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Built for Shopify & agencies

Go beyond last-click. See how your channels work together.

NodeNarrative maps every customer journey as a knowledge graph and computes attribution over it, so credit reflects how touchpoints influenced each other, not just which one came last.

Built for agencies, merchants and in-house analysts.

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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. LAST-CLICK 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 in the graph has no column in the table.
GDPR cascading erasure Hashed identifiers Consent-gated pixel

Connects with your stack

Shopify Google Ads Meta Ads

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. So when a client asks why a channel got its share, an agency can walk them through the journeys behind it.

Built On

Shopify Google Cloud Platform Neo4j Cloudflare
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

Privacy here means control, not blind spots. Shoppers can have their data deleted across the whole graph, the identifiers we join on are hashed, and what you get back is decisions about channels, content and creative.

Consent-gated collection

On Shopify the pixel runs inside Shopify's own sandbox and collects only when the shopper has given analytics consent through Shopify's banner. It sets no cookies of its own.

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.

Pseudonymised, built for decisions

Emails are hashed before they reach our database, checkout details are dropped in the browser and IP addresses are truncated. What you get back is which channels, content and creative work, not a profile of each shopper.

  • Emails hashed before storage
  • IP addresses truncated
  • Consent-gated pixel
  • Cascading erasure
See our full privacy architecture
Do your ad platforms claim more sales than your store recorded? Two numbers you already have, divided. 60 seconds, no signup. Size your attribution gap

Founding Partner Offer

40% off for 12 months, for 10 founding partners (stores or agencies).

Any plan, locked at that rate. Founding partners shape the roadmap by talking directly to the person building it, and keep the discount when we launch.

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See which channels are doing the work your last-click report cannot see.

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