A finished MKT-462 Topic 2 cross-device path analysis example, rebuilding meal-kit sign-up paths across phone and laptop and showing where the recorded path splits into strangers. Searches like "mkt 462 topic 2 assignment example", "mkt462 topic 2 sample" and "mkt-462 topic 2 example" land here.
What a finished MKT-462 Topic 2 cross-device path analysis looks like
Paths from 1,200 new subscribers who logged into an account on at least two devices before ordering form the core of the finished analysis, every figure labeled illustrative. Seen whole, a typical path starts with a short recipe video on a phone, continues through two or three phone visits to the menu page, and ends with a sign-up on a laptop days later. Seen as the analytics tool records it without logins, the same person is three visitors, and only the laptop visitor converts. The analysis then counts what the split does to credit: paid social, present at the start of about 60 percent of stitched paths, receives about 9 percent of sign-ups in the unstitched report. A closing caution notes that people who log in early may not behave like those who never do.
How an MKT-462 Topic 2 example is structured
Opening with the report the company already trusts, the analysis shows sign-up credit by channel exactly as the dashboard presents it. A second section explains the sample: only subscribers who signed into accounts on two or more devices can be followed, and the section states plainly why that group may differ from everyone else. Reconstructed paths come next, drawn as five typical sequences with device, channel and elapsed days marked at each step. A comparison table sets channel credit from stitched paths beside the same credit from device-level data. The analysis then separates the breaks it can see, a device switch, a move from app to browser, a sign-up after the reporting window closed, from those it cannot, such as a recommendation over dinner. The last section sorts its conclusions into those that survive the sample's bias and those that need a test.
Only logged-in paths can be followed
The sample is limited to subscribers who signed in on two devices, and the analysis says why those early account holders may be keener than the average buyer.
Three visitors who are one person
Without a login, a recipe video on a phone, two menu visits and a laptop sign-up appear as separate people, and only the last one converts.
Credit moved by the device switch
Paid social opens about 60 percent of stitched paths yet earns about 9 percent of sign-ups in the device-level report, a gap the break itself creates.
A window that closes too early
Some subscribers order eleven days after first contact, beyond the seven-day window the reporting uses, so the touch that began their path is dropped entirely.
Breaks no tool can observe
A friend's recommendation or a kit seen on a coworker's desk leaves no record, and the analysis lists such influences as unmeasured rather than as absent.
Where marks go in MKT-462 Topic 2
Presenting the device-level report as though it described people is what this topic penalizes most, because a customer who switches devices has been split into strangers before any credit is assigned. The opposite overreach also costs marks: treating stitched paths from logged-in subscribers as representative of every buyer, when account holders who sign in early are a particular group. Drafts often draw a tidy path diagram with no elapsed time, which hides how many conversions fall outside the reporting window. Some analyses name cross-device breakage in a paragraph and never estimate what it does to any channel's credit. Influences that leave no record are frequently omitted altogether, so an absence in the data is read as an absence in the world. A closing claim that stitching solves attribution overstates it, since a complete path still shows sequence rather than cause.
Get an MKT-462 Topic 2 example written to your instructions
Send the MKT-462 Topic 2 instructions and the rubric posted in your classroom, with the company or path data your section supplied. We write a custom example to those criteria, with paths rebuilt across devices, the sample's bias stated, credit compared before and after stitching and unobservable influences named, in 24 to 48 hours. The first one is free.
MKT-462 Topic 2 questions, answered
What is cross-device tracking?
Linking the activity of one person across a phone, a laptop, a tablet or an app so that it appears as a single path. The most reliable link is a login, the same account used on each device. Without one, tools either leave the devices unconnected or estimate a connection from indirect signals, and browser restrictions and privacy rules have made that estimation harder and less common.
Why does a reporting window affect credit?
Most platforms and analytics tools count a touch only if the conversion happens within a set period afterward, such as seven days after a click. A considered purchase, whether a subscription or a sofa, may take longer. Touches outside the window are dropped, so channels that start paths early lose credit to those appearing near the end. The example reports how many sign-ups fall beyond the window used.
Are logged-in customers a fair sample?
Not necessarily. People who create accounts and sign in on several devices before buying may be more deliberate, more engaged or further along in deciding than those who arrive once and order. Their paths are the only ones fully visible, which makes them useful and also biased. The analysis uses them to show that fragmentation exists and how it shifts credit, and stops short of applying their exact figures to every subscriber.