Keyword mapping requires more human effort, although anyone who does it regularly will have a number of tips and best practices in stock to make their work easier. However, before machines replace us in keyword mapping, we can briefly look at certain patterns and rules of search queries (with many exceptions), awareness of which can again help us a little. MODEST BUT USEFUL INSIGHTS FROM PRACTICE If we divide the dataset by the number of words in the search queries, we notice that among the single-word terms we find basic keywords that often form the building blocks of other, more refined searches (watch, perfume, building blocks, computer). We also find a number of specific or slang terms. Therefore, it may be worthwhile to focus primarily on categorizing these words.
The vast majority of terms in the dataset will be two- and three-word phrases. These will usually be basic keywords enriched with qualifying adjectives. It is thanks to this refinement that the Bosnia and Herzegovina Mobile Database dataset will gradually begin to take on the outlines of a potential structure. Three- and multi-word expressions will also contain prepositions, e.g. on, for, to, which will also help us define purpose, focus, destination, etc. Obviously, the more words a term contains, the more specific and less searched for it is. This needs to be taken into account when designing new pages.
If we do not have enough relevant assortment, it may be more appropriate to optimize the parent category for these terms. However, in total, these long-tail terms can bring more conversions than the more searched and more general terms that most of the competition targets. The complexity of the search query structure is directly proportional to the complexity of the product, how long it has been on the market, and its financial value. The purchasing process is then longer and there are more selection parameters. We also often see that inaccurate labels have higher search volume than technically correct labels.
