Nagao and analysis

have called “long tail” theory point out that there are many relatively small number of blog links into it, but the “long tail” only a few millions of blog links into it. This is very similar to the bow tie theory.

The same long tail theory is also applied to search, because a small number of queries will lead to a large number of clicks, but in fact, sometimes thousands of other terms are converted at a higher speed.

In this article, I will explore the long tail of search and how analysis affects it.

Danny Sullivan published a good article in Search Engine Watch about long tail and search and how the top 50% of searches generate 80% of search volume.

However, there is also a view that the bottom 20% of searches also generate 60% of sales. In other words, those more focused and specific terms are more likely to be converted. For example, searches for people like “Lost Lost” are unlikely to translate into “New Jersey divorce lawyers. Holmium

Therefore, as a search engine marketer, we not only need to focus on those high traffic more generic terms, but also efforts to reduce traffic, but more likely a high conversion keywords.

This is a commonly used strategy in today’s SEM. Even marketers with high click-through rates do not have high click-through rates. This helps build brand awareness. Then, the budget focuses more on key terms with lower costs but higher conversion rates.

Or the organic search market we see every day. My large clients get a lot of clicks from more general phrases, but the conversion occurs on more specific terms.

My biggest problem right now is that I don’t have an easy way to effectively measure thousands (sometimes tens of thousands) of individual conversions.

I think today’s analysis software packages need to be more flexible, automatically grouping recommended information into keyword baskets, just like today’s modern PPC programs.

In this way, you can easily determine whether geographic recommendations (for example) are “more valuable” than product-specific recommendations.

By grouping keywords, you can better analyze traffic, including paid and organic traffic, to determine where strategies and tactics can be better applied.

For example, using the above situation, if my website has good organic positioning in terms of geographic location and product specific aspects, but I see more geographic recommendations leading to conversions, then I would want to develop a strategy to strengthen this. I want to emphasize my organic location and work hard to further improve my ranking here.

But without the group analysis I mentioned earlier, it would be difficult to do this at this time.

For example, one of my clients is a well-known legal website that receives millions of search recommendations from hundreds of thousands of visitors every month. Of the roughly 100,000 Google recommendation letters, I know that about 75% are more general terms, but what I really want to know is that the other 25%—better conversion terms—are mostly geographic terms (like a New Jersey lawyer) Or search for a specific type of lawyer (such as a divorce lawyer).

However, because there are too many terms in the long tail, I cannot easily determine this.

Therefore, I call on the analysis provider to solve this situation. I need to know what conditions are more favorable to my clients. Of course, I can guess that geographic terms might be better in this case, but I need proof, which I can’t get easily.

In a sense, analysis should be more like a search engine. Instead of showing all my recommendations, showing my organic recommendations may contain a location from Google. And don’t make me have to enter the location (although let it be an option), the analysis should be smart enough to group keywords, with some guidance.

Why Hitbox, Urchin and Webtrends cannot understand that bankruptcy clauses and financial clauses may be related, or that Paris and European clauses are related.

More intuition needs to be programmed into the analysis to help ordinary users be able to determine the focus of attention.

In other words, if there is such a function in an analysis package, I think it will become one of the most commonly used functions. Because if I can segment the data in many different ways to find out whether New York is more popular than New Jersey (in terms of search), then I can better target my paid and organic activities.

This is because I can see that if I pay a higher PPC cost for the New York clause, but find that the actual conversion rate in New Jersey is better, then I will shift the budget to the New Jersey clause.

On the contrary, if I find that New Jersey’s terms have been converted better, and I have done paid activities for both New York and New Jersey, even though I rank highly in both of these areas, why don’t I try to transfer some of the budget to the other What can be done better?

In addition, having such an intuitive analysis can help find those organic terms that have lower PPC costs, because others have not considered their conversions to be good.

As you can see, there are many different ways to use an analysis package that uses data more efficiently.

Because if you can increase the incidence of higher conversion keywords in your organic and paid activities, you will increase your sales. Even if your total search volume drops because of a lower emphasis on generic terms, does it matter as long as there is a positive return on investment in the end?

Think about it from another perspective: My experience tells me that more specific terms have a higher conversion rate than general terms. Even if the general terms get more traffic, the specific terms will bring greater benefits. What I want to say is that even if a term has a high citation rate, it is unlikely to have a high conversion rate like other terms. Therefore, people need to be able to see the entire picture, and in today’s analysis, it is very difficult without a lot of manual intervention (exporting to a spreadsheet or database and performing complex analysis).