Shedding Some Light On Keyword DataSpending most of my time in keyword research and analysis for clients, I’m always interested in the types of metrics that others find useful in determining the relative value of a keyword. Traffic and competition metrics are a great start, but after that, one is left to evaluate the relevance of keyword targets to more subjective assessments. I’d prefer to let the data paint the picture as much as possible. If I’m going to dedicate resources in targeting an ideal Organic Rank, or optimize Paid Search bid to achieve the adrank that maximizes ROI, I’d like to limit the, “hunch” factor as much as possible.

After spending hours upon hours in Paid Search Accounts and Google Analytics data, I inevitably reach a point where I start to lose a little focus and allow myself to tangent down, “the rabbit hole.” In my last adventure, I came up with a few metrics that could be useful for initial keyword research and strategy.

Here are a few metrics on my wish list for Google’s Keyword Planner:

  1. Time Before Action: with this metric we would see how much time elapses (avg) between the initial query and the eventual click. This “Action” would be defined as any action leaving the current page, i.e. a click on a specific link, clicking to the next SERP, typing a new URL entirely into your browser, etc.

  1. Keyword Click Distribution: this could be as simple as a percent split of Paid Click vs. Organic Click. It could be more complex and show paid top vs. paid side vs. organic natural vs. organic image vs. organic location. Or it could be as segmented as SERP Ranks 1-10. I’ll take any of these!

  1. Number of Queries: This metric would provide the average number of searches performed. For instance, if I ran the keyword tool for keyword1, then in addition to traffic volume, competition, suggested bid, etc. I would also know, on average, when someone searches for keyword1 they average four follow up searches.

  1. Follow-up Query: This would show the most common search term to follow the keyword you’re researching and the percent of times it follows the researched query. For instance, 10% of people searching for keyword1 will then search for keyword1 rates.

While none of these metrics would revolutionize how keyword research and targeting is done, they could help prioritize keyword targets. For instance, for keywords with a longer, “Time Before Action,” the results may not be entirely relevant, and I may not want to target this keyword. On the other hand, I may care less where I rank in Paid Results as I know the user is more likely to scan more ads. Furthermore, combining these metrics could help gain insight into the psychology of the these searches. Short-tail terms can be very expensive, but knowing the “Follow-up Queries” and the average number of searches can uncover the intent behind the search.

In any case, I believe the more data we have available, the better services we can provide. What metrics do you stumble across when you venture down the Rabbit Hole?

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