Finished BU 443 Unit 3 channel performance analysis: the attribution model declared first, one period and definition set held throughout, then repeated under a second model. Searches like "bu 443 unit 3 assignment example", "bu443 unit 3 sample" and "bu 443 unit 3 example" land here.
What a finished BU 443 Unit 3 channel performance analysis looks like
The analysis is two versions of one table with an argument running between them. The opening fixes the period, the definitions taken from the earlier sheet and the attribution model in use, saying what that model does with a visit arriving through several sources. The first table compares channels on the same handful of measures, with the base for every rate visible in the cell. A second table repeats that comparison under a different model, and the two sit close enough together that any change in ranking is obvious. Commentary reads the pair: which channels hold position under both, which move, and what the movement says about how much credit a single model was quietly handing out. Overlapping paths get a short section. The close keeps only what survived both readings.
How a BU 443 Unit 3 example is structured
The model is declared before any figure appears because every number in both tables is a product of it, and a comparison presented without one implies a neutrality that does not exist. Period and definitions are fixed in the same opening so a reader knows all three constraints before meeting the first row. The tables are placed together rather than in separate sections, since the entire argument depends on watching the same channels move. Bases appear inside the tables because a channel with very few visits can lead a rate column and mean almost nothing. Overlap is treated apart from ranking, as the paths people actually took are a different question from who receives the credit. Surviving conclusions close the document, and the ones that did not survive are named rather than dropped, which is what makes the second table worth running.
The model declared before any figure
Attribution is named in the opening because every number in the tables is a product of it rather than an observation of the world.
One period, one set of definitions
The comparison holds the same dates and counting rules across every channel, since letting either drift invalidates the column.
Bases visible inside the table
Each rate shows the count it was calculated on, as a channel with a handful of visits can lead a percentage meaninglessly.
The same comparison run twice
A second model repeats the table so a reader can watch the ranking shift instead of accepting the first ordering as settled.
Overlap kept apart from credit
The paths visitors actually took are examined separately, because how people arrived is a different question from who gets counted.
Only surviving conclusions retained
The close keeps findings that held under both readings and names the ones that did not, which is why the second table exists.
Where marks go in BU 443 Unit 3
The failure this piece invites is a league table with no model behind it. A ranking of channels by conversions, presented as though those figures were observations, has hidden the assumption doing all of the work. Comparisons mixing periods, or built on definitions that shifted between tools, produce a table nobody can read. Rates reported without their bases let a channel with a handful of visits appear to be the strongest performer, and it is the fastest route to a wrong recommendation. Model names dropped in without saying what the model does with a shared path are decoration. Claims about which model a platform applies by default, stated from memory rather than checked and dated, go stale quickly. Campaign reporting exported from an employer's accounts cannot be the dataset.
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BU 443 Unit 3 questions, answered
Which attribution model should the analysis use?
Whichever your instructions specify. Where the decision is left to you, take the model your own data can actually support and state its consequences plainly. What matters more than the selection is that you say what the model does with a path involving several sources, and that you show at least one alternative reading so the ranking is not offered as a fact about the world.
What if the second model barely changes anything?
That is a result worth reporting and it strengthens the paper. Say the ordering held, print both tables anyway and explain why the stability makes your conclusion sturdier. Stability usually means one channel dominates the paths in this data, which is itself a finding. What loses credit is running a single model and then asserting the ranking would have held regardless.
Can I compare channels with very different volumes?
Yes, provided the counts are on the page and the commentary refuses to let a tiny base win. Report the volumes beside the rates, say which comparisons are too thin to lean on, and consider grouping the smallest channels rather than granting each a row that implies more precision than this data holds.