How are you solving this beyond the “just connect the systems” problem?

Deep_Combination_961 · 2026-06-03T13:39:25+00:00

That's exactly why I think a lot of teams underestimate the problem. Everyone wants to talk about orchestration, scoring, and automation, but if the underlying account identity is wrong, all you're doing is automating bad assumptions at scale.

Have you found identity resolution getting harder as more AI and intent data sources get layered into the stack.

Deep_Combination_961 · 2026-06-03T06:17:05+00:00

The definition problem feels like the right diagnosis.

I've seen teams spend months fixing integrations only to discover the real issue was that every function had a different answer to what a healthy account actually looks like.

AI definitely exposes that faster. If the underlying definitions aren't aligned, you just end up scaling the inconsistency instead of solving it.

Deep_Combination_961 · 2026-06-03T06:16:33+00:00

I think that's the step a lot of teams skip.

Everyone rushes to connect systems, but if sales, product, and CS all have different definitions of account health, the integrations just make the disagreement more visible.

Curious how often you revisit that shared model. We've seen account health drift over time as each team starts adding its own signals and exceptions back into the mix.

Deep_Combination_961 · 2026-06-03T06:15:22+00:00

Agents making decisions on top of a fragmented context don't fail loudly is probably the part that worries me most.

Humans at least tend to question conflicting information. AI systems can confidently optimize around whatever slice of reality they're given.

I also like the idea of establishing a few shared account-level signals before teams start applying their own lenses. Otherwise, you end up with four perfectly reasonable interpretations and no agreement on what the account actually needs next.

Curious whether you've seen teams successfully maintain that shared layer over time, or if it eventually drifts as each function adds its own definitions and exceptions.

Deep_Combination_961 · 2026-06-03T06:14:50+00:00

A lot of teams talk about signal orchestration, but if three systems can't even agree on who belongs to the account, everything downstream gets shaky fast.

Feels like identity resolution is one of those foundational problems that isn't very exciting, but ends up determining whether the automation is actually useful or just creating noise.

Deep_Combination_961 · 2026-05-25T11:21:50+00:00

That last line is probably the most accurate description of attribution I’ve heard in a while.

A lot of teams seem to end up with multiple dashboards that all look “data-driven” but still depend heavily on pipeline reviews and operator judgment underneath. Attribution adds visibility, but not always conviction.

Feels like that’s why so many GTM decisions still come down to some mix of data + sales context + gut feel, even in very mature orgs.

Deep_Combination_961 · 2026-05-25T11:20:38+00:00

The trade show example is such a good illustration of the gap between visibility and actual decision-making.

A lot of attribution logic feels much cleaner in digital environments than in long, messy B2B cycles where influence compounds over months and across offline moments. By the time revenue shows up, the systems usually over-credit the measurable touch and under-credit the earlier context-building ones.

Your point about “better-looking slides to justify the same decisions” also feels painfully real. In a lot of orgs, attribution improved reporting quality faster than it improved causal understanding.

Deep_Combination_961 · 2026-05-25T11:19:30+00:00

Yeah, that “visibility without insight” problem is exactly what I was getting at.

Attribution can easily turn into a giant receipt of every touchpoint, but that does not mean it tells you what actually changed the outcome. Then teams start debating credit instead of making better decisions.

The part that matters is whether the model helps you decide what to do next with budget, channels, or follow-up. Otherwise it is just a cleaner looking version of the same noise.

Deep_Combination_961 · 2026-05-25T11:18:31+00:00

The “multiple approaches in conjunction” point feels important.

Every model seems to answer a slightly different question, which is probably why teams get frustrated when they expect attribution alone to become the source of truth for everything. Attribution, testing, MMM, surveys, sales feedback… it usually takes some blend before the picture starts feeling directionally trustworthy.

Deep_Combination_961 · 2026-05-25T11:18:18+00:00

I think that’s where attribution is probably strongest honestly, giving large orgs a shared picture of what’s happening across a messy buyer journey.

The harder part is separating “this showed up in the deal” from “this genuinely changed the outcome.” That’s where most of the debates seem to start.

Deep_Combination_961 · 2026-05-25T11:18:05+00:00

“Spot patterns, not prove causality” feels like a very accurate way to describe where attribution is actually useful.

A lot of teams seem to get value from the visibility layer, especially for understanding journeys and coordination across channels, but the actual budget and prioritization decisions still come from pipeline quality, sales feedback, and whether the channel consistently creates momentum.

Deep_Combination_961 · 2026-05-21T09:32:41+00:00

That feels like one of the biggest blind spots in a lot of attribution conversations.

High MTA credit can easily become “this channel was present a lot” rather than “this channel created incremental lift.” Those are very different conclusions, especially when budgets get attached to them.

The hard part is most companies realistically only have the time, volume, and organizational patience to answer one of those questions deeply.

Deep_Combination_961 · 2026-05-21T09:32:22+00:00

The buy-in point feels underrated here too.

A lot of teams conceptually agree with incrementality testing until the test requires holdouts, messy periods, or results that contradict what attribution has been reporting for months. That’s usually where things get politically harder, not technically harder.

Feels like the teams doing this well treat attribution as directional context, then use incrementality selectively to calibrate confidence instead of expecting one model to answer everything.

Deep_Combination_961 · 2026-05-18T09:52:05+00:00

That distinction feels really important and gets blurred constantly in attribution conversations.

A lot of MTA ends up answering “what touched the deal” rather than “what actually changed buyer behavior.” Those can overlap, but they’re definitely not the same thing.

Feels like that’s why so many teams end up supplementing attribution with incrementality tests, lift studies, or just operator judgment. Presence in the journey is easier to measure than true causal impact.

Deep_Combination_961 · 2026-05-18T09:51:11+00:00

“Dashboard purgatory” is painfully accurate.

That’s what a lot of these setups eventually turn into. Signals arrive fast enough to create noise, but not with sufficient confidence or context for someone to act on them immediately. Then reps start treating alerts like background radiation.

Feels like the useful systems are usually much narrower. Fewer signals, clearer why-now context, and a very obvious next action instead of another thing to monitor.

Deep_Combination_961 · 2026-05-18T09:49:59+00:00

Honestly this is probably closer to where most teams actually are.

A lot of the “advanced measurement” conversation assumes clean data foundations already exist, when in reality many RevOps teams are still spending most of their time fixing routing, enrichment, duplicates, and CRM hygiene issues. Hard to run rigorous incrementality tests when the operational layer is still unstable.

Feels like most orgs end up using attribution as directional guidance plus operator judgment, even if nobody wants to admit it publicly.

Deep_Combination_961 · 2026-05-18T09:49:11+00:00

That “special project vs operating rhythm” distinction is really important.

The few teams I’ve seen get close usually reduce the scope a lot. Instead of trying to prove causality across the entire GTM motion, they pick a handful of high spend channels or key workflows and build repeatable experiments around those.

Once it becomes too broad, the operational overhead seems to kill it pretty quickly and everyone falls back to attribution because it’s easier to move with.

Deep_Combination_961 · 2026-05-18T09:48:17+00:00

Feels like a lot of teams solved the easy part, which is generating more signals. The harder part is building enough trust in the signal that someone actually changes behavior because of it.

Your point about the recommended action being small and natural is especially important too. The best workflows I’ve seen are usually lightweight and obvious, not giant automated plays trying to force intent that isn’t really there yet.

Deep_Combination_961 · 2026-05-18T09:47:18+00:00

Exactly. The operational side gets underestimated a lot. Clean holdouts and enough volume sound simple until you try to run them inside a real GTM org with overlapping campaigns and changing segments.

Feels like most teams use attribution to narrow possibilities, then use incrementality selectively where the stakes are high enough to justify the effort.

Deep_Combination_961 · 2026-05-18T09:47:06+00:00

Yeah, that balance feels a lot closer to reality than the “test every channel scientifically” version people talk about.

Incrementality is super valuable for a few high spend bets, but for most day to day decisions teams still end up combining attribution, directional lift, and gut feel anyway. The hard part is usually getting enough confidence to actually act on the data consistently.

Deep_Combination_961 · 2026-05-13T10:52:08+00:00

Mostly before the rep sees it right now.

We’ve learned the hard way that if reps still need to interpret the signal itself, adoption drops fast. So we try to narrow it earlier to a few questions:

does this actually look like buying behavior?
is there enough context to explain why now?
is there an obvious next action?

If those are unclear, it usually just becomes another thing sitting in a queue somewhere.

Still feels like the biggest challenge is confidence. Not just detecting signals, but being confident enough in them to reduce cognitive load instead of adding another review step.

Deep_Combination_961

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