There is no fixed calendar term that applies to every market. The frequency depends on how quickly your market moves and on concrete events that can change your position — not on an annual plan that happens to come around again.
An annual cycle is a habit from strategic planning, not from market reality. A company of 150 employees that has a competitive analysis produced every autumn may already run into a surprise in June: a competitor lowers its price, a new player enters, or an existing player launches a feature that hits exactly the dimension on which you always used to win. By the time the annual update appears, the damage is already visible in the pipeline. The question is therefore not "when is it on the agenda again", but "which dimensions currently determine whether we win or lose a deal" — and that changes per quarter faster than most planning cycles allow for.
There are a few practical triggers that carry more weight than a date on the calendar:
For an organization of 80 employees in a niche market, that may mean nothing changes for two years, so a repeat adds little. For a company of 250 employees in a market with fast product development, the same research can already be outdated within six months.
Not every repeat needs to redo the full analysis. Often only part of the evidence matrix shifts: one competitor improves its delivery time, another raises its price, a third loses a strong salesperson. The question then is which dimensions currently actually decide the deal — something that can differ by market phase and is set out on the page about which dimensions determine whether you win in a market. If those dimensions remain stable, a targeted update of the scores is often sufficient. If the dimensions themselves shift — for example because price gives way to implementation speed as the decisive factor — then a broader revision is needed, including the substantiation behind each score. How that substantiation is built without slipping into assumptions is described at how you score a competitor without making assumptions.
A management team at a company of around 120 employees initially had the competitive analysis updated every autumn, tied to the budget cycle. After two quarters of a declining win rate, it turned out that a competitor had, in the meantime, improved a dimension that had still been scored as weak in the last analysis. The information was there — in lost deals and in conversations with sales — but wasn't linked to the evidence matrix because that would only reopen in November. From that point on, the repeat was tied to the loss rate on quotes instead of to the calendar: as soon as that rate crossed a certain threshold, the analysis reopened on the dimensions most often cited as the reason for loss.
If it's unclear whether the current shortfall is coincidence or a pattern, a first indication can be obtained without starting a full process. The free loss-on-price check consists of eight questions and shows which dimension is likely leaking in the deal cycle — a starting point for determining whether a full repeat makes sense now or can still wait.
If that check, or your own gut feeling about lost deals, points to a concrete shortfall, the next question is no longer when you measure again, but what you do with that shortfall — a choice that is explained at what you do with a shortfall on a dimension. Closing a gap on a dimension is, after all, not a matter of measuring again, but of execution in processes, people, and systems. What that execution costs and which part of it AI can take over is set out in the work scan at ftetoai.com.