Pipedash

Attribution that survives scrutiny.

Every multi-touch model splits credit across touchpoints. Pipedash decides that split by reasoning over the full record of a qualified opportunity, and cites the evidence for every dollar.

Dealprint

Prime Analytics · $70.7K
53%22%15%10%Prime$70.7K
Marketing53% · $37.5K
Inferred pool22% · $15.5K
SDR15% · $10.6K
3rd-party10% · $7.1K

There has never been a golden age of B2B attribution… until now.

Legacy multi-touch models were a reasonable answer for an old game. They assume the CRM record is the truth, then pour position-based weights over it: 40% to the first touch, 40% to the last, the middle splits the rest, and so on. But a fourteen-month enterprise deal with three logged touchpoints isn’t a data set; it’s a rounding error with a dashboard.

Legacy multi-touch models
Pipedash
✕Only credit what the CRM happened to log
✓Reconstructs the full record, including what was never logged, and cites it
✕Credit follows position: first touch, last touch, fixed weights
✓Credit follows evidence: reasoning over what actually moved the deal
✕Allocations are a model assumption
✓Allocations trace to specific, cited touchpoints
✕Falls apart the moment a director drills in
✓Built to survive the drill-in

How Pipedash works

STEP 1
Reconstruct the deal

Pipedash starts from Upside’s data foundation: every email, meeting, event, web visit, and CRM record for the opportunity, unified into one labeled timeline, including the buying group members nobody logged.

SalesforceGongemailwebwebinars
2022–’26Webinar program (6+ webinars / 5 yrs)source
2022–’25SDR outreach (multiple reps)
2024Content + web engagement
Feb ’26Goodyear peer recommendationextracted
Jan ’26Demo-request formhand-raise
STEP 2
Reason like an analyst team

A team of independent AI analysts each reads the full history and argues for an allocation. A consensus judge weighs the quality of their reasoning, not just the vote count, and escalates when they disagree. Every allocation ships with the reasoning behind it.

Analyst 1
Analyst 2
Analyst 3

Consensus → Mktg 53 / SDR 15 / 3rd-party 10 / Pool 22

STEP 3
Report in dollars

The output is a full attribution report: 100% of credit allocated across touchpoints and cited. What genuinely can’t be tied to a discrete touchpoint, like brand awareness, still earns credit as a non-touchpoint deal influence rather than being forced into a false number.

Marketing · webinar program (source)$21.2Kcited
Marketing · content + web$10.6Kcited
SDR · outreach ×3$10.6Kcited
Third party · peer rec$7.1Kextracted
Inferred pool (4 drivers)$15.6Kest.
Demo form (hand-raise)$0
= 100% of credit$70.7K

Credit follows the evidence, not the calendar.

Position-based models hand credit to whoever showed up first or last. Pipedash reads the actual deal history and allocates credit the way the touchpoints actually worked together to move the deal forward: the field event, the SDR sequence, and the paid search that each did part of the job.

Dollars, not points. Credit is allocated against deal value, so outputs read as input dollars mapped to output dollars, the framing a channel-level ROI model, a budget review, or a board deck actually needs.

A fair read on every program. Because credit reflects what actually moved the deal, the programs doing real work, including the early, hard-to-track ones, show up accurately instead of being eclipsed by whatever got the last click.

One model, every team. Marketing, SDR, sales, and partner credit come out of a single shared allocation, taking the politics out of “who sourced this deal.”

Prescriptive, not just descriptive. Because allocations aggregate cleanly, Pipedash can support reallocation recommendations: where the next dollar of spend would have earned more.

Prime Analytics

$70.7K

Last-touch credit

Demo-request form — 100%

Pipedash — reasoned from evidence

30%
15%
15%
10%
8%
22%
Webinar 30%Content 15%SDR 15%Peer rec 10%Email 8%Pool 22%
Same deal · Prime Analytics, $70.7K. The same closed deal, two ways. Last-touch hands 100% to the demo form; Pipedash gives that form $0 and credits the 5-year webinar program (30%) as the real source, with the rest spread across what actually moved the deal. Same 100%, sized by evidence — not by which touch landed last.

Every signal counts, even the ones the CRM missed.

Most attribution systems only credit what was logged. Pipedash works from three kinds of signal:

Tracked

Captured in your source systems: CRM, marketing automation (HubSpot, Marketo), and the like. The touchpoints already on the record.

Extracted

Never logged as a touchpoint, but recovered from unstructured data: a third-party recommendation surfaced from a sales conversation, a stakeholder pulled out of a forwarded email thread. Real engagement the systems never recorded.

Inferred

No discrete touchpoint exists or ever could, like brand awareness or readiness to buy, but signals indicate it was in play and part of why the deal happened.

When a deal closes after fourteen months and the CRM shows three touches, the tracked layer alone tells a misleading story. Extracted and inferred signals fill in what actually happened: ungated content that surfaces when it’s mentioned in a reply, the event that drove no trackable conversion but shaped the deal anyway.

Prime Analytics · credit allocation

$70,680
Webinar program · 5 yrs, 6+ webinars · sourceTracked$21,204
evidence6 webinar registrations logged, 2021–2026. On the demo call, Wendy: “I know of Flowlogic from the various webinars I've attended.”
Marketing content + web engagement$10,602
Annie @ Goodyear peer recommendationExtracted$7,068
evidenceNo CRM record. Demo notes: “They talked to Annie from Goodyear about Flowlogic.” A former-colleague recommendation, recovered from the conversation.
SDR outreach + demo follow-up (4 touches)$10,602
Bulk email nurture$5,654
Brand awareness · inferred poolInferred~$2,333
evidenceNo discrete touchpoint. 3.5 years of marketing exposure across the buying group. Probabilistic share.
+ rest of inferred pool (3 drivers)$13,217
Demo request form (hand-raise)$0
TrackedExtractedInferred
Illustrative · from the sample report. How the three signal types show up in the product: each credit line carries its evidence in a hover tooltip — Tracked, Extracted, or Inferred. Written as the evidence reads in the attribution report (clean records, not marketing); from the Prime Analytics sample deal, lightly edited.

And when someone demands a single source, give them the one you can defend.

Sometimes the CFO or the board wants one source per deal. Pipedash gives you the one that survives scrutiny, separating the hand-raise (the moment the buyer acted: the demo request, the inbound email) from the source (what actually drove them to act: the field event, the referral, the content). When you name a single source, you name the cause, not the last click.

# gtm-attribution
M
Marketing10:22 AM
We drove $33M in pipeline in Q2. We beat targets! 🎉
S
SDRs10:24 AM
Our team brought in $41M in meetings.
S
Sales10:25 AM
FYI, the AEs created $20M of their deals 💰
P
Partnerships10:27 AM
Our intros sourced $12M in pipeline too.
C
CEO10:30 AM
…that adds up to $106M, but we only have $54M in real pipeline. What worked?! 🤔
Existing homepage component. The “everyone claims credit, the math doesn’t add up” thread — the moment the CFO stops trusting the dashboards and demands one real number per deal. Numbers scaled to the enterprise ICP (claimed ≈ 2× the real pipeline).

The cause, not the coincidence. The source is whatever truly drove the buyer to act, not the tracked touch that happened to land right before the form.

A single source you can defend. Both the hand-raise and the source trace to specific evidence, so the one number you report up stands on the same footing as the full attribution.

Multi-TouchOpportunity SourceOverview

Source · the cause

Webinar program · 5 years, 6+ webinars

Wendy: "I know of Flowlogic from the various webinars I've attended." (Jan 2026)

Hand-raise · the action

Demo-request form · $0 credit

Jan 21, 2026 — the SDR-logged lead, but not the cause.

Real example · report on file. The same Prime Analytics deal in the report’s Opportunity Source view (toggled from Multi-Touch up top): the full Dealprint collapses to the one defensible source — the 5-year webinar program — while the demo-request form that “converted” stays at $0 (the hand-raise, not the cause). Flip back to Multi-Touch and credit fans back out across every touchpoint.
“Our sales leaders were VERY excited about this, and they can be hard to impress. They had me bring up a couple deals that they knew, and they’re like, ‘oh, it’s not going to pick up this one.’ And there’s one where it had picked up that someone had a connection from, like, an alumni association. They said: ‘that was it! How did it know?!’”
— Marketing Operations leader, enterprise logistics customer

The same numbers, rolled up to the channel, team, campaign, and more.

Every per-deal allocation aggregates the way you’d expect from any multi-touch model: into channel ROI, team contribution, campaign performance, and so on. The difference isn’t the roll-up, it’s that the numbers underneath are reasoned from evidence and cited. So the channel report survives the same drill-in a single deal does, all the way back to the touchpoint.

Channel ROI

Direct Email
$490K
Meeting
$288K
Content
$114K
Web activity
$96K
Webinar
$89K
Bulk email
$68K
Real aggregate · report on file. The same per-deal allocations roll up into the channel / team / campaign views leadership budgets from — real numbers from the sample report. Same math as a single Dealprint, zoomed out, and every bar drills back down to the cited deals underneath.

Attribution that runs on the whole record.

Pipedash reasons over the same reconstructed record every other Upside product does, which is what lets a number trace all the way back to the touchpoint behind it.

Runs on
Data Foundation

The unified touchpoint spine and buying-group detection Pipedash reads from.

Explore results in
Dashboard

Account timelines and report cards where attribution lives alongside the rest of the account story.

Build & query via
MCP & Mini-Apps

Query attribution from Claude or Cursor; deliver results as interactive apps for the team.

Proof the numbers survive scrutiny.

Two teams that pointed full-journey attribution at deals they thought they understood, and changed what they did next.

53% of deals came from referrals standard systems never saw

$10M+ referral revenue uncovered

Graphite’s last-touch reporting showed no value from events, so they stopped running them. Full-journey reconstruction surfaced $10M+ in hidden referral revenue and traced over half of all deals back to referrals, and the events program restarted on the evidence.

We stopped doing events because we didn’t have data saying they were impactful. With Upside, we can see events are actually a massive flywheel for our business.
Ethan Smith · CEO & Founder, Graphite
Read the Graphite story
Attribution that holds up on messy enterprise cycles

Defensible, deal by deal

Long, multi-stakeholder enterprise deals are exactly where tidy models break. Cresta’s reconstruction holds up against the deals their team actually remembers.

Real life is messy. Enterprise cycles are messy. Upside accurately reflects that messy reality, which I think is rare and was impossible before.
Stephen Daniels · VP, GTM & Strategic Operations, Cresta
Read the Cresta story

Frequently asked questions

How is this different from the attribution tools we’ve already tried?

Tools like the ones you’ve probably used apply positional weights to whatever the CRM logged. Pipedash starts earlier, reconstructing what actually happened on the deal, including signals the CRM never captured, then allocating credit by reasoning over that full record. The difference shows up the first time someone asks “why did this channel get credit?” and the answer is a citation instead of a model assumption.

Does credit really add up to 100%?

Yes: per deal, across every touchpoint, including across the independent consistency runs. Like any multi-touch model the allocation sums to 100%. What’s different is that Pipedash decides the split by reasoning over evidence, so the numbers stay comparable and aggregate cleanly.

What happens when the evidence is thin?

Pipedash tells you. Anything that can’t be tied to a citable touchpoint goes into an explicit unattributed pool with labeled, probabilistic subdivisions, not into false-precision line items. The pool’s size is itself a useful read on how much of your pipeline creation is happening outside what gets logged.

Can finance actually audit this?

Every allocation traces to specific touchpoints; every analysis documents how many passes ran, where they diverged, and why the synthesis landed where it did. It’s the same standard of rigor finance applies to other material decisions.

What does Pipedash need from us to run?

Pipedash runs on the Upside data foundation, which connects to your existing systems in a few OAuth clicks: no field mapping, no migration. Once your data is flowing, analyses run on the deals you choose.

See a deal you thought you understood.

The fastest way to evaluate Pipedash is to watch it reconstruct a deal you already know the story of.