China has over 400m search engine users, which makes it a significant market for search marketing.
There are a world of articles about best practice for search marketing practice in China out there, but unfortunately, most of them are hypothesis and based on incomplete evidence.
In this article, I would like to share my insights with you built a recent exercise I undertook recently in Chinese keyword research.
Let's see what I have learned...
Search suggestion
First I made a comparison of the search engines' Search Suggestions. This is when you enter keyword into the search engine inquiry box, the search engine instantly shows a list of suggestions while you are typing, as with Google Instant.
How do the search engines come up with these suggestions? Most SEOs believe that the Search Suggestion is provided based on (1) search volume, (2) search recency, and (3) location and occasion (like when you are searching on mobile device in a foreign country).
My exercise began with a methodology using a tool that I have developed to collect data from Google.cn and Baidu.com. The data sample included five luxury fashion brand keywords in three levels.
That means for all the suggestions that I got in the first round, I used them to search again up to 3-level down or until the search inquiry ran out of suggestions.
[Diagram of my data collection model]
[Summary of the Search Suggestion result]
Human versus machine
Before I further explain the result, we must understand that there is a fundamental difference between Search Suggestion and Keyword Suggestion.
As I mentioned in the preceding paragraph, Search Suggestion is the suggested queries provided by the search engines based on search volume and search recency. As for the Keyword Suggestion, it is the word or phrase provided by the keyword tool according to the algorithmic computation for words that should have similar meanings when used in a same context, as well as other information like the competition of the bidding, the estimated cost of the keywords, etc.
In short, Search Suggestion represents more of the human's search intent, whereas the Keyword Suggestion represents more of the machine's intelligence.
[My Search Suggestion data sample]
According to the data sample, the first consistency that I have discovered was that Baidu always returned navigational search queries at the top, such as the brand official website like "chanel ??" and "louis vuitton ??," etc.
All of the brand keywords that I tried had the same result no matter it was an English brand term or a Chinese brand term. I also have noticed that Google.cn provided more suggestions compare to Baidu.com in both languages' brand terms. The average ratio was 13:3 in the English terms and 13:8 in the Chinese terms.
In my test result, some interesting search customs have drawn my attention. In China, even though most foreign brand terms have their own Chinese synonyms, people like to search using the original English brand terms.
Foreign brands, nonetheless, should take note that when people in China search use the Chinese synonym, they might use the different ones. As you can see in my test sample, although the official Chinese brand name of Burberry is ???, more people use ??? to search Burberry, according to Baidu keyword tool.
Specialist versus generalist
I also have found that the user group's profile on the two engines is different, which can be reflected in their search queries.
Searchers on Google.cn are the specialists who include more details of the products in queries, such as a specific name of a collection or a package description. By contrast, searchers of Baidu are generalists who look for the official website or the pronunciation of the brand name, etc., a more explorative type.
Besides the comparison of the search engines' Search Suggestion, I conducted a comparison between the Search Suggestion and the Keyword Suggestion in my exercise as well. As I highlighted earlier, search engines assumably use two different datasets for the two.
In the combined screenshot below, there is a comparison between the Baidu Search Suggestion (the top data grid of the screenshot) and the Baidu Keyword Tool's suggestion (the middle part of the screenshot).
As you can see, Baidu's Keyword Tool's suggestion is mainly based on semantic relevancy and this makes it differ from Google.cn. And then you can also compare the relevant keywords suggested by Baidu in the Search Engine Result Page (SERP) (the bottom part of the screenshot). The middle part and the bottom part share more similarities.
Hence I believe that Baidu pulls the data from the keyword tool database for its suggestion on the SERPs.
Key takeaways
So, how can we interpret the findings in my exercise and how can they apply to search marketing practice in China?
Before answering these questions, I would like to point out that there are not many useful third-party keyword research tools in China. A lot of tools that claim to be developed for Chinese keyword research in fact use the same Keyword Suggestion data provided by the search engines. These tools fail to add any external data source to improve their intelligence.
Understanding the search intent is always the first step for Chinese search marketing. I suggest beginning with a methodology that separates research for search intent from the research for Keyword Suggestion.
As you can see in my test result, the Keyword Suggestion was not as same as the Search Suggestion. In fact, since the signal of search recency has been added into the suggestion, Search Intent is always more diverse and up-to-date. In my opinion, it reflects the customers' demand more accurately. Semantic relevancy, on the other hand, looks too linear.
When you develop your search marketing strategy or content strategy for China or any other country where Google is not the predominant platform, I highly advise you to use Search Suggestion in order to know more about how people search, what local synonyms they use, and why they search in that way.
This not only benefits the research for Keyword Suggestion for your search marketing, but also helps your audience to find you by applying search intent to your content marketing and developing engaging content.
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