Most marketing teams start with rule‑based models.
Whether you credit the first interaction, the last, or split value across touchpoints in a U‑ or W‑shape, the principle is identical: a fixed formula decides who gets the credit. These heuristics are quick to configure and easy to explain, which explains their enduring popularity.
The simplicity hides a cost. Fixed rules:
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ignore the way channels influence one another
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treat every customer journey as identical
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stay frozen even when buying behaviour shifts
In short, they introduce bias the moment you adopt them.
What Algorithmic Attribution Does Differently
Algorithmic (data‑driven) attribution replaces rigid rules with statistical evidence drawn from every recorded journey, converting and non‑converting alike. Two methods dominate real‑world use:
Markov Chain Models
Markov chains examine the probability of conversion for each sequence of events. Remove a channel from those paths and watch how the probability drops. If the drop is steep, the channel truly assists conversions; if the drop is shallow, the channel merely appears near the finish line.
Shapley Value Models
Borrowed from cooperative game theory, Shapley values consider every possible order of channels. The model then calculates the average marginal lift each channel adds to a mix. Because it evaluates all permutations, the result feels fair and captures synergy that heuristics miss.
Both approaches rely on mathematics, not assumption, so they reveal sequence effects, channel partnerships, and diminishing returns that rule‑based frameworks overlook.
The Strategic Payoff
When a model ties credit to observed behavior, budget conversations shift from politics to evidence.
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Teams spot overlooked touchpoints i.e channels that rarely win last‑click credit yet quietly appear in high‑converting sequences.
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Marketers channel funds into these undervalued assets and often lift ROI without raising total spend.
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Finance leaders appreciate algorithmic models because they can validate them. Pause a top‑ranked channel and conversions should decline in line with the model’s estimate. If results differ, you recalibrate the model and move on.
Four Requirements for Success
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Clean, unified data
Deduplicate leads, standardize UTM tags, and include offline touchpoints where possible. -
Both winners and losers
Feed the model journeys that ended in conversion and those that did not. Negative examples teach the algorithm what doesn’t work. -
Sufficient volume
I typically aim for at least 1 000 conversions per modelling window or about 50 conversions per channel to keep the estimates stable. -
External validation
Run hold‑out tests or geo‑experiments. Ground‑truth checks keep the model honest and boost stakeholder trust.
Where Rule‑Based Models Still Help
Heuristics aren’t useless. They power quick dashboards and satisfy stakeholders who prefer a straightforward metric to a statistical explanation. For directional insights, a U‑shape or last‑touch view can still guide day‑to‑day decisions.
When It’s Time to Level Up
If you need to:
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optimise spend across an expanding channel mix
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understand complex, multi‑step buying journeys
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reconcile conflicting reports from several platforms
then fixed rules have reached their ceiling. Algorithmic attribution provides a more precise, adaptable alternative but only for organizations willing to invest in data discipline and continuous validation.
Adopt the right model, maintain clean inputs, and you’ll trade guesswork for confident budget moves and ultimately see the results in revenue, not just reports.
