Most early-stage positioning is decided by whoever spoke last in the meeting. A founder writes about the thing they find interesting, an advisor suggests a topic, a competitor publishes something and the queue is rearranged. Six months later there are thirty pages, no pattern, and no way to say which of them did anything.
There is a cheaper method, and it does not require a budget or an agency. Decide what to publish with an explicit scoring rule, publish in that order, measure what each item produces, and let the measurements reorder the queue. That is all "algorithmic positioning" means. It is not artificial intelligence writing your copy, and anyone selling it as that is selling you a volume problem you do not have.
What does an algorithmic approach to positioning actually mean?
It means the decision about what to publish next is made by a written rule applied to measured inputs, not by whoever feels strongest about it. You list every question a buyer might type, attach a demand figure and a difficulty figure to each, score them, and work down the list. When a published item produces enquiries, its neighbours move up. When it produces nothing after a fair window, its neighbours move down.
The rule is deliberately simple, because a rule nobody can compute in their head gets abandoned in the first busy week.
The scoring rule
Four inputs, all obtainable without paid tooling in a first pass.
Demand. How many people search this phrasing each month. Approximate is fine; what matters is the order of magnitude, not the decimal.
Answer quality of what already ranks. Read the top five results. If they are thin, generic, or written for a different company size than your buyer, this is an opening. If three of them are excellent and specific, it is not, whatever the volume says.
Distance from what you sell. A page can rank beautifully and attract nobody who could ever buy. Score honestly: can a reader of this page become a customer in one step, two, or never.
Cost to write it well. Some pages need a specialist's afternoon. Some need a specialist's week. In a small company this is the real constraint and it is almost never in the spreadsheet.
Score is demand, multiplied by the opening, divided by distance and cost. Any consistent weighting works. The value is in applying the same one every time, not in the arithmetic being clever.
Why the ordering matters more than the volume
A new domain has no history, and the pages you publish first shape what search engines and AI assistants conclude the site is about. Twelve pages tightly clustered around one problem establish a subject; forty scattered pages establish nothing and compete with each other for the same weak signal.
For a project in health or finance the effect is stronger, because both are subject to stricter evaluation of who is qualified to say something. A site that is unmistakably about one narrow area accumulates that credit. A site that is about nine areas accumulates it nowhere.
This is also why the method survives contact with a two-person team. Twelve items, in order, with a rule for what comes next, is a plan a founder can actually execute between other work.
It is worth remembering who else is competing for that attention. The Swiss Federal Statistical Office records that the overwhelming majority of businesses in Switzerland employ fewer than fifty people. Almost every company you are competing with for a search result is also small, also without a marketing team, and also publishing whatever somebody felt like. Consistency of method is a genuine advantage precisely because it is rare, not because it is clever.
What to measure, and what most people measure instead
The default dashboard measures the things that are easy to collect, which are almost never the things that decide anything.
| Commonly tracked | What it actually tells you | Track instead |
|---|---|---|
| Page views | How many people arrived | Enquiries per page, per month |
| Bounce rate | Very little on an informational page | Whether the next page was the service page |
| Followers | Nothing about pipeline | Enquiries mentioning a specific page |
| Keyword rank | Position, not demand captured | Impressions and clicks for that phrasing |
| Time on page | Reading speed, mostly | Whether an enquiry followed within the session |
The right-hand column requires that conversions are actually instrumented. If a form submits and fires no event, none of it is measurable and the whole method degrades to guessing with extra steps. That is worth fixing before anything else; we describe the general version of the problem in measuring the ROI of digital transformation.
Where the algorithm stops working
Three places, and being honest about them is what separates a method from a pitch.
It cannot invent expertise. The rule tells you which question to answer. It cannot answer it. If nobody in the company knows something a generic article does not, the correct output of the process is to publish nothing and go and learn something instead.
It is blind to demand that does not exist yet. Scoring by search volume finds problems people already know they have. A genuinely new category has no volume by definition, and for those the channel is direct conversation, not publishing. Most early-stage companies are not in this position, but the ones that are will waste a year if they follow the rule anyway.
It has a floor on time. A new domain publishing well typically needs several months before search results respond, and no amount of optimisation compresses that to weeks. Any plan that depends on revenue from search inside a quarter is a plan that will be abandoned in month two.
How long before an early-stage project sees results from this?
Direct outreach produces conversations within weeks. Published pages take longer: in the projects we have observed, a new domain publishing consistently starts to see meaningful search-driven enquiries somewhere between three and nine months, depending on how competitive the subject is and how specific the pages are. Founders who plan both timelines at once stay funded through the gap. Founders who plan only for the second one run out of patience first.
What this costs to run
A first pass takes about a week: enumerate the questions, score them, write the twelve-item queue. After that it is one afternoon a month to re-score against what the measurements say, plus whatever the writing itself takes.
There is no software to buy. A spreadsheet is sufficient and is easier to argue with than a tool, which matters, because the argument about whether an item really belongs at position three is where most of the thinking happens.
If any part of this ends up automated, it should be the scoring and the reporting, not the judgement — the same boundary we draw for AI adoption beyond pilots. Where machine learning genuinely helps is in clustering thousands of query variants into the dozen real questions underneath them, which is our AI and machine learning work applied to a small problem, and it is worth doing only once the manual version is already running. For technology and startups specifically, the manual version is usually enough for the first two years.
Related reading
- Measuring the ROI of digital transformation
- AI adoption in Swiss companies: getting beyond pilots
- Digital transformation in an SME: the first 90 days
- AI and machine learning services
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