That little green number in Ubersuggest looks so reassuring, doesn't it?<\/p>\n
You search for a keyword. You see an SEO Difficulty score of 15. You think, "Easy win." You write the article, publish it, and wait. And wait. And three months later, you're still on page four of Google.<\/p>\n
What went wrong? The score lied to you. Well, not exactly. But it didn't tell you the whole truth either.<\/p>\n
Let's break down what Ubersuggest's SEO Difficulty score actually measures, how to use it properly, and why it can seriously mislead you if you're targeting the Malaysian market.<\/p>\n
Ubersuggest's SEO Difficulty (SD) score runs from 0 to 100. Lower means easier. Higher means harder. Simple enough on the surface.<\/p>\n
But what's happening underneath?<\/p>\n
According to Ubersuggest's own documentation<\/a>, the SD score is primarily based on backlink data<\/strong>. It looks at the top-ranking pages for a keyword and analyses how many referring domains point to those pages. More backlinks on competing pages means a higher difficulty score.<\/p>\n Here's the rough breakdown:<\/p>\n The calculation is straightforward. It asks one question: how many quality backlinks do the current winners have?<\/p>\n That's it. That's the whole formula at its core.<\/p>\n It doesn't deeply weigh content quality. It doesn't factor in search intent alignment. It doesn't consider how well-optimised those competing pages are on-page. Backlinks are the star of this show, and everything else is a supporting actor at best.<\/p>\n Credit where it's due. For popular English-language keywords with high search volume, Ubersuggest's SD score is genuinely useful.<\/p>\n Here's why. High-volume keywords attract lots of competing pages. Lots of competing pages mean lots of backlink data. Lots of backlink data means the algorithm has plenty to chew on.<\/p>\n The score becomes a decent shortcut. If you're doing keyword research for a US-based blog targeting "best running shoes" or "how to start a podcast," the SD score gives you a reasonable picture of the competitive landscape.<\/p>\n Think of it like a weather forecast. When the meteorologist has decades of data for your city, the forecast is pretty reliable. You trust it enough to leave your umbrella at home.<\/p>\n But what happens when you move to a city nobody tracks?<\/p>\n This is where things get tricky for Malaysian businesses and SEO practitioners.<\/p>\n Ubersuggest pulls its data primarily from global databases. Its backlink index and keyword databases are heavily weighted toward English-language, Western markets. When you target Malaysia-specific keywords \u2014 especially in Bahasa Melayu or for niche local industries \u2014 the underlying data gets thin. Really thin.<\/p>\n And thin data produces unreliable scores.<\/p>\n A keyword might show an SD of 12. Looks like a cakewalk. But Ubersuggest might only be analysing a handful of competing pages with incomplete backlink data. The actual competition could be much stiffer than what the number suggests.<\/p>\n The reverse happens too. A local keyword might show a high SD because one or two government sites (with massive backlink profiles) rank for it, even though the rest of the results are weak pages you could easily outrank.<\/p>\n Here are the specific situations where Ubersuggest's SD score tends to mislead in the Malaysian context:<\/p>\n\n\n
\n \nSD Score<\/th>\n Difficulty Level<\/th>\n What It Means<\/th>\n<\/tr>\n<\/thead>\n \n 0\u201329<\/td>\n Low<\/td>\n Few backlinks on competing pages<\/td>\n<\/tr>\n \n 30\u201349<\/td>\n Medium<\/td>\n Moderate backlink competition<\/td>\n<\/tr>\n \n 50\u201369<\/td>\n Hard<\/td>\n Strong backlink profiles needed<\/td>\n<\/tr>\n \n 70\u2013100<\/td>\n Very Hard<\/td>\n Dominated by high-authority sites<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Why the score works (sometimes)<\/h2>\n
The Malaysia problem: thin data, unreliable scores<\/h2>\n
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