ATTRIBUTION INTELLIGENCE
AI-powered weekly attribution. Pulsar Analytics connects to the GA4 BigQuery export, runs Markov chains, Shapley values, and four simple path models side by side, scores them, and sends a ranked PDF. Every report is approved by a marketing professional with extensive experience in marketing analytics.
BUILT ON
GA4
BigQuery
Python for marketing ML and analytics
Human review
SERVICES
Each engine is an AI-powered product. The language model does not invent the numbers — it writes from a context stack built from ten years of marketing-analytics practice.
The system is built that way: journeys, models, scoring, draft, professional review, your PDF.
01
The starting point
Weekly multi-touch attribution that shows where last-click and path models agree — and where they do not.
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02
When you have the history
A mix model on GA4 plus spend. It answers a different question: what happens if you move the budget.
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03
From credit to a plan
Takes the credit your models just produced and solves a constrained media plan.
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WHY IT EXISTS
Performance teams still open the budget meeting on last-click because everyone already has that number. GA4 retired the simple path rules in 2023. Data-driven attribution is closed. The Data API cannot rebuild journeys.
Markov chains and Shapley values can — if you have the BigQuery export. Pulsar Analytics turns that export into a weekly decision sheet a CMO can act on.
METHOD
First the plain case for each model. Then how it is actually computed.
Simple rule
All credit to the last touch. Easy to explain. Still the number most meetings start with.
Use it as the shared baseline — and when the path is genuinely short: branded search, remarketing, checkout.
Gives every conversion to the last session before it happened. Last-click stays on the page so last-touch ROAS is still in the meeting. It tends to credit closers (Direct, branded Paid Search) and miss assists. A reporting habit — not proof that a channel caused the sale.
Simple rule
All credit to the first touch. This is how a converting journey actually started.
Use it when discovery matters: new markets, prospecting, upper-funnel brand, first-time buyers. First-click is often the right bound when you are deciding whether to keep a channel that almost never closes.
Gives every conversion to the first session in the lookback. It shows openers last-click hides — often Organic Search, Paid Social, and Display. The number moves if the lookback or Direct mapping changes. Treated as the ceiling on introducer value, not as the only plan.
Simple rule
Equal credit across every step in the converting path.
Use it when the path is a team sport — mid-funnel nurture, content plus paid, long B2B cycles — and you want a conservative midpoint between first and last.
Splits credit evenly across every step. Softens last-click’s closer-bias without picking a fade speed. Long messy paths (bots, consent duplicates) can stretch it. Used as a midpoint check against the path models, not as the recommendation.
Simple rule
Later touches get more credit. How fast it fades is a setting agreed with you.
Use it when recency is a real signal: short purchase cycles, promo windows, retargeting that is supposed to close.
Gives more credit to touches closer to the conversion. Older touches fade on a half-life you set (default: a week). That setting is written in the appendix so the number can be rebuilt. It still does not say what would happen if a channel were removed.
Path model
Asks what would happen if a channel disappeared from the path.
Use it when you need to know which channels are load-bearing — the ones converting journeys actually pass through, not just the ones that happen to be last.
Maps how people move between channels, then asks: if this channel were gone, how many conversions would disappear? That is the removal effect. It needs full journeys from BigQuery — the Data API cannot do it. The report shows the removal next to the path steps that drive it.
Path model
Fair credit from every combination of channels, with a range so you can see the uncertainty.
Use it when channels work as a coalition — paid plus email plus organic — and you need a number you can defend as equitable rather than sequential.
Looks at every mix of channels on converting paths and splits credit so no channel is favoured just for being first or last. Few channels: exact split. Many: an estimate with a range, printed on the page. If the range overlaps last-click, no move is recommended.
THE WEEKLY REPORT
01
Monday-morning moves, not a data dump.
The model recommended for planning this week, the three budget implications that actually change a meeting, and the items held for professional review. One page that forwards.
02
Last-click and the path models in one grid.
Every planning channel across all six models, with the gap versus last-click. Shares add back up to your conversion count, so finance can trust the arithmetic.
03
See which channels the path actually depends on.
If a channel vanished from converting journeys, how many conversions would you lose? That is the removal effect.
04
Fair credit, with a range you can stand behind.
Each channel’s share plus a 90% range. Exact when the set is small; an estimate when it is not.
05
Know if this week is a pattern or a blip.
How ranks have moved versus the last eight weeks. A channel whose share jumps is flagged, so you do not reallocate on noise.
06
When the models disagree, a next step — not a debate.
If the gap is expensive enough, a geo or pause experiment. Labeled as a recommendation. The product does not bid, pause, or publish tags.
PROCESS
01
The GA4 property and the BigQuery export. Work happens in the client Google Cloud project. Data Viewer and Job User on the export dataset only.
02
Journeys, six models in parallel, scored against simple checks. The first ranked report lands in your inbox — not behind another login.
03
An agent drafts. A marketing professional with extensive experience in marketing analytics approves. The PDF lands in the inbox — not forty tabs. Slack or email. A short monthly readout on the retainer.
FOUNDER
Founder · Marketing data scientist · 10+ years in marketing analytics
Based in Tashkent (Central Asia). Fully remote.
A decade in measurement, machine learning, and BI for performance teams. Every Pulsar Analytics report is approved by a marketing professional with that background before it is sent.
about.me/stepanromanovFAQ
DIAGNOSTIC
Built for Heads of Growth and performance directors. Bring spend band, whether BigQuery export is on, and the conversion that actually matters.
