Short Vs. Long Tail: Which Search Queries Perform Best?

The focus on the utility (or lack thereof) of long tail keywords in paid search campaigns seems to ebb and flow, but recently a series of articles about leveraging the long tail for pay-per click have been published. But in all the debate there’s an important distinction that most people fail to focus on.

Keywords and search queries are two very different entities. While keywords are the inventory an advertiser buys, search queries are the actual words people type into a search engine. The distinction is important, because search queries typically cover a significantly longer tail than keyword buys, and there is a lot of value in mining search engine query data.

To take a deeper dive and look at the way that head, mid-level, and long tail search queries perform in paid search accounts, we took a random sampling of WordStream client accounts and analyzed the aggregate cost, number of conversions, and cost-per conversion across the sample. The results were very interesting, and to some extent would confound the zealots on both the “short keyword list, no such thing as the long tail” camp and the “the highest value lives in the lowest frequency of clicks” contingency.

Which types of search queries drive more traffic for less spend?

We analyzed about a million dollars worth of spend, spread across roughly 15 million impressions. A few notes on the data:

  • The advertisers we looked at were a randomly selected mix of lead-gen and e-commerce companies who are small to mid-sized advertisers.
  • We analyzed the total spend, number of conversions and cost-per conversion against a series of segments.
  • We divided the data based on the number of clicks per query: 300+, 100-300, 6-100, and 0-5.
  • All of this activity was on Google’s AdWords.
  • We were also careful to mix in both long-term and new account data, so that the results wouldn’t be overly skewed by the way our software builds campaigns.

It’s also worth noting that paid search is a multi-billion dollar industry, and this sample size is by no means definitive, but there are nonetheless some very interesting insights to be gained.

First let’s look at the distribution of spend across head, mid, and long tail terms:

The graph depicts spend distribution across a variety of different click volumes, including long tail spend.

The interesting thing about this analysis is that a vast majority of the spend in the accounts in question was directed toward long tail search queries. The aggregate number of search queries in the accounts analyzed was greater than the aggregate number of keywords by about 4 to 1.

This leads to a misconception about the distribution of searcher intent. Many advertisers think that most of the traffic is being driven by a short list of keywords, when in reality these advertisers are spending significantly more of their budgets on the long tail.

Next, let’s look at the distribution of conversions across the same segments:

The chart depicts conversion distribution across a variety of different click volumes, including long tail conversions.

This data maps pretty consistently with the first data series, showing that these advertisers are driving a majority of their conversions from queries with 0-5 clicks. Additionally, 90 percent of the conversions for this data set were driven by queries with 1-100 clicks attached!

Probably the most interesting data set of all is the cost-per conversion across head, mid and long tail queries:

The graph depicts cost-per conversion (CPC) distribution across a variety of different click volumes, including long tail costs per conversion.

The cost-per conversion in the 6-99 click segment is less than half of the CPC for more competitive terms, while the particularly low-volume terms (0-5) are roughly two-thirds higher per conversion than the 6-99 click segment.

So what does all this query data mean?

I think the story this data is telling is that:

  • There is enormous value in the long tail of search queries
  • Query data has a longer tail than keyword data
  • To achieve high ROI, it’s crucial to aggressively mine negative keywords and to effectively target more specific search queries.

Here are three key takeaways from our analysis:

There is value in the long tail. The fact that 0-5 click queries and 0-100 click queries comprise so much of the cost and total conversions for these companies is obviously an indication that the long tail of search queries contains a lot of traffic and opportunity.

There’s a longer tail for query data. The fact that the total number of keywords is roughly a fourth of the total number of queries is the result of numerous long tail search queries “hiding,” rolled up under a single keyword. A cursory glance at any of the advertisers accounts might lead one to believe that most of the traffic is being driven by a short list of keywords, when in reality a series of broad matched keywords are matching against more specific, less popular queries.

Negative keywords also have value. The final key takeaway is the product of the discrepancy between the cost-per conversion for 0-5 clicks versus 6-100. While 0-5 clicks drove a majority of the cost and conversions across these advertisers, the 6-100 segment had a significantly lower cost-per conversion.

In theory, the 0-5 segment should be more specific variations of words and phrases. This should mean that by and large they’d be better targeted and more likely to convert. So where’s the gap?

The relatively poorer results in the 0-5 segment are largely because those queries aren’t hyper-targeted: many are the result of broad match aggressively pushing impressions at people that are matched to much broader keywords. Additionally, many irrelevant terms are rolled up in those broad keywords, driving up costs by matching terms people didn’t realize they’d be bidding on to ads for what are now very poorly targeted ads.

Where to go next with search query data and the long tail

While this data certainly isn’t representative of every single advertiser’s experience, it does reveal the need for a strategic approach to combining broad match with negative keyword discovery and implementation, and it emphasizes the importance of discovering, grouping and targeting specific search queries.

Since this article was spurred by a series of recent discussions surrounding the long tail, I thought I’d include links to the articles that inspired this inquiry:

Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.

Related Topics: Channel: SEM | Features: Analysis | Google: AdWords


About The Author: is the co-founder and managing partner of Measured SEM, a search engine marketing consulting firm that offers paid search management and search engine optimization (SEO) services to businesses of varying sizes in various industries.

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  • Terry Whalen

    Hi Tom,

    I think your takeaways are solid, if underwhelming. Who would argue against the notion that there is more search query data than keyword data (that is basically what is being said: “there’s a longer tail for query data”)? This is true almost by definition, as many search queries are unique – there will always be more search queries than keywords, even when folks are diligently using search query data to add keywords to accounts. And I don’t know anyone that would argue whether negative keywords are a good idea. How could they not be? Of course they have value. And anyone that argues that the long tail has no value or is unimportant either doesn’t know what they’re talking about or is simply trying to come up with article copy.

    There’s a lot of value in the short tail and the long tail and the tail in between, and it’s certainly a good idea to try for additional granularity by making sure keywords map as much as possible to actual search queries that ads in ones account are being matched up against.

    But it’s also possible to go overboard on this – if your account has 100,000 keywords but only 5-20% of those are showing any cost data at all in a given month, you may want to actually prune keywords (and afterwards, add keywords for which there are actually searches being performed by actual searchers), so that your account becomes less unwieldy and a little easier to manage on a day-by-day basis.

    p.s. I am a fan of Wordstream!

  • George Michie

    Hi Tom,

    Thanks for the kind references.

    Question: why are the costs per conversion different among the groups? Seems like if you’re willing to pay $X for a conversion that would apply — on average — as much to the long tail conversions as to the head terms. We’ve never gotten instructions from a client to the effect: ” I’m happy to spend 30% of my revenue on short KW, but don’t spend more than 10% on the long KW.”

    Might differences in the cost per conversion indicate faulty bidding, or is there some other explanation?

  • reidspice

    Interesting analysis. I have to say, though, this sentence raises some serious questions about the cleanliness of the results you’re presenting:

    “The interesting thing about this analysis is that a vast majority of the spend in the accounts in question was directed toward long tail search queries.”

    I’d say that’s not true of many (most?) paid search accounts.

  • Tom Demers

    @Terry: Hey Terry! Hope all is well.
    Yeah I suppose the thing that I found interesting/compelling was the way the actual data broke down from a CPA/volume basis for these advertisers. The point of interest might be more the way that these SMBs are structuring their accounts, and the fall out in terms of distributed costs, conversions, and CPAs. The importance of search query data and the distinction between keywords and queries is obviously something you’re very familiar with, but for these advertisers not adequately managing for the distinction seems to have created some waste in certain areas.
    @George: no problem re: the kind references, I’m a huge fan of your blog and your SEL column!
    Yes bidding is definitely a component I overlooked in the article. As I said the data is a mix of new WordStream client data and more frequent users, which (among many other things) makes the data far from perfect, which I hope I helped to qualify in the article. Many of the clients are likely failing to manage bids effectively at the query level – particularly so because even in the instances where the data is representative of WordStream customers, our software isn’t a bidding platform.
    I suppose the take-away I didn’t adequately articulate is that these numbers are an interesting indication of what many “steady-state” paid search accounts look like for SMBs. I’m sure RKG and CPC Search client accounts look different.
    Anyway great comments guys: I’m a big fan of both of your writings whenever I see them around the Web.

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