session to help refine keyword targets.

Sadly, Trellian’s implementation isn’t quite as impressive as I would’ve liked. It doesn’t specify the order in which keywords were searched for, and it doesn’t make it clear what keywords are in the collection for a particular query chain. Still, the feature turns out to be a good alternative for finding related keywords. For example, you’ll get words like “medicaid” in response to a search for “medicare,” something a regular thesaurus wouldn’t provide.

Combining the above tools with the actual results from your paid search and search engine optimization efforts make for the building blocks of an iterative process for constant keyword refinement and expansion. This process, if done correctly and vigilantly, all but guarantees success.