Attribution by Business Type

Attribution defaults differ sharply by business model. The same “which channel drives revenue?” question wants a different source of truth, model, and paradigm depending on how long your cycle is, how many people are involved, and where your budget goes. Two playbooks: B2B SaaS and Ecommerce/DTC. Match the user’s product to one (or blend, for PLG-with-sales).

B2B SaaS (long cycle, sales-assisted)

Shape of the problem: journeys run weeks to months, span multiple people (champion, economic buyer, users), and include touches that never appear in web analytics — a conference conversation, a sales call, a Slack-community mention, a peer recommendation. Deal values are high and volume is low, so every deal matters and averages are noisy.

Why single-touch models mislead badly here: with 15 touches over 3 months across 4 people, “last-touch = direct” and “first-touch = one LinkedIn ad” are both almost useless. The middle — and the offline — is where the deal was actually won.

The B2B playbook

Source of truth = the CRM, not any analytics tool. Revenue is real in the CRM (closed-won, ARR); everything else explains where those deals came from. Pipeline and revenue attribution live in revops — attribution feeds it the “source” dimension.
Models: first-touch + position-based, shown together. First-touch values demand creation (which channel started the accounts that became pipeline). Position-based credits the created-and-closed bookends, the two decisions you actually make. Last-touch alone will defund your top of funnel — don’t lead with it.
Self-reported attribution is your strongest signal, not a nice-to-have. Ask “How did you first hear about us?” on the demo request / signup form and again qualitatively on sales calls. For high word-of-mouth and dark-social-heavy B2B, this catches what tracking structurally can’t (podcasts, communities, “my old coworker used you”). Weight it heavily.
Attribute to pipeline stages, not just the conversion. The useful B2B question isn’t “what drove the form fill” — it’s “what drove qualified pipeline and closed revenue.” Break down MQL→SQL→closed-won by first-touch channel; a channel that fills forms but never closes is a trap. (Stage mechanics → revops.)
MTA is weakest here; incrementality is awkward but valuable. Low volume makes data-driven attribution unreliable and geo-tests hard. Use on/off tests for big-ticket programs (turn off a channel for a quarter, watch pipeline) and lean on self-reported + first-touch for the rest.
Account-level, not just lead-level. Attribution should roll touches up to the account (all the people at the buying company), or you’ll credit whichever individual happened to fill the form. In practice one org is several people signing up with mixed work and personal emails, so person-level attribution scatters the story across records — match contacts to the account (email domain, enrichment, or your CRM’s contact→account link) and attribute at the account level. That’s where the signal has to land to be useful to a rep working the whole buying committee. Exclude free-mail domains (gmail/yahoo/outlook) from domain matching — they can’t identify a company; fall back to enrichment or manual matching for personal-email signups. (Production emphasis from Tessa Kriesel; the CRM-sync mechanics live in first-party-tracking.md Step 5.)

Tooling: CRM (HubSpot/Salesforce) as truth; a product-analytics tool identifying by user/account UUID for first-party first-touch (see first-party-tracking.md); self-reported fields written to the CRM; RB2B-style de-anonymization to catch un-formed account visits.

The B2B trap to name for the user: branded search and direct will look like your best “channels” because that’s where researched buyers convert. They’re not channels — they’re where demand created elsewhere cashes out. Segment branded vs. non-branded search and treat a big direct share as evidence your top-of-funnel is working, not as a channel to invest in.

Ecommerce / DTC (short cycle, self-serve)

Shape of the problem: journeys are fast (minutes to a few days), high-volume, and almost entirely digital and self-serve. Budget concentrates in paid social + paid search + email/SMS. The conversion is a purchase you fully control (your checkout or a hosted one). The dominant lie is platform over-attribution — Meta and Google each claiming the same sales.

The DTC playbook

Source of truth = your store/backend (Shopify, your payments system) — the count of actual orders. Platform-reported conversions get de-duped against that total; they never define it and are never summed.
Distrust platform ROAS by default. Post-iOS ATT, platforms model and estimate conversions, count view-through, and use generous windows — reported ROAS runs well above incremental ROAS. Use it for in-platform optimization (it’s fine for the algorithm) but not for cross-channel budget truth.
Last-touch is defensible for quick-turn, impulse SKUs — the closing click really is most of the story for a $30 impulse buy. It gets dangerous as consideration lengthens (higher AOV, considered purchases), where it over-credits retargeting and branded search.
MMM once spend is material. When you’re spending real money across paid social, search, and offline (podcasts, TV, influencers, OOH), MMM is how you allocate — it’s the only paradigm that sees the untrackable channels and the saturation curves. Below ~six figures/month of blended spend, MMM is overkill; good UTMs + a survey do more.
Incrementality on your biggest channels — especially the “always credited” ones. Geo-holdouts and on/off tests earn their keep on retargeting, branded search, and Meta prospecting, which platform reporting flatters most. Incremental CPA (spend ÷ incremental orders) is the number that should move budget. (See measurement-paradigms.md.)
Post-purchase survey to catch the dark-social + brand demand. A one-question “How did you hear about us?” on the order-confirmation page consistently reveals that podcasts, TikTok organic, and word-of-mouth drive far more than pixels credit — because those touches convert later as “direct” or branded search. Kickstarter-era DTC brands run this as standard for exactly this reason.

Tooling: store/backend as truth; platform pixels + CAPI for optimization (setup → ads conversion-tracking.md); Supermetrics/Coupler to pull platform numbers into one place for de-duping; a post-purchase survey app; MMM tooling (Robyn/Meridian or a vendor) once spend justifies it.

The DTC trap to name for the user: summing platform-reported conversions. If Meta claims 100 and Google claims 80 but you had 120 orders, you do not have 180 conversions — you have 120 with overlapping claims. Anchor on the 120 and allocate the overlap with incrementality, not by trusting whichever platform shouts loudest.

Blended / PLG-with-sales

Many modern SaaS businesses are both: self-serve signups and a sales-assisted motion for larger accounts. Blend the playbooks:

Use the DTC approach for the self-serve funnel (fast, high-volume, first-party first-touch → conversion, defensible last-non-direct + survey).
Use the B2B approach for the sales-assisted funnel (CRM as truth, position-based, pipeline-stage attribution, self-reported at demo).
Alias identities across the two so a self-serve signup who later becomes a sales-assisted expansion keeps one journey (email↔UUID alias at signup — see first-party-tracking.md).
Report them separately. Blending a $50 self-serve signup and a $50k enterprise deal into one “attribution” number hides both stories.