One-Line Definition
Northbeam is a multi-touch attribution (MTA) and marketing analytics platform that helps DTC brands measure the true incremental contribution of every ad channel, so they can shift budget toward what actually drives profit — not just what gets credit for the last click.
Real-Life Analogy: The Restaurant Kitchen Problem
Imagine you run a restaurant and want to know which station in the kitchen deserves a raise. The grill cook claims every steak, the salad station claims every side, and the pastry chef insists no one finishes a meal without dessert. If you only rewarded whoever handed the plate to the customer (the "last touch"), the pastry chef would win every month — even though the steak is what actually brought people in.
Paid marketing works the same way. A customer might see a TikTok ad on Monday, click a Meta ad on Wednesday, get retargeted on Instagram on Friday, and finally convert after typing your brand name into Google on Sunday. Google gets the credit, but TikTok planted the seed. Northbeam is the kitchen manager that traces the entire meal back to each station, weighs how much each one actually contributed, and tells you where the next dollar should go.
Core Formula: How Northbeam Thinks About Attribution
Northbeam's model is a data-driven, multi-touch attribution system that combines three inputs:
Attributed Revenue = f( Touchpoint Data , Spend Data , Conversion Data )
More practically, Northbeam calculates a contribution score per channel using:
1. Deterministic data — clicks, UTM parameters, and platform-reported conversions tied to a known user.
2. Probabilistic modeling — machine learning that fills in the gaps for view-through, cross-device, and privacy-limited touchpoints.
3. Incrementality signals — geo-lift tests, holdout experiments, and blended MER (Marketing Efficiency Ratio) benchmarks that validate whether a channel is truly *causing* sales or just *correlating* with them.
The output is usually expressed as:
- Blended CAC = Total Ad Spend ÷ Total New Customers
- Channel-level ROAS (attributed) = Attributed Revenue ÷ Channel Spend
- Incremental ROAS (iROAS) = Revenue *caused* by the channel ÷ Channel Spend
For example, a brand spending $120,000/month across Meta, Google, TikTok, and email might see Meta report a 4.2 ROAS in-platform, but Northbeam could show an incremental ROAS of only 1.8 — while TikTok, which self-reported a 1.5, is actually driving 2.9. That gap is the entire point of the platform.
Comparison with Related Terms
| Term | What It Measures | Methodology | Best For | Northbeam's Difference |
|---|---|---|---|---|
| **Google Analytics 4** | On-site behavior & last-click conversions | Rule-based, last non-direct click | Web analytics, funnel analysis | Northbeam models cross-channel contribution, not just last click |
| **Triple Whale** | DTC analytics & attribution | Pixel + post-purchase surveys + MMM | Shopify-native brands wanting a dashboard | Northbeam leans harder into MTA + incrementality testing |
| **Marketing Mix Modeling (MMM)** | Channel contribution at aggregate level | Statistical regression on spend/revenue | Budget planning without user-level data | Northbeam is *user-level* MTA; MMM is top-down and privacy-proof |
| **Platform-reported ROAS** | Conversions the ad platform claims | Platform pixel, self-attributed | Quick sanity checks | Northbeam de-duplicates and corrects for platform over-reporting |
| **Incrementality testing** | True causal lift | Geo holdouts, A/B tests | Validating MTA outputs | Northbeam *includes* incrementality as a layer, not a separate tool |
The key distinction: GA4 tells you what happened on your site. Northbeam tells you what caused it.
Use Cases
1. Budget reallocation across channels.
A DTC skincare brand spending $500K/month across 6 channels uses Northbeam to find that TikTok's incremental ROAS is 3.1 while Display is 0.4. They cut Display by 40% and reinvest into TikTok, lifting blended MER from 2.4 to 3.0 within 60 days.
2. Proving upper-funnel value.
Brands constantly face the question: "Is our $80K/month YouTube spend doing anything?" Northbeam's view-through and incrementality modeling can show that YouTube drives a 22% lift in branded search — value invisible to last-click tools.
3. Creative and audience-level decisions.
Beyond channels, Northbeam breaks down performance by creative, campaign, and audience segment, so teams can kill underperforming ads faster and scale winners with confidence.
4. Post-iOS 14 measurement.
With signal loss from Apple's ATT framework, Northbeam's probabilistic modeling and MMM-style triangulation give brands a measurement layer that doesn't collapse when pixel data gets noisy.
5. Board and investor reporting.
Founders use Northbeam's blended CAC and contribution margin views to show investors *why* growth is efficient — not just that revenue went up.
Common Misconceptions
❌ "Northbeam gives you the one true number."
No attribution model is perfect. Northbeam provides a *better* estimate than last-click or platform-reported data, but it still relies on assumptions. That's why it pairs MTA with incrementality testing — the two together are more trustworthy than either alone.
❌ "It's just a fancier dashboard."
Dashboards show data. Northbeam *models* it — applying machine learning to attribute conversions across touchpoints and validating them against real experiments. The value is in the modeling layer, not the UI.
❌ "It replaces GA4."
They answer different questions. GA4 is your on-site behavioral analytics; Northbeam is your cross-channel acquisition measurement. Most mature DTC brands run both.
❌ "It's only for big brands."
Northbeam is priced for scaling DTC brands — typically those spending $50K+/month on paid media, where attribution errors cost real money. Below that threshold, the ROI of the tool itself gets harder to justify.
❌ "MMM makes MTA obsolete."
They're complementary. MMM is privacy-proof and works at aggregate scale; MTA is granular and user-level. Northbeam's approach blends both, which is increasingly the industry standard.
❌ "It fixes your marketing for you."
Northbeam tells you *where* the problem is. You still need a team to act on it. Attribution is a flashlight, not an autopilot.
Related Terms
- Multi-Touch Attribution (MTA) — Assigning conversion credit across multiple touchpoints in a customer journey.
- Incrementality — The true causal lift a channel produces, measured via holdout or geo tests.
- Marketing Mix Modeling (MMM) — Top-down statistical modeling of channel contribution using aggregate spend and revenue data.
- Blended CAC / MER — Total spend divided by total new customers or revenue; a sanity check on attribution outputs.
- Last-Click Attribution — The default model in most analytics tools; heavily biased toward bottom-funnel channels like branded search.
- View-Through Conversion — A conversion attributed to someone who saw (but didn't click) an ad; critical for upper-funnel channels like YouTube and TikTok.
- Post-Purchase Survey (PPS) — Self-reported attribution collected at checkout; a low-tech complement to MTA that many DTC brands run alongside Northbeam.
- Data Clean Room — Privacy-safe environments where platforms and brands match data without exposing user-level PII; increasingly relevant for cross-channel measurement.
Bottom line: Northbeam is the measurement layer that lets DTC brands stop guessing which channels deserve credit — and start allocating budget based on incremental contribution to revenue, not vanity ROAS. In a post-iOS 14 world where platform data is noisy and last-click is misleading, that clarity is worth more than the subscription.