Curious if others are seeing this too. I’ve been digging into early intent signals beyond traditional tools, and what I keep running into is this gap: most “intent” lights up after reps already feel something is happening. I’m experimenting with operational signals inside CRM + workflows (manual workarounds, sudden field changes, internal nudges) to see which ones actually precede real pipeline movement. Early days, but I’m realizing defining which signals are worth acting on is harder than the tooling itself. If you’ve tried anything similar, curious what worked (or didn’t).
How much are you reviewing historical data to assess what triggers to watch? And what are you reviewing?
Great question, Evan S.. I’m mainly looking at the last 3–6 months of conversational and CRM activity to spot early operational shifts. Less about keywords, more about context changes, e.g. when teams stop asking about features and start struggling with implementation, data migration, or onboarding friction.
New deals or renewals or both?
Evan S. Both, but with different signals. For new deals, I’m watching early buying friction (handoffs breaking, repeated clarification questions, stalled next steps). For renewals, it’s usage decay + support escalation before it shows up as churn risk. Same pattern, different surfaces, timing is the real differentiator.
Adding to your lists which are great, here's what we do for deal progression: We mine MEDDPICC, was there next steps, new risks identified, do we have EB signoff, etc. from meeting transcripts. Each of these is its own one shot LLM prompt. We then put them in a deal risk dashboard so managers can see which deals are on track. Recently I started summarizing all meetings, emails/calls, and slack msgs to create a bullet point executive summary. For future context purposes, I also extract whether there were next steps. These can be used for future alerts, or as context in conversational agents (e.g. automatically runs workflows to complete each next step). I shared the full prompt on substack if you'd like! Planning on writing about the deal progression system soon too.
So often, it's not that we need new signals, it's that we need to execute better on what we know works today. That's why I think your focus is super valuable.
Jordan G. This is exactly the tension I’m seeing too. A lot of signals show up only after momentum has already shifted. What I’m focusing on is the *earlier friction*, when deals start slowing down quietly: reps creating workarounds, next steps becoming vague, or activity increasing without real progress. The goal isn’t better reporting, but catching those moments early enough to actually change the outcome.
Evan S. we store raw transcripts in the data warehouse, we also have an embeddings db for use cases that don't require specificity (reduces our costs), and then we store summaries/etc that are generated from transcripts in the data warehouse as well. Hope that helps! Gizem Ç. Good luck with this! Some other thoughts: Anything that applies across multiple deal stages (vague next steps) would be a higher priority for me, and then I'd move on to the signals which apply to the stages where you see the greatest conversion drop off. I am curious how you are evaluating things like vagueness and progress? I'd love to implement this too! I imagine you have some criteria you're looking for (e.g. the next steps did not specify a date or participants, etc.) Yael M. Good call out!
Gizem Ç. This resonates. In teams I’ve seen, the tooling isn’t the hard part — it’s agreeing in advance which signals justify action vs review vs ignore. Curious: where do you see the most confusion today — thresholds, ownership, or incentives?
Jordan G. That’s really helpful, especially prioritising signals that span multiple deal stages. I’ve been experimenting with vague next steps and timeline drift as early indicators. Still figuring out the evaluation criteria, would love to hear how you define when vagueness becomes actionable risk.
Yael M. Great question, I actually think 3rd party win-loss interviews are extremely valuable for understanding why buyers made a decision. What I’ve been noticing though is that they tend to explain the decision after it’s already formed. The gap I’m exploring is identifying behavioural hesitation while the deal is still active, things like stakeholder engagement slowing down, next steps becoming less concrete, or evaluation activity becoming fragmented. Ideally I see win-loss insights helping define which behaviours matter, and operational signals helping teams act earlier.
Gizem Ç. these are the risk categories that the signals roll up into. Then we have a risk category reasoning:
The primary risk is a Pain/Price Mismatch. Company has a clear mandate to move off expensive incumbents like Y and Z, but they are heavily pushing back on pricing. They are seeking a significant cost reduction (aiming for $4M-$8M) which is misaligned with our current proposals based on their usage volumes. This fundamental disagreement on value versus cost is the central point of contention in the negotiation.
Imho, all the signals you are looking to create end up rolling up into a risk category, but open to feedback on that! Akhilesh w. at our company, I just pitch signals, get feedback quickly, and go build / test them. What helps is having a single decision-maker where the buck stops and can just say "we're doing this". Hope that helps!
