Run a forty-five-minute discovery call. What comes back? A transcript of eight thousand words, maybe nine thousand. Somewhere in there is one paragraph that will actually influence your roadmap. The problem is that the other 8,800 words are not obviously noise at the time. They sound like signal. The customer is talking about their work, their problems, their team. The PM is listening hard and taking notes. And yet, when they compare notes with a colleague who sat in on the same call, they remember different things.
What noise looks like in a discovery call
Noise in discovery calls does not usually mean off-topic conversation. Most of it is one of three things. The first is context narration: the customer describing their current workflow in fine detail, which is useful background but not a signal about what to build. The second is one-off problems: incidents specific to their configuration or team that will not generalize to other accounts. The third is aspirational requests that the customer themselves would not prioritize if forced to rank their actual day-to-day frustrations.
All three sound substantive in the moment. The context narration helps the PM understand the customer. The one-off problem might seem urgent. The aspirational request is often the most interesting idea in the call. But none of them reliably predict what will improve retention or expand usage across accounts.
What makes something a signal
In our view, a signal is a customer statement that describes a friction point or a missing capability that is directly connected to the work they are trying to accomplish in your product. Not aspirational. Not historical context. Not a feature idea they brainstormed during the call. Friction with current behavior, expressed in terms of the specific thing they cannot do or the extra step they are forced to take.
The other defining characteristic of a real signal is that it recurs. A single account describing a problem is interesting but weak evidence. The same problem surfacing across four accounts with no overlap in their industries or team sizes is much stronger evidence. One of the most consistent patterns in discovery work is that individual calls are poor predictors of product priority, but call patterns across accounts are quite reliable.
The review process that makes the difference
Most PMs process discovery calls in one of two ways: either they write summary notes immediately after the call while memory is fresh, or they tag moments in a recording to come back to. Both approaches have the same bottleneck. The PM's interpretation is the only filter between the raw conversation and the roadmap. If their attention was caught by the aspirational feature request instead of the friction description, the signal does not make it into their notes.
A more effective process runs the transcribed call against a set of criteria before the PM reviews it: look for complaint language (broken, slow, missing, have to, workaround, wish); look for account-specific requests where the customer describes their workflow needing something specific; look for repetition within the call, where the customer returns to the same theme twice. That filtered view takes the PM fifteen minutes to review instead of forty-five, and it directs attention to higher-probability signal candidates.
This is what BuildBetter does with call transcripts. The extraction step is not about summarizing everything the customer said. It is about identifying the friction and feature-request statements, tagging them by theme, and surfacing only those to the PM for review. The rest of the call still exists in the transcript if you need it. But the default view is the filtered signal layer.
The caveat on automated extraction
Automated extraction improves the signal-to-noise ratio but does not eliminate the judgment requirement. A customer can describe a problem in polite or indirect language that reads as satisfaction on the surface. "We have found a way to make it work" is a workaround, not a compliment, but phrasing like that will not always get flagged by keyword matching. PMs still need to review extracted signals for calls with their highest-ARR accounts and any call where the customer's tone suggested frustration that did not fully surface in their words.
The goal is not to remove the PM from the loop. It is to make sure that when they spend time on call review, they are spending it on judgment calls that actually require their judgment, not on manually sifting through eight thousand words to find the four sentences that matter.
