How Signal Gets Distorted
If you follow customer signal through a GTM workflow, there are a few specific places where it tends to lose its shape.
If you take the idea from the last post and follow it a bit further, the more useful question isn’t whether signal gets reshaped as it moves across teams, because that part is almost inevitable. What matters more is understanding where that reshaping actually happens in practice, and why it tends to show up in the same places over and over again.
It’s easy to talk about this in abstract terms, but when you look at how work actually moves through a GTM organization, the breakdown points are surprisingly consistent. You can usually trace them back to a small number of transitions where information has to change form in order to be useful to the next team, and those transitions are where most of the signal loss happens.
Where translation tends to happen
In most GTM workflows, customer signal moves through a few predictable steps before it influences a decision. It usually starts in a raw form, whether that’s community activity, support tickets, sales conversations, or product usage, and from there it has to be turned into something that can travel across teams.
That typically involves a sequence of changes that look something like this:
Summarizing what’s happening into a smaller set of patterns
Grouping those patterns into categories or themes
Framing them in a way that fits the next team’s priorities
Each of those steps is necessary, because without them the signal is too noisy to act on. At the same time, each step introduces a small shift in meaning, where something specific tied to a particular situation becomes more general and easier to process, but also easier to deprioritize once it’s competing with other inputs that have already been structured in a similar way.
Why this doesn’t get caught earlier
One of the reasons this is difficult to address is that the process makes sense from inside each function. Product needs to see patterns across many customers rather than individual anecdotes, marketing needs language that can scale, and sales and customer teams need signals that map to pipeline and retention.
From within each of those contexts, the translation step feels like the right thing to do. The issue is what happens when those translations aren’t connected to each other, because over time you end up with multiple versions of the same signal that all make sense locally but don’t line up in a way that drives a clear decision globally.
Where it breaks inside workflows
If you map this to how work actually gets done, there are a few points where the signal tends to break down, and they’re usually tied to moments where ownership shifts or where inputs get combined.
One is the initial handoff from a raw source into a structured system, which is where context is most likely to get compressed. Another is when that structured input gets combined with other inputs, because at that point it’s no longer evaluated on its own terms but as one item in a broader set of priorities. You also see it when outputs get pushed back out into the market, because messaging, enablement, and product updates are all shaped by whatever signal made it through those earlier steps.
Across those moments, the same pattern tends to show up:
The signal becomes easier to compare
It becomes easier to prioritize at a high level
It becomes harder to connect back to the original context
That tradeoff is part of what allows teams to operate at scale, but it’s also where the gap starts to widen in ways that are hard to see from inside any single function.
Why adding more signal doesn’t fix it
A common response to this problem is to try to improve visibility by adding more dashboards, more reporting, and more ways to surface customer feedback. Those can help at the edges, but they don’t address what’s happening inside the workflow itself.
If the underlying process requires signal to be translated multiple times, then adding more signal just increases the volume moving through the same system. You end up with more data, but the same points of breakdown, which is part of why community can feel like it’s working and not working at the same time. It generates a lot of useful signal, but that signal is still subject to the same translation steps once it leaves that environment.
What this looks like in practice
If you look at how this plays out across functions, it tends to follow a familiar pattern. Product sees a set of categorized inputs that are easy to compare but harder to tie back to specific customer situations, marketing works with language that has been generalized enough to scale but doesn’t always reflect how customers actually describe their experience, and sales and customer teams operate on signals that map cleanly to their metrics but don’t always capture what’s changing underneath.
None of those views are wrong. They’re all shaped by how the work needs to happen. The issue is that they don’t reconnect, which makes it harder to act on the underlying situation in a coordinated way.
Where this leaves you
If you’re trying to improve how customer signal influences your GTM, the goal isn’t to eliminate translation, because that’s not realistic and it’s not even desirable. The goal is to understand where it’s happening and what’s being lost at each step, which usually starts with a simple question: at what point in your workflow does the signal stop looking like the thing you originally observed?
Once you can identify those points, you can start to decide where you want to preserve more context, where you need better alignment between teams, and where the system itself needs to change. That’s a harder problem than just improving visibility, but it’s also where most of the leverage tends to be.
Decoded Takeaways
Customer signal doesn’t disappear inside GTM workflows. It gets translated into forms that are easier to process and prioritize, and that translation happens at predictable points.
Those points include the initial shift from raw input to structured data, the combination of that data with other inputs, and the way decisions get pushed back out into the market. Each step makes the signal more usable within a specific system and less connected to its original context, which over time creates gaps between how different teams understand the same underlying situation.
Improving visibility can help, but it doesn’t address where the signal is actually breaking down. The more useful approach is to identify where translation is happening and decide where you need to preserve more of the original meaning.