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Built for Shopify & agencies

See the relationships every other attribution tool misses.

NodeNarrative models the journey as a knowledge graph, so the relationships between touchpoints survive the maths.

Built for agencies, merchants, data teams and freelancers.

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The same customer journey scored two ways Above, a last-click table gives meta.paid, klaviyo and google a credit of 0.00 and direct a credit of 1.00. Below, the same four touchpoints appear as a graph: meta.paid amplifies klaviyo, both assisted google, google assisted direct, and direct converted the order. direct earns its 1.00 only by being last. EVERY OTHER TOOL touchpoint credit meta.paid 0.00 klaviyo 0.00 google 0.00 direct 1.00 no column can hold a relationship NODENARRATIVE meta.paid klaviyo google direct order AMPLIFIES ASSISTED ASSISTED ASSISTED CONVERTED + 1,024 PATHS · 512 COALITIONS
One journey, scored two ways. The relationship on the right has no column on the left.
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Connects with your stack

ShopifyGoogle AdsMeta AdsNeo4jCloudflareStripe

Relationships become numbers you can defend.

The graph above carries numbers. Scored by Shapley values, the same journey pays out very differently: the touch that opens the journey takes the largest share, and last-click gave it 0.00, because it never closes anything.

The hero's journey, scored by Shapley values meta.paid takes 0.31 of the credit where last-click gave it 0.00. klaviyo takes 0.24 of the credit where last-click gave it 0.00. google takes 0.17 of the credit where last-click gave it 0.00. direct takes 0.28 of the credit where last-click gave it 1.00. AMPLIFIES meta.paid 0.31 0.00 ASSISTED klaviyo 0.24 0.00 ASSISTED google 0.17 0.00 direct 0.28 1.00
Each row: Shapley credit above, last-click credit below. An illustrative split of the journey in the hero, not a customer's figures. Your channel mix will differ, month to month and store to store.

All seven models run on the same graph. First-Click · Last-Click · Linear · U-Shaped · Time-Decay · Shapley Values · Markov Chain

Shapley Values

Imagine removing one channel from every possible journey combination. The average change in conversion rate across all those combinations is that channel’s Shapley value, its true marginal contribution to the outcome.

Markov Chain

The model maps all observed journeys as transition probabilities between channels. It then removes each channel one at a time and measures the drop in overall conversion rate, the "removal effect". A larger drop means a more critical channel.

Deterministic, not generative

Every number above comes from deterministic ML and Bayesian methods computed over the knowledge graph, never from a generative or token-derived model. A number produced by a deterministic model can't be hallucinated, it can only be recomputed and checked.

Built On

ShopifyGoogle Cloud PlatformNeo4jCloudflare
See how each model works
Four channels compared across five reporting dimensions A five-axis chart with one outline per channel: attribution, reach, velocity, position, awareness_rate. meta.paid and direct both reach near the edge on attribution, and are opposites everywhere else: meta.paid is wide on reach and awareness rate and sits at the centre on position, while direct is the mirror of it, at the edge on position and near-zero on reach and awareness rate. No values are plotted; the comparison is between the shapes. attribution reach velocity position awareness_rate
  • meta.paid
  • klaviyo
  • google
  • direct

The five dimensions are what the platform reports for every channel. Attribution and position come from the journey above; the other three are illustrative. The comparison is between the shapes rather than any value on an axis.

Two channels can earn the same credit and be nothing alike.

The first touch of that journey and the last one score within a few points of each other on attribution. On the other four dimensions they are opposites: one is wide, early and slow, and starts most of the journeys it appears in. The other is narrow, late, and starts almost none of them.

Judge them on the attribution column alone and you cannot tell them apart, so the channel that opens journeys looks like the one that closes them. NodeNarrative reports all five per channel, which is what makes the difference visible before you cut the wrong one.

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Privacy by Architecture, Not by Policy

Every competitor treats privacy as a compliance checkbox buried in their footer. We built it into the knowledge graph. A full consent banner ships with the pixel, two of these capabilities are unique to NodeNarrative, and our cookieless mode works without fingerprinting.

Unique to NodeNarrative

Consent-per-identifier

Every identifier in your customer graph carries its own consent status. Email consent is tracked separately from cookie consent and device identifiers. Attribution models only use consented data paths.

Unique to NodeNarrative

GDPR cascading erasure

When a customer exercises their right to be forgotten, deletion cascades through every connected touchpoint in the graph automatically. No manual cleanup, no orphaned records.

Cookieless by default

Full attribution accuracy without any cookies. No fingerprinting, no workarounds. First-party server-side collection with graph-based identity resolution.

  • GDPR compliant
  • CCPA compliant
  • Automatic PII detection
  • Consent banner included
  • No fingerprinting
See our full privacy architecture
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