Channel Attribution

The Channel Attribution page is the main attribution dashboard. Open it from Insights → Attribution.

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Selecting a date window

Use the date range picker at the top of the page to set the analysis window. Only customer journeys that started within this window are included. Quick presets are available for common windows: After selecting a range, click Run attribution. If results for this exact window (and any active label filters) are already cached, they load immediately. Otherwise, the model runs and results appear within seconds to a few minutes depending on data volume.

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Label filters

If label splits are configured in your attribution settings, a multi-select filter appears next to the date picker. Select one or more segment values (e.g. NL, DE) to view attribution for that combination. When multiple labels are selected, each label is modelled independently and results are shown separately or aggregated — depending on the visualisation.

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Dashboard sections

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Channel removal effects

The removal effect chart shows the Markov model’s primary output: how much conversion rate drops when each channel is removed from all journeys.

• A tall bar means the channel is structurally important — many journeys rely on it
• A short bar means journeys frequently convert even without this channel
• Channels not appearing in any journey during the window show zero removal effect

This view helps identify which channels are essential to the path (often mid-funnel channels) vs. which are replaceable.

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Attributed revenue

The attributed revenue chart distributes total journey revenue across channels according to the credit share from each model.

• Markov credit is based on removal effects (re-normalised to sum to 100%)
• Shapley credit is based on cooperative game-theory coalition values (only shown when Shapley is enabled)
• Last-touch is shown as a reference baseline

Use this chart to compare which model paints a more realistic picture of your channel mix — particularly to identify channels that last-touch over- or under-credits.

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Journey flow (Sankey)

The Sankey chart visualises the most common customer journey paths as a flow from first touchpoint to conversion or dropout.

• Node width is proportional to the number of journeys passing through that channel
• Link width is proportional to flow volume between two channels
• Journeys that do not convert flow to a “No conversion” terminal node

Use this to understand typical conversion paths, spot where journeys drop off, and identify which channel combinations most frequently lead to conversion.

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Coverage and quality indicators

Two metric indicators appear alongside the charts:

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Model comparison

Both Markov and Shapley results are shown side-by-side when Shapley is enabled. Key differences: When the two models agree on channel ranking, confidence in the results is higher. When they disagree significantly, investigate journey structure: highly sequential journeys favour Markov; channels with strong synergistic effects show up more clearly in Shapley.

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Next steps