Win Loss Analysis Prompts for Competitive Deals

What this job almost always produces is a list: five or six reasons, in order, sometimes with counts beside them. It travels further than anything else competitive intelligence makes, and it is read as a description of why buyers choose somebody else.

It is a description of the write-ups. Three filters run between a deal ending and a line appearing in that list. Somebody decided the deal was worth writing up. Somebody asked questions in their own words, which shaped what got answered. And somebody chose which sentence in the answer was the reason. A model reading the set sees the result of all three filters, and none of the filters themselves.

The first filter matters most, because you cannot see it working. Deals nobody wrote up are not under-represented in the list. They are missing from it. And the deals least likely to produce a write-up are the ones that faded rather than ended. So the reason that never appears is often the most common thing that happens. Naming it is a set problem rather than a coding one, and the no-decision tier has to appear in your competitor set before it can be counted anywhere.

Four inputs, and two of them are not documents. The code list and the denominator are numbers and definitions you bring to the run. Leave either out and this stops being an analysis and becomes a summary.

Put the denominator in the brief rather than in your head. An output that opens “across 19 write-ups, from 64 deals closed against this competitor” can be argued with. The same output without those numbers cannot. It takes one sentence to arrange. Where nobody is running the interviews at all, a list of win/loss questions is the cheapest way to start, because the hard part is asking rather than analysing.

Four passes, and they are deliberately not one pass. Ask for the coding and the ranking together and the ranking starts steering the coding. A model that has already decided price is the headline has a reason to read the next unclear sentence as being about price.

Almost nobody runs the second pass, and it repays the twenty minutes faster than anything else here. Run the same set cold, in a fresh conversation, then compare the two codings. What moved is what two careful readers would disagree about. Those codes are where a year of counting quietly goes wrong, and no amount of re-running the analysis finds them.

We asked a model to rank why deals go to a real competitor, supplying nothing, and read what came back against that competitor’s published pricing.

An ordered list is a number in disguise

Nothing in that answer would be challenged in a meeting. Each reason is real, each is put carefully, and there is not a statistic anywhere in it to argue with. That is the whole difficulty. A list numbered one to five says the first outranks the second, which is a claim about how often each happens. It arrives in a form nobody knows how to question, because it never printed a figure.

The same shape survives when the material is real. Nineteen write-ups produce a ranked list just as readily as nothing does, and the list looks identical. The only difference is whether the output says how many deals sit behind each position, and how many deals it never saw. That is why both of those are required inputs above rather than nice additions.

There is no grounded counterpart to the run above, and the reason is worth stating plainly rather than leaving as a gap. The grounded version of this job reads your own customers talking about their own companies. Nobody can publish that. Comparisons like this normally show the same question answered twice. This one cannot, and any article showing you a tidy set of win/loss interviews has written them. The nearest publishable equivalent is churn reason analysis, which has the same privacy problem and a larger sample.

Ask for themes and a model will give you six, well chosen, fitting the material neatly. Ask again next quarter and it will give you six again, also well chosen, also fitting, and not the same six. Both outputs are good. Together they are worth nothing, because a trend needs two measurements on one scale.

A coding scheme is plumbing rather than analysis. It gets agreed once, written down, and changed rarely and on purpose. That is what a coded archive has always needed. A model changes only the speed, and speed is exactly what hides the drift. Nobody re-reads a scheme that took four seconds to produce.

Read the unfitted pile before the counts

That is why every prompt above insists on somewhere to put the write-ups that do not fit, and forbids widening a code to swallow them. A closed scheme with an honest overflow tells you something a tidy one never will. Three deals that fit no existing reason is how a new competitor, a new objection or a changed buying process first shows up in your own data, usually a quarter before anybody names it.

The quotes are the other thing to protect. A coded row with the buyer’s sentence attached can be handed to a sceptical sales leader. The same row with a paraphrase will not survive one “did they actually say that?”. Verbatim material also travels further than analysis does. The passages worth pulling are the ones carrying a competitor’s argument back to you in a buyer’s own words, months after the deal. That is the raw material for a battlecard that argues with something real. The same quotes, heard while the deal was still open, are what competitor mentions in sales calls catch months earlier.

A synthesis describes deals that have already closed, which makes it the most backward looking thing competitive intelligence produces. By the time thirty write-ups exist, the oldest is a year old. The competitor in it has repriced, renamed something, or shipped the feature the loss was about.

That is not an argument against doing it. It is an argument for reading the findings against a current picture of the competitor, rather than on their own. A loss reason from March and a pricing page from March are one story. The same loss reason read in September, against September’s pricing page, is a different one. Only the second is the question your sales team is asking.

Loss reasons also travel upwards, and the shape changes on the way. A ranked list becomes a single sentence in an executive summary, and the recommendation it ends on is the one line no set of write-ups can supply. Give it the denominator, not the conclusion.

The output needs somewhere to live that is not a deck. A win/loss report holding the coded rows, the denominator and the quotes can be compared against the last one. That comparison is the only way a set of reasons becomes a trend rather than a fresh opinion every quarter.

Flares tracks what competitors change and dates every change. A set of loss reasons can then be read beside what was true when each deal closed, and what is true now. Why one buyer chose somebody else is not something monitoring can recover, and asking them is still the only method that works.