What can we show in the section?
Posted: Wed Jan 22, 2025 8:36 am
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What to do if some queries contain words that fit into different groups?
Example: where should the query “forward children’s bicycle” be classified – in the group of children’s bicycles or in the group of forward bicycles?
Let's compare:
what does the consumer need? - a children's bicycle is definitely needed here,
"forward bicycles" - here are adult bicycles (doesn't fit right away),
"bicycles for children" - you can choose here (there is a brand called forward - it is suitable).
Step 2: Identify groups and subgroups in the keyword array
Step 2: Identify groups and subgroups in the keyword array
Step 3: Clean the list, set priorities
The semantic core has begun to take shape, and lawyer phone number data we can clean it of junk. We delete:
rental and repair (since we do not do them),
queries like “sell a bicycle” (most likely, a person is looking for free classifieds boards),
brands that are not in our range.
A summary table is then created that will show the total frequency in each group and subgroup.
The query groups should become sections of the site. We got groups with the following total frequencies:
Step 3: Make a pivot table and select the most attractive categories
Step 3: Make a pivot table and select the most attractive categories
We exclude groups that have too little traffic.
4. Distribute across pages
In the final step, we plan to create pages that match the queries.
Again, we make sure that the pages are not repeated. We especially monitor synonyms: for example, “inexpensive” and “cheap” are the same thing for Yandex.
What to do if some queries contain words that fit into different groups?
Example: where should the query “forward children’s bicycle” be classified – in the group of children’s bicycles or in the group of forward bicycles?
Let's compare:
what does the consumer need? - a children's bicycle is definitely needed here,
"forward bicycles" - here are adult bicycles (doesn't fit right away),
"bicycles for children" - you can choose here (there is a brand called forward - it is suitable).
Step 2: Identify groups and subgroups in the keyword array
Step 2: Identify groups and subgroups in the keyword array
Step 3: Clean the list, set priorities
The semantic core has begun to take shape, and lawyer phone number data we can clean it of junk. We delete:
rental and repair (since we do not do them),
queries like “sell a bicycle” (most likely, a person is looking for free classifieds boards),
brands that are not in our range.
A summary table is then created that will show the total frequency in each group and subgroup.
The query groups should become sections of the site. We got groups with the following total frequencies:
Step 3: Make a pivot table and select the most attractive categories
Step 3: Make a pivot table and select the most attractive categories
We exclude groups that have too little traffic.
4. Distribute across pages
In the final step, we plan to create pages that match the queries.
Again, we make sure that the pages are not repeated. We especially monitor synonyms: for example, “inexpensive” and “cheap” are the same thing for Yandex.