Why clean data comes first
B2B attribution isn’t just about models or math, it's about cause and effect. One of the first insights we had as we started on the journey to tackle attribution: you can’t understand what worked if you don’t understand what happened.
That’s the core problem with attribution today. Buyer journeys in B2B stretch over months or years, with hundreds of touchpoints and dozens of stakeholders. But the systems we rely on to track them. CRMs, marketing automation software, email, calendars only capture fragments of that journey. They’re messy, duplicated, incomplete, and siloed.
If you feed that “Swiss cheese data” into any attribution model, you get Swiss cheese results, or as some say, garbage in, garbage out. That’s why most teams end up abandoning complex attribution models and falling back to naive last-touch reporting. It’s not that they believe last-touch to be accurate; it’s just easier than pretending broken data can support sophistication.
Upside approaches it differently. We assume your systems are signals, not unquestionable truth. Then we reconstruct the actual story of the deal using a knowledge graph designed to handle complex relationships. Out of the box, here are four ways we clean and enhance your GTM data so you can finally trust it.
1. Persona resolution
Buyers appear across systems under different guises, from a Gmail address on an event signup, a Salesforce lead without a job title that is the same person appearing on a corporate email on an opportunity.
Upside automatically merges these into one unified identity. That means both one complete record of a person’s journey across systems AND events and campaigns get the credit they deserve.
2. Buying group detection
Every deal has a buying group, but CRMs rarely capture everyone involved. AEs sometimes only add the minimum number of contacts their leaders require, leaving out key influencers.
Upside automatically reconstructs the real buying group by scanning emails, calendar invites, and meeting transcripts. We don’t rely on what was manually logged. Instead, we spider out the connections so you see the actual group of stakeholders who shaped the decision.
3. Touchpoint direction detection
Not every touchpoint means the same thing, for example sending an email isn’t the same as clicking on it. Upside automatically distinguishes between:
Engagements → what the customer actually did (clicked, replied, attended).
Attempts (“bids”) → what your team tried to do (sent, invited, called).
This lets you separate effort from impact and see when true momentum measured as engagements over time shifted on a deal and what drove that shift.
4. Email chain reconstruction
Email is where deals live, but it can also be where attribution breaks down. Execs don’t track their emails or log their intros, some reps track, others don’t and sometimes forwarded threads disappear into the ether and the first thing you find tracked on an opp is a meeting. Upside reconstructs email chains, including forwarded ones and trunks them into singular touchpoints appearing at the appropriate time in a journey. If a CEO forwards an intro, we pull it into the record. If an AE joins a forwarded thread, we stitch it back into the journey. That way, you can see the full conversation and how it interacted with other deal activities.
Why this matters
If you don’t have a good understanding of what happened, there’s no way you can have a good understanding of what worked. That’s why Upside starts with cleaning and structuring data. Persona resolution, buying group detection, touchpoint direction, and email chain deconstruction aren’t just conveniences, they are the foundation for attribution you can trust. But clean data isn’t the end goal. It’s the beginning of attribution that actually helps you make better decisions. With Upside we think about is as a pyramid:
- What happened → Reconstruct the deal story.
- What worked → Analyze which signals truly mattered.
- What to do next → Forecast, allocate, and decide with confidence.