Pipedash — Attribution that survives scrutiny | Upside
/meta; at runtime SEO.tsx sets<br>document.title imperatively. This is only the pre-JS fallback. --><br>Upside – Revenue Attribution & Intelligence Platform
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PipedashAttribution that survives scrutiny.<br>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.<br>Request a demoExplore an example report →
Dealprint<br>Prime Analytics · $70.7K<br>53%22%15%10%Prime$70.7KMarketing53% · $37.5K<br>Inferred pool22% · $15.5K<br>SDR15% · $10.6K<br>3rd-party10% · $7.1K
There has never been a golden age of B2B attribution… until now.<br>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.<br>Legacy multi-touch models<br>Pipedash
✕Only credit what the CRM happened to log<br>✓Reconstructs the full record, including what was never logged, and cites it
✕Credit follows position: first touch, last touch, fixed weights<br>✓Credit follows evidence: reasoning over what actually moved the deal
✕Allocations are a model assumption<br>✓Allocations trace to specific, cited touchpoints
✕Falls apart the moment a director drills in<br>✓Built to survive the drill-in
How Pipedash works<br>STEP 1Reconstruct the deal<br>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.<br>SalesforceGongemailwebwebinars<br>2022–’26Webinar program (6+ webinars / 5 yrs)source<br>2022–’25SDR outreach (multiple reps)<br>2024Content + web engagement<br>Feb ’26Goodyear peer recommendationextracted<br>Jan ’26Demo-request formhand-raise
STEP 2Reason like an analyst team<br>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 3Report in dollars<br>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.<br>Marketing · webinar program (source)$21.2Kcited<br>Marketing · content + web$10.6Kcited<br>SDR · outreach ×3$10.6Kcited<br>Third party · peer rec$7.1Kextracted<br>Inferred pool (4 drivers)$15.6Kest.<br>Demo form (hand-raise)$0<br>= 100% of credit$70.7K
Credit follows the evidence, not the calendar.<br>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.<br>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<br>$70.7K<br>Last-touch credit<br>Demo-request form — 100%
Pipedash — reasoned from evidence<br>30%<br>15%<br>15%<br>10%<br>8%<br>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.<br>Most attribution systems only credit what was logged. Pipedash works from three kinds of signal:<br>Tracked<br>Captured in your source systems: CRM, marketing automation (HubSpot, Marketo), and the like. The touchpoints already on the record.
Extracted<br>Never logged as a touchpoint, but recovered from unstructured data: a third-party recommendation surfaced from a sales conversation, a...