A finished MKT-462 Topic 3 attribution model comparison example, crediting one quarter of eyewear orders under five models and naming the assumption each one makes about cause. Searches like "mkt 462 topic 3 assignment example", "mkt462 topic 3 sample" and "mkt-462 topic 3 example" land here.
What a finished MKT-462 Topic 3 attribution model comparison looks like
The same 4,000 orders from one quarter appear five times in the finished comparison, each figure labeled illustrative. Under last click, branded search takes 42 percent of the credit and paid social 8. Under first click the order nearly inverts: paid social takes 38 percent and branded search 27, while display retargeting, which by design reaches only past visitors, falls from 14 percent to 5. Linear, time-decay and position-based models land between those poles. A table places the five results side by side, and a paragraph beneath each model states its hidden premise, for instance that the last touch did the persuading or that every touch mattered equally. The comparison then makes the point all five share: each divides credit among touches that happened, and none asks which orders would have occurred without them.
How an MKT-462 Topic 3 example is structured
Assumptions organize the comparison more than arithmetic does. An opening section describes the retailer's six paid and owned channels and the quarter of orders chosen, with paths drawn from logged-in customers. Each of the five models then gets a short section in the same pattern: how it divides credit, what it implies about how buyers decide and which channel it tends to favor. Last click and first click come first as the two extremes, followed by linear, time-decay and the position-based model that gives 40 percent to each end and splits the remainder. A side-by-side table shows the channel shares under all five. Data-driven attribution is discussed in a paragraph, as a statistical allocation still confined to observed paths. The comparison concludes that choosing among models is choosing among premises, and it recommends testing before any budget moves.
One quarter, five sets of credit
The same orders run through every model, so differences in channel share come from the rules applied and not from any change in customer behavior.
The extremes set out first
Last click and first click bracket the range, one crediting whatever closed the order and the other whatever opened the path, and both ignore the middle.
Retargeting credit depends on position
Because retargeting reaches only people who already visited, it looks strong at the end of paths and nearly vanishes when the first touch is rewarded.
Premises written beside each model
A sentence under every model names its assumption, such as recent touches persuading more than early ones, which exposes that the rule was chosen rather than observed.
Data-driven credit kept in its lane
Statistical attribution learns weights from converting and non-converting paths, and the comparison notes that it still sees only recorded touches and cannot establish what caused an order.
Where marks go in MKT-462 Topic 3
Comparing models by their diagrams is where papers on this topic lose the most, since describing the shape of position-based credit says nothing about what it does to this retailer's channels. Running the models on invented data that fails to add up is the next weakness, and markers often check that each model's shares total one hundred percent. Drafts frequently crown one model as correct, usually data-driven, without noticing that every model on the list allocates among recorded touches. Leaving out the assumption behind each rule turns the comparison into a menu. Retargeting's swing between positions is commonly missed, although it is the clearest sign that credit reflects the rule rather than the channel. A further loss comes from recommending a budget shift on the favored model's numbers, which the testing topics later in the course exist to challenge.
Get an MKT-462 Topic 3 example written to your instructions
Send the MKT-462 Topic 3 instructions and the rubric attached in your classroom, with the campaign data or case your section provided. We write a custom example to them, with one set of conversions credited under each model, the shares reconciled, every model's assumption stated and causal limits named, in 24 to 48 hours. The first one is free.
MKT-462 Topic 3 questions, answered
What is position-based attribution?
A rule that gives fixed shares of credit to the first and last touches in a path, commonly 40 percent each, and divides the remaining 20 percent evenly among the touches in between. It is sometimes called U-shaped. The premise is that opening and closing a path matter most. Like every rule-based model, it sets those weights by assumption, and the example shows how its results differ from linear credit on the same orders.
Is data-driven attribution more accurate?
It fits the retailer's own data rather than a fixed rule, comparing paths that converted with paths that did not and assigning weights accordingly, which is an improvement in one sense. It still works only with recorded touches, so device breaks, showroom visits and people who would have bought anyway remain outside it. Its credit describes association within observed paths, so the example still recommends testing.
Which attribution model should a company use?
The example declines to name one as correct, because each rests on a premise about how buyers decide and none measures what would have happened without the spend. Companies often keep one model for routine reporting, with its bias understood, and use experiments to check the channels where the models disagree most. The comparison ends by naming those channels as candidates for the tests that come later.