You benchmark your company against competitors by first establishing which dimensions actually decide deals in your market, and then scoring per dimension who wins on what and why, with a source behind every score. A list stating 'we are strong on service, weak on price' is an opinion. A benchmark only becomes usable once every statement can be traced back to a quote, a review, a job posting, or a conversation with a customer who switched.
Most management teams compare on the dimensions they themselves consider important: quality, innovation, brand name. Those are often not the dimensions on which a customer actually chooses. A technical service provider with 120 employees believed for years that quality made the difference, until lost quotes revealed that response time and the clarity of the quote itself weighed just as heavily. Which dimensions those are differs per market and per customer segment, and can be uncovered by looking at what actually tipped the scale in won and lost deals, not at what is written in a strategy document.
Once the dimensions are established, the next step is to score them with evidence. For your own company that is usually available: quotes, loss reasons, customer conversations. For competitors this is harder, especially if they do not publish annual figures or customer satisfaction scores. Still, more can be found than it seems: job postings show where a competitor is investing, reviews reveal a pattern in what customers praise or miss, and customer cases show which argument a competitor puts forward itself. How you systematically gather that information about competitors that do not publish figures largely determines how solid your benchmark will be.
A benchmark that only confirms what management already suspected has little value. At a software company with 80 employees, the sales team indicated that integrations were the distinguishing argument. The lost deals showed something else: customers more often chose based on speed of implementation. That gap between assumption and evidence is precisely where a benchmark delivers value. How you make that distinction between presumed and proven strength is explained at what you actually win on.
With the dimensions and the evidence on the table, the comparison itself is relatively simple: a matrix with the dimensions on one axis and your company plus the peer group on the other, where every cell carries a score and every score a source. This immediately reveals where you have a gap that nobody in the peer group closes, and where everyone in fact scores equally strong, meaning the dimension no longer offers any distinction. Those empty spots in the market, where there is demand but no strong provider, are what a white space analysis maps out, as a follow-up step to the benchmark.
For a significant share of the companies that ask this question, the matrix turns out to reveal a leak on one specific dimension: price. Not because the price is actually higher than competitors', but because another dimension is not made clear enough in the sales conversation, leaving price as the only remaining argument. Whether that also applies to your company, and which dimension lies behind it, can be checked via the free eight-question loss-on-price check, which indicates within a few minutes which dimension is likely leaking. More on how that mechanism works can be found at why you are increasingly losing on price.
Start by gathering the last ten to twenty lost and won deals and note per deal which dimension tipped the scale according to the customer themselves, not according to your own assumption. Also record per competitor what evidence you can find on the dimensions that emerge from those deals. That gives the raw input for a matrix built on evidence rather than on gut feeling.
A gap that the benchmark exposes is not yet closed by this: that is execution work in processes, people, and systems. What that work costs and which part of it can be carried by AI is shown by the workscan at ftetoai.com.