Turkiye's SEO Agency Market: Mapping the Competition
How to map the competitive landscape with DR and traffic estimates, where those metrics mislead, and how to validate them against first-party data.
By Roozbeh Nazari · CEO
Conversation about the SEO agency market in Turkiye tends to sit at one of two extremes: impressions resting on no data at all, or a single metric ripped out of its context. Neither produces a decision.
This article is not a ranked list. That is deliberate: we are not publishing any agency's Domain Rating or estimated traffic below. The reason is that figures of that kind are stale the moment they are published and, more importantly, that a number shared without its source is not verifiable for the reader. Instead we are giving you the method for producing the map yourself; the results will come from your tool subscription, on your measurement date, and in that form they are far more useful.
Step 1: define the competitive set correctly
Most competitive analyses go wrong at the first step: they treat the sites appearing on page one for "SEO agency" as the competitors. That substitutes the search result itself for a market definition, and it produces two problems. First, a great many of the sites appearing for that query are content sites rather than agencies. Second, some of your real competitors may not appear for that query at all, because they too are working in vertical queries.
A more accurate definition is the union of three sets:
- Query competitors: the sites appearing across the query set you target. Derive this from a list of at least twenty or thirty queries, not from a single one.
- Pitch competitors: the agencies named in sales conversations. That list already exists in the sales team; it is simply that nobody writes it down.
- Vertical competitors: players specialised in the sector you work in rather than general agencies. If you work in the clinic vertical, that set is more decisive than the general SEO agencies.
Combining the three sets typically produces a list of between fifteen and thirty. Write that list into a table; it is the skeleton of the map.
Step 2: collect the metrics knowing what they are
Now the data collection step. What matters here is knowing what each metric measures and what it does not.
Domain Rating is, in Ahrefs' own definition, a proprietary metric showing the strength of a site's backlink profile on a logarithmic scale from 0 to 100. So DR is a tool metric, not a Google metric. That has two consequences: first, a DR gap does not directly mean a ranking gap; second, because of the logarithmic scale the distance between 40 and 50 is not the same as the distance between 70 and 80. Read DR in the table as a size class for the link profile, not as a ranking estimate.
Estimated organic traffic is what its name says: an estimate. Tools produce it by multiplying the keyword rankings they have detected by their own click-through models. That production chain has three separate error sources: the coverage of the tool's keyword database, the accuracy of the volume estimates, and how well the click-through model matches real behaviour. On Turkish queries and vertical topics, coverage is narrower than on general English queries.
The practical consequence: do not use estimated traffic as an absolute number, use it comparatively against the other competitors' figures from the same tool. Because the same tool's error is distributed similarly across the whole list, the comparison is far more reliable than the absolute value.
Other fields worth collecting: referring domain count, number of ranking queries, the site's language versions, blog publishing frequency and last publication date, and the vertical breakdown of the service pages. The last two are not in the tools; you have to look manually, and they tell you the most about the competition.
A warning on data collection discipline, too. Collect every row on the same day, with the same tool and the same country setting. Comparing figures collected on different days in the same table is the single biggest error source in it, because the gap between tools' index updates can exceed the real competitive gap. Add the measurement date and the tool used as separate columns.
Step 3: read estimates against first-party data
Skip this step and the whole analysis floats free. You have an estimated figure for the competitor; for your own site you have real data. Putting the two side by side shows you how far the tool is off on your site, and that deviation ratio gives you a correction factor for reading the competitor estimates from the same tool.
Applying it is simple: take the real click count for your chosen period from the Search Console performance report, take the tool's estimate for your own site for the same period and country, and calculate the ratio. That ratio is usually not 1, and its direction is not constant either. Add the deviation you find as a column in the table so that everyone remembers the same thing when reading the competitor numbers.
The same logic holds on the technical side: read tool-supplied information about a competitor's site structure through the methods you have validated on your own site. Page counts reported by tools on multilingual setups, for example, may not reflect reality because of faulty language markup; we covered how those errors are detected in our hreflang errors article.
The same cross-reading is needed on the AI search side, and tool data there is much weaker. There is no reliable public metric showing how often a competitor appears in assistant answers; the only honest way to measure it is to repeat a fixed question list at regular intervals and record the answers. We explained the method in detail in the GEO playbook.
Step 4: get a decision out of the map
Once the table is full the real work starts. The point of a competitive map is not learning who is bigger but seeing where the space is. Three questions are enough.
First: in which topic clusters is everyone on the list weak? Those are usually newly forming topics, and a link profile advantage helps least there.
Second: in which vertical has none of the competitors gone deep? Vertical specialisation is one of the few things that can offset a DR gap. That is why we work in the clinic vertical; we describe how we position on our clinics page.
Third: in which language and market combination is the list empty? Most Turkiye-based agencies stay confined to Turkish and English; competition on the Arabic and Persian side is noticeably sparser.
The answers to those three questions turn into a content and technical roadmap. We build the technical leg of that roadmap on the technical SEO side and the content leg on the content SEO side.
One last warning: refresh this map quarterly and write the measurement date on every refresh. An undated competitive table turns into wrong information within a few months and, worst of all, gives no sign that it has.