Refinement management of keywords in cross-border operation courses
For overseas merchants, cross-border operation is the daily work of opening traffic and increasing sales, and refined management of keywords is an important means to improve search matching and improve keyword rankings. Today’s cross-border operation course mainly focuses on five levels of keyword database, etymological object, traffic structure, traffic stratification, and traffic generalization to help you understand the refined management of keywords.
- The refinement of the keyword database
To refine the cross-border operation keyword database, the first step is to refine the underlying foundation. For word selection, the underlying foundation is the thesaurus, and the thesaurus composed of keywords searched by consumers is an effective basic thesaurus. The solution is to use ABA data. ABA is the data of Amazon’s official backend, and its authority is beyond doubt.
Second, the refinement of etymological objects
By comparing the number of traffic words of the variants, we can indirectly confirm the sales of different variants, and sort out the traffic words of different variants under the same Listing, especially the natural traffic words. It is much easier to choose which variant for cross-border operations.
- Refinement of flow structure
For cross-border operators, the traffic structure is to plan different keywords and play strategies for different traffic channels. Strong people have a system, and efficient traffic has a structure.
Fourth, the refinement of traffic stratification
If different types of traffic are the “horizontal” of the matrix, then a reasonable traffic stratification is the “vertical” of the matrix. Specifically, each “horizontal” traffic type will consist of which “vertical” keywords, including main keywords and long-tail keywords, and allocate budget and plan the rhythm of play.
Fifth, the refinement of the generalization strategy
Generalization is to obtain the traffic of related keywords on the basis of target keywords, which can be divided into related keywords and extended matching of phrase roots.