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:
Preset
Typical use
Last 30 days
Recent performance, fast to compute
Last 90 days
Standard operational window
Last 180 days
Longer-term patterns
Last 365 days
Full-year analysis
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.
Use Run all presets in the Runs settings page to pre-compute the standard windows in the background, so the dashboard always loads instantly.
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.
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.
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.
Two metric indicators appear alongside the charts:
Indicator
What it means
Coverage
Percentage of journeys where at least one touchpoint matched a known channel. Low coverage (below ~80%) suggests many journeys contain channel values not in your configured channel list — check for naming inconsistencies.
Single-step journeys
Percentage of journeys with only one touchpoint. Very high values (above ~60%) can affect Markov model quality, as there are fewer multi-channel paths to learn transition probabilities from.
Both Markov and Shapley results are shown side-by-side when Shapley is enabled. Key differences:
Markov
Shapley
Approach
Removal effect from transition matrix
Marginal contribution across all channel orderings
Sensitivity to order
High — channel position matters
Low — all orderings are considered equally
Handles synergies
Partially
Yes
Computation speed
Fast
Slower (exponential with channel count)
Best for
Operational decisions, regular runs
Deep analysis, model validation
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.
