Predictive Marketing

SEO: Predicting the Payoff

SEO Strategy – The Landscape

SEO is a critical component of marketing for every website. There are many tips and techniques that are widely available that can help you increase the chances of getting a high ranking for the search keywords and phrases that are central to your marketing strategy. Everyone knows that a higher ranking is better, but exactly how high does your ranking have to be to generate significant traffic for your website? Is it possible to predict how much traffic you can generate for a given search phrase and ranking?

It is well known that you can use a resource such as the Google Keyword Tool to estimate monthly traffic for a keyword. Once you have that number, the question becomes: given a particular ranking, what percentage of those searches will result in a visit to your website? You can’t really create a reliable, comprehensive search phrase strategy without this critical piece of information.

There is a variety of counsel and opinion on this topic, not all of it consistent. For instance, one website, which provides research, training and educational services exclusively for the publishing industry, states the following rule of thumb:

“When your website or landing page turns up on page one in Google, you’re getting 100% visibility..But what happens when your landing page ends up on page two or three? We estimate that you’re getting about 32% Google visibility on page two, meaning only about 32% of users ever click through to page two, and a meager 7% visibility on page three. If you’re on page four or beyond, you simply don’t have a chance of being seen by your potential customers.”

The authors cited no source for this rule of thumb, or explanation of how they developed it. There are a number of other rules of thumb about click distributions floating around on the web, which are entirely inconsistent with the above. I’m not going to dwell on these here; I’d rather get right to the data I believe is the most credible and useful.

SEO Click Disributions – The Best Data Avaliable

There have been several eye-tracking studies that have been done over the past few years, all of which produce consistent results. Perhaps the best-known among them is a study that was performed at Cornell University that showed the following:

Source: SEO Researcher

This data tells a far different tale than the rule of thumb cited above: the first three ranks get 80% of the clicks, and the first page gets 98.9% of the clicks!

You might object, and I would agree, that this data is derived from an eye tracking study, not actual searches, and would thus compel some caution on extrapolating the results. Fortunately, there is some actual data available. In 2006, AOL leaked some data on over 36 million queries. The data was analyzed by Richard Hearne, and the results are as follows:

These results, by and large, are consistent with the Cornell eye-tracking study, in that the first page attracts an extremely high percentage of the clicks. The first three ranks garner 63% of the clicks; the top 10, 90%; the top 20, 94.5%. Here are the percentages for ranks 1-21, 31, and 41:

Viewed another way, an improvement in rank from second to first will almost quadruple the number of clicks. The number one ranking produces as many clicks as ranks two through eight combined. The drop-off in clicks is enormous by the time you get to the second page; a rank off 11 produces only .66% of the clicks; in comparison a rank of 10 produces more than 4 times as many, and the number 1 rank more than 60 times as many!

This click distribution has also been confirmed by an independent set of search data analyzed by Enquisite, a firm that specializes in search optimization software. Based on a proprietary data set of 300 million searches, the first page grabbed 89.71% of the clicks; the second 5.93%; the third, 1.85%, the fourth, .78%; and the fifth, .46%.

Since there are several methods that have produced highly similar results, there is a high degree of confidence that this data provides a reliable foundation on which to base an SEO strategy.

Implications for SEO Strategy

  • The ranking you can achieve for any given search phrase depends on a number of factors, including how well you optimize your pages for the search phrase, your page rank, and the amount of competition. If you opt to compete for high volume search phrases with a lot of competition, you have to realistically weigh the chances that you can make the first page.
  • A better option may be to pursue a long tail strategy, in which you set your sights on achieving a number one ranking on lower volume search phrases with lower levels of competition. This strategy necessarily involves multiple keywords in order to generate significant volumes of traffic for your website.
  • But perhaps the best option of all, made possible by this data, would be to pursue a mixed strategy. The increase in traffic you can expect from improving your ranking for any particular search phrase can now be predicted. You can therefore weigh the incremental increase in your website traffic for an entire portfolio of search phrases, and allocate your efforts in a way that will optimize your ROI.

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Always Be Testing

“It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.” – Mark Twain

In today’s rapidly evolving markets, you can never take anything for granted. The average lifetime of so-called “best practices” is shorter than ever.

I experienced this first hand recently on a couple of email tests designed to drive registrations for some upcoming conferences. Both conferences were targeted at highly technical IT audiences.

My past experience had always indicated that the best subject lines had offers and calls to action, especially when closing in on a limited time offer pricing deadline. So the following test results were as expected:

subject-line-test-3

I had seen this result dozens of times before; invariably, the subject line that emphasized the dollar savings and created a sense of urgency had always emerged triumphant. Imagine my surprise, then, when I saw the results of the following test for a different conference directed at a similar audience:

subject-line-test-2

Not only did the standard subject line offer no improvement on the alternative; it produced a result that was significantly worse. There is no doubt that, for this particular audience, the second subject line produced more conversions than the first.

The beauty of testing is that marketers don’t have to figuratively stumble around in the dark searching for the best way to communicate with customers. Test and learn strategies provide a way to find out directly, from prospects and customers, what they value and want most. There is no longer any excuse for a marketer to rely on hunches, anecdotes, and biased opinions in order to make marketing decisions. Even the seemingly most insignificant of decisions – the color of a registration button, for example – may have an effect on conversion rates which can be quantified.

However, when employing a test and learn approach to marketing, there is a trap to be avoided, which is illustrated by this example. Every business, and every customer set are different. There is no one set of best practices that apply in every situation. Think of the body of knowledge that you gain by testing to be a set of “best guidelines” rather than best practices. And know that no testing program ever arrives at a final best answer. Your customers are always changing. Always be testing.

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Email Campaigns and Customer Engagement

Many marketers pay a lot of attention to email stats such as opens, click-throughs, unsubscribes, etc. When you have embarked on a campaign that involves multiple emails to the same list, one of the factors you should consider is how deeply you are engaging the universe of customers that you are trying to reach.

For example, it’s typical to see open rates that decline by a fairly constant percentage on each effort, such as 20% on the first email, 15% on the second, 11% on the third, and so forth. If you were to graph the percentage of opens as a function of the number of emails sent to this customer list, it would look something like this:

multiple-emails-to-same-list-graph-v2The question now is, how many unique names on this list have actually opened the email? Or, equivalently, what percentage of the list (i.e. market) has been engaged?

At one extreme, the minimum penetration would be 20%. In other words,after the first email, no one new is opening the email. The opens on the second through fifth efforts are coming from an ever decreasing proportion of the orignal 20% who opened the first email.

At the other extreme, the maximum penetration would be 61%. In this case, the prospects opening the email are different on every effort. No one opens any of the five emails twice.

As you might expect, the actual percentage of penetration is somewhere in between. Upon examination of the email addresses from the prospect list that opened up each email, the actual cumulative penetration is 33%. The penetration after each effort looks like this:

cumulative-market-penetrationSeveral facts are clear from this. First, the 33% penetration is a lot closer to the lower of the two extremes. Second, it is clearly approaching a limit. Finally, the limit in this case, judging from the trend of the graph, is surely less than 40%, even if additional efforts are made.

There are a number of ways to use this information:

  • To keep emailing this list with essentially the same subject lines, “from” field, etc. is like beating your head against a wall. Instead of continuing to beat your head against a wall, you can test new subject lines to try and motivate new prospects to open the emails.
  • If additional information about the prospects is available, by statistically analyzing the differences in the characteristics of those who opened the emails versus those who didn’t, a predictive model can be developed to devise a messaging strategy to motivate those who didn’t open the email to open subsequent emails with the new message.
  • This information can also be used in predicting which third party lists will be most responsive and in devising messaging strategies for other lists with similar characteristics.
  • Even if you find that you can’t penetrate this list (or market) any further, you can save time and money by not trying to do so.

These graphs may look very different for your lists. But the information you need to analyze a major list in this way is readily available, and you can use it to your advantage.

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