Why leave the heuristics behind
Last session ended on a ceiling: every heuristic is a fixed rule with a knob a human turned. If the webinar in your funnel is really worth 45%, no heuristic will ever find that out - it has no way to read your data. Data-driven attribution breaks that ceiling. Instead of asserting the weights, it learns them from the whole population of journeys. Two ideas do almost all the work - the removal effect (Markov) and fair-share marginal contribution (Shapley). You do not need the math. You need to understand what question each one asks, and where each one still falls short. Because "learned from data" and "true" are not the same thing.
Markov: what happens if we remove this channel? 7 min live
The Markov idea is disarmingly simple. Picture every customer journey as a walk between states - Start, then each channel, ending at either Convert or "Null" (left without buying). Across all your journeys, you can measure how often each move happens. Then you ask the one question that gives Markov its power: if we deleted this channel entirely, how much would conversion drop? That drop is the channel's credit.
LiveThe removal effect, in one plain sentence3 min▶
Markov gives the most credit to the channels whose absence would collapse conversion. Delete a channel from every journey, re-measure the conversion rate, and the size of the drop is that channel's importance. A channel nobody misses gets little; a channel that quietly holds the funnel together gets a lot - even if it never grabs the last click.
- It reads the whole population, not one path. Unlike a heuristic, Markov does not split this $92 journey - it learns from all of Lumen's journeys at once what each channel is worth.
- It rewards the connectors. On Lumen's data, paid social and email often score high on removal effect - they are the touches that keep people moving toward the purchase, even though last-touch handed them almost nothing.
- Critical caveat: removal effects do not sum to 1. Each is measured independently, so they must be normalized before you read them as budget shares. A vendor showing raw removal effects that add up to 130% is showing you an un-normalized model.
A retailer ran the removal effect and found their email channel - dead last under last-touch - was the single biggest drop when removed. Pulling email out of the journey cost more conversions than pulling out paid search. They had been about to cut the email team. Removal effect saved the channel, because it measured what was lost in its absence, not what showed up at the finish line.
Shapley: everyone's fair average contribution 7 min live
Shapley comes from game theory - Lloyd Shapley, 1953 - and it answers a fairness question: if channels are teammates cooperating to win a conversion, what is each one's fair share of the prize? The answer is each channel's average marginal contribution: across every possible order the team could have formed, how much did adding this channel move the result?
LiveMarginal contribution and shared synergy3 min▶
The mechanic in one line: line the channels up in every possible order, and each time a channel joins, note how much it added to the result so far. Average those additions over all orderings - that average is the channel's Shapley credit.
- It is provably "fair". Shapley is the only credit split that satisfies four common-sense axioms at once - efficiency (shares sum to the whole), symmetry (equal channels get equal credit), null-player (a useless channel gets zero), and additivity. That is why it is the reference standard for fairness.
- It shares synergy. If display and social together drive more than the sum of what each does alone, that surplus gets split between them - neither one hogs the joint win. Heuristics cannot do this; they have no concept of two channels lifting each other.
- The cost is the catch. Exact Shapley is
O(2^n)- it considers every subset of channels. Fine for the ~10-15 channels most brands run; beyond that, teams switch to Monte-Carlo approximations. For Lumen's nine channels, exact Shapley is comfortable.
GA4 DDA, and the black-box warning 6 min live
You may never run Markov or Shapley yourself - but you almost certainly already use a data-driven model, because Google made it the GA4 default. GA4's Data-Driven Attribution (DDA) is Shapley-based with a time-decay element, and Google removed the old rules-based models from GA4 reporting entirely. It is powerful. It is also a black box - and that combination is exactly where leaders get fooled.
LiveWhy "data-driven" is not the same as "true"3 min▶
DDA is a genuine upgrade over last-touch. But two properties should keep you cautious every time you read one.
- It is a black box. Google does not expose the model or the per-channel weights. You get a number and no way to audit how it was produced. You cannot ask it "why did display get 8%?" and get an answer.
- It only sees what Google can see. DDA works on Google-observable, consented, web-and-app touches - it defaults to the last 50 interactions with a 90-day lookback. Your influencer, your CTV, your offline, your unconsented visitors, and every non-Google channel are simply invisible to it. It is not modeling the journey; it is modeling the slice of the journey Google happened to witness.
- It is still correlational. Like Markov and Shapley, DDA finds channels that correlate with conversion. It cannot prove any channel caused it. Only incrementality experiments do that - Sessions 4 and 5.
A CMO presented GA4 DDA numbers to the board as "the truth, finally - it's AI". A sharp director asked one question: "Does it include our CTV spend?" It did not - CTV is not a Google-observable web touch, so DDA never saw it. Half the media budget was invisible to the model being sold as ground truth. The number was not wrong; it was answering a smaller question than the room assumed.
Self-studyThe family tree: heuristic → learned → causal2 min read▶
| Model | Core idea | Still just correlation? |
|---|---|---|
| Markov | Removal effect - what conversion you'd lose without it | Yes - correlational |
| Shapley | Average marginal contribution, fairly shared | Yes - correlational |
| GA4 DDA | Shapley + time-decay, on Google-observable touches | Yes - correlational + partial view |
| Incrementality (Session 4-5) | Randomized holdout / geo experiment | No - this one is causal |
Data-driven is a real step up from heuristics - it learns instead of asserting. But it is still standing on correlation. The jump to causation needs an experiment, not a smarter split. That is the whole next arc of the course.
Read a data-driven report - and ask the three black-box questions ★ 10 min · on your reports
No math. We put a data-driven attribution report on screen - Lumen's GA4 DDA output - and practice the three questions that puncture any "it's AI, trust it" claim. These three questions are the entire leader skill for this session.
Question 1 - What window? Ask what lookback and interaction limit the model uses. Lumen's DDA defaults to a 90-day lookback and the last 50 interactions. A journey that started 100 days ago, or the 51st touch back, is invisible. If your real cycle is longer than the window, the model is cropping your journeys.
Question 2 - What touches can it see? Ask which channels the model actually observes. DDA sees Google-observable, consented, web-and-app touches only. For Lumen that means influencer, CTV, and offline are missing entirely. Name every channel the model is blind to.
Question 3 - Can you see the weights? Ask to see the per-channel model, not just the output. With GA4 DDA the answer is no - it is a black box. That is not a dealbreaker, but it means you cannot audit why a channel got its credit, so you treat the number as an informed opinion, not a proof.
Write the verdict: "This is a data-driven model, which beats last-touch - but it runs a 90-day window, is blind to CTV and influencer, and won't show its weights. I'll use it for direction, not as the final word on budget."
When is data-driven worth it - and when is it overkill? ★ 10 min · pen and paper
Data-driven is not automatically the right choice. It needs volume, a real multi-touch journey, and a decision big enough to justify it. Decide, out loud, when Lumen should reach for it and when a heuristic is honestly good enough.
Take a low-volume, short-path case - a new Lumen product with 40 conversions a month, mostly one-touch. Is Markov/Shapley worth it? (No - there is not enough journey data to learn from, and barely a path to split. A simple model is more honest here.)
Take a high-volume, multi-touch case - Lumen's core serum line, thousands of 5-touch journeys a month across nine channels. Worth it? (Yes - this is exactly where learned credit beats a fixed rule, because the interactions between channels are real and measurable.)
Take a strategic budget case - reallocating the $4M media budget. Is data-driven enough? (No - it is better than a heuristic, but it is still correlational. A budget of that size needs an incrementality test on top. That is the honest limit of everything in this session.)
Write your rule of thumb: data-driven attribution earns its complexity when you have volume, genuine multi-touch journeys, and a decision worth the effort - and even then, it informs the budget rather than deciding it.
A team spent three months standing up a Shapley model for a product line that got 30 conversions a month. The model's outputs swung wildly week to week because there was not enough data to stabilize them - and they made real budget cuts on the noise. Sophistication without volume is not rigor; it is expensive guessing with a fancier label.
This week ◐ 25 min total
- Run the three questions on a real report. Take your own GA4 or vendor data-driven report and answer, in writing: what window, what touches can it see, can you see the weights? Bring the answers to Session 4.
- Find your model's blind channels. List every channel your data-driven model cannot observe - offline, CTV, unconsented, non-Google. That list is the size of your measurement gap.
- Catch a "true" claim. Find one place someone treats a data-driven number as ground truth. Write the one-sentence correction: "It's learned, not proven - it's still correlational."
- Optional: read Google's GA4 Data-Driven Attribution help doc (Skillshop) - note where it says the model and weights are not exposed. That sentence is the black box, in Google's own words.
Three questions before you go 🎯 ◐ 90 seconds
1 · In Markov attribution, the "removal effect" measures...
Removal effect = the conversion you would lose without the channel. Channels whose absence collapses conversion get the most credit - and the raw effects must be normalized, since they do not sum to 1.
2 · What makes Shapley value attribution distinctive among the models?
Shapley averages each channel's marginal contribution across all orderings and splits joint synergy between the channels that created it. Heuristics have no concept of channels lifting each other.
3 · A director says "GA4 DDA is AI, so it's the true attribution". Your correction?
DDA does not expose its model or weights, and it only sees Google-observable web/app touches - so CTV, offline, influencer, and non-Google channels are invisible. It is a better opinion than last-touch, still correlational, and far from ground truth.
What this session covers
This session distills the data-driven-attribution chapter of the leading courses into leader-first concepts - the removal effect, fair-share marginal contribution, and the GA4 black box - without any math derivation. Certificates and full video courses stay with their official sources.