What you can answer without multi-touch attribution: Losing path-level data does not lose you the budget question. Five things you can still answer, two you genuinely cannot, and a way to tell them apart.
Read the full article below for detailed insights and actionable strategies.
Customer journey
How attribution misses the real journey
One conversion. Five touchpoints. Last-click credits the final touch with 100%.
Last-click attribution
Every other channel gets zero credit, even though they created the demand.
Causal inference
Almost every question that changes a budget is an aggregate question, and aggregate questions survive the loss of path-level tracking. The questions that do not survive are mostly ones that produce interesting slides rather than decisions.
Here is the split.
Still answerable
| Question | Why aggregate data suffices |
|---|---|
| Should this channel get more or less budget? | It compares outcomes across spend levels |
| Which channel should I test properly next? | It only needs a ranking with uncertainty |
| Is my reported ROAS inflated, and by how much? | It compares platform claims to real orders |
| Did last quarter's reallocation work? | It compares two periods |
| What share of my orders can I explain at all? | Coverage is an aggregate by definition |
Not answerable
Two things genuinely go: the order in which a specific person encountered your channels, and any personalisation that depends on individual history. Both need the individual record.
The honest question is what the first one would have changed. In most DTC brands, path order informs a narrative about the funnel and does not move budget, because the budget decision is per channel and the path analysis is per person. Worth checking against your own last four quarters: how many budget changes were made because of a path insight?
The distinguishing test
Ask whether your question has a person in it. "Which channel caused revenue" has no person in it and is answerable from aggregates. "What did this customer see first" has a person in it and is not.
Most questions phrased as the second are actually the first wearing different clothes. "Does display assist conversions" sounds path-shaped, but the decision behind it is whether to fund display, which is a budget question and is better answered by turning display off in some regions and watching what happens. The geo testing guide covers that design.
What replaces the path chart
Three numbers per channel rather than a journey diagram: the causal estimate, its confidence interval, and the coverage share of orders it explains. Plus a named list of channels below the level of spend where anything can be resolved.
That is less visually satisfying than a Sankey diagram and considerably more actionable, which is the trade most teams are happy with once they have made it. The report structure is set out in a defensible attribution report you can export.
Where to start
Run one read on a window you already have opinions about, and see whether the answers you needed are in it. That is a €99 one-time upload of a Google Analytics export at Causality Engine, refundable if it does not move a budget decision, with the interactive demo available first with no signup.
If a question you actually needed is missing, that is useful information about which tool you need, and it costs a lot less to find out this way than through a migration.
Related answers
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Key Terms in This Article
Analytics
Analytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
Attribution
Attribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
Attribution Report
Attribution Report shows which touchpoints or channels receive credit for a conversion. It identifies which campaigns drive desired actions.
Causality
Causality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
Confidence Interval
Confidence Interval is a statistical range of values that likely contains the true value of a metric. In marketing analytics, it quantifies uncertainty around estimates, indicating the precision of an outcome or causal effect.
Conversion
Conversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
Google Analytics
Google Analytics is a web analytics service that tracks and reports website traffic.
Multi-Touch Attribution
Multi-Touch Attribution assigns credit to multiple marketing touchpoints across the customer journey. It provides a comprehensive view of channel impact on conversions.
Related Articles
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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