Last click often sends credit to the channel closest to checkout. Before moving budget toward that channel, ask whether the report measures the final recorded visit or the activity that changed the buying decision.
Probabilistic attribution offers a broader estimate, but it should be treated as a decision aid rather than a final verdict.
Separate the last visit from the reason to buy
Last-click models naturally favor channels that sit near the end of a journey, such as branded search, direct visits, and retargeting. Earlier activity may introduce the product, explain its value, or make the later click more likely without receiving credit.
That does not mean every earlier impression deserves credit. Some exposures are incidental, some are duplicated, and some customers would have purchased anyway. The useful question is not what touched the customer. It is which patterns are consistently associated with a meaningful change in outcomes.
A probability is not proof of causality
A probabilistic model compares observed journeys and assigns estimated contribution under a set of assumptions. Its output depends on the quality of the inputs, the available coverage, the chosen time window, and the way missing observations are handled.
Read the uncertainty alongside the estimate. If two channels differ only slightly, the model may not support a budget change. A precise percentage on screen does not make the difference reliable.
Use attribution to form budget hypotheses
Begin by reconciling the model with commercial totals. Attributed conversions and revenue should tie back to the orders the business recognizes, with clear treatment of refunds, duplicates, and unattributed demand.
Next, look for decisions where the estimated contribution differs materially from the current allocation. Turn that difference into a hypothesis. If the model suggests an upper-funnel channel contributes more than last click reports, move budget gradually or run a controlled test rather than accepting the estimate on faith.
Define the outcome that would confirm or challenge the hypothesis. Incremental orders, new-customer revenue, or contribution margin may be more useful than a platform-specific return metric.
Calibrate estimates with outcomes
Compare past estimates with later results. Use holdouts, regional tests, timing changes, or other controlled experiments where they are practical. When experimental evidence conflicts with the model, investigate the assumptions instead of explaining away the result.
Choose one budget hypothesis, name the commercial outcome that would support it, and record what would overturn it. Use the model to select the test; use the resulting evidence to decide what comes next.
