You will be able to prioritise keywords by relevance, intent and value, and group similar searches into one target.
By the end of lesson 2.3, Priya had a spreadsheet of 20 searches, intent labels and volume guesses from two tools. Looking at it, she felt the same thing most people feel at this stage: there was too much on it. "Maths tuition Tampines", "maths tuition centre Tampines", "Tampines maths tutor", "P5 maths tuition", "P5 maths tuition east", "PSLE maths tips", "how to solve PSLE maths heuristics", and a dozen more. She could not write a page for each, and she should not try.
This lesson does two things with a list like that. It groups searches that belong together, and it scores the groups so you know which to work on first.
Many searches on your list are different ways of asking the same thing. "Maths tuition Tampines" and "Tampines maths tutor" are typed differently by different people, but they want the same answer. Writing a separate page for each would split your effort and could leave two of your own pages competing for the same results.
The most reliable test for whether two searches belong together is the results page. Search both, signed out and on a Singapore connection. If the top results are largely the same pages, Google treats them as the same need, and one page can usually win both. If the results are mostly different, they are separate needs and probably need separate pages.
Call each group a keyword cluster: a set of searches that share intent and mostly share results, so one page can serve them all. Give each cluster a short name and choose one search in it as the main search, usually the clearest or most common way of putting it.
When Priya checked, "maths tuition Tampines", "maths tuition centre Tampines" and "Tampines maths tutor" shared most of their top results. That became one cluster. "P5 maths tuition Tampines" showed different results, with level-specific pages ranking, so it became its own cluster. "PSLE maths tips" and "how to solve PSLE maths heuristics" shared some results but not most, so she kept them apart for now.
Once your searches are clustered, score each cluster from 1 to 3 on three questions. A simple scale is enough.
Closeness to a sale asks how near the searcher is to paying. A transactional or commercial cluster such as "P5 maths tuition Tampines" scores 3. A navigational search for your own name is close to a sale but you probably rank for it already. An informational cluster such as "PSLE maths tips" scores 1, unless you can show it leads people to enquire.
Ability to serve asks whether you can give the best answer for this search. If Priya teaches P5 and P6 maths in Tampines, she scores 3 for those clusters. If a cluster is "Sec 3 A-maths tuition" and she does not teach secondary, she scores 1, however tempting the numbers.
Competition asks how hard the current results look, scored in reverse so that easier is higher. If the top results are big directories and national brands with detailed pages, score 1. If they are thin pages, outdated posts or sites that only partly match the intent, score 3. Use your reading of the results page from lesson 2.2 more than any tool's difficulty score.
Add up the three scores. Here is Priya's arithmetic for two of her clusters, with her own judgement as the input. "P5 maths tuition Tampines" scored 3 for closeness, 3 for ability and 2 for competition, a total of 8 out of 9. "PSLE maths tips" scored 1, 3 and 1, a total of 5. The first cluster goes near the top of her list.
Long, specific searches tend to score well on all three questions. Someone typing "halal birthday cake delivery Jurong" has told you the occasion, a requirement, a service and a place. There are fewer pages that fit that exactly, and the person is close to ordering. A baker who makes halal-certified cakes and delivers to Jurong could serve it very well, and the competition is likely to be lighter than for "birthday cake".
These searches show small volumes in every tool, which is why many businesses ignore them. For a small business that can only build a few good pages a month, they are often the best place to start.
Some clusters will have attractive numbers and no honest fit. Priya could write a page targeting "best tuition centre Singapore", but she runs one centre in one area and cannot be the best answer for an islandwide comparison. A home baker who does not have halal certification should not chase halal cake searches, however close they are to a sale.
Dropping these is part of the method. A page written for a search you cannot serve either fails to rank or ranks and then disappoints people, and lesson 1.3, Helpful content and E-E-A-T in plain words, explained why trust matters so much here.
Take your list of 20 and start sorting it into clusters, checking the results page whenever you are unsure whether two searches belong together. The activity below asks you to group the whole list and score each cluster.
Group your keyword list into clusters and score each cluster on closeness to sale, ability to serve and competition.
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