MKT-838 · Topic 2

MKT-838 Topic 2 hit attribution post-mortem example

Complexity of Marketing Grand Canyon University Free custom sample in 24 to 48h

Two puzzle games from a composite mobile studio scored almost identically in pre-launch playtests and launched a month apart with matching creator campaigns; one reached the top-grossing chart and the other faded within weeks. The studio's internal review credits the winner's campaign. This MKT 838 post-mortem reconstructs the first fortnight of each launch and locates the divergence in feedback rather than in anything the campaign did.

What this page holds

A finished MKT-838 Topic 2 hit attribution post-mortem example that traces two near-identical game launches, finds the feedback loop that split them and limits what the campaign can claim. Searches like "mkt 838 topic 2 assignment example", "mkt838 topic 2 sample" and "mkt-838 topic 2 example" land here.

What a finished MKT-838 Topic 2 hit attribution post-mortem looks like

The finished post-mortem sets the twin launches side by side, day by day, for their first two weeks. Divergence begins on day three, when the eventual hit received an editorial feature slot that the studio did not buy and whose timing it did not choose. Chart position then fed visibility, visibility fed installs and installs fed position again, a loop the critique draws with the store's ranking rules described from public documentation. Arthur's work on increasing returns frames the pattern, showing how small historical events can lock a market onto one of several possible outcomes. MusicLab, the artificial music market run by Salganik, Dodds and Watts, supplies the evidence that such outcomes resist prediction, since the same songs finished at very different ranks in parallel worlds. The campaign is credited with raising both games' chances and with nothing beyond that.

How an MKT-838 Topic 2 example is structured

Opening the post-mortem is the internal review's conclusion, quoted, with the counterclaim beside it in one sentence. Pre-launch evidence comes next, comparing the two games before release, including playtest ratings, store pages, prices and campaign spend, and finds no difference large enough to predict the outcome. The launch timelines follow as a paired table of daily chart rank, featured placements and installs, with illustrative figures. The fourth section draws the amplifying loop and marks the day the games parted. Theory enters next: increasing returns explain why the loop locked in, and the MusicLab worlds explain why nobody could have named the winner in advance. The objection that the hit is simply the better game receives its own section, answered with the pre-launch ratings and a concession that quality bounds the range of outcomes. The document finishes by rewriting the review's attribution sentence in terms the evidence can bear.

Twin games with matching campaigns

Playtest scores, store pages, prices and creator spend were close enough before launch that no reviewer holding the files could have predicted which game would lead.

Day three and an unbought feature

An editorial slot the studio neither purchased nor scheduled lifted one game's rank on day three, and every later difference between the launches traces back to that placement.

Rank, visibility and installs in a loop

Higher chart position brought more store visibility, visibility brought installs and installs raised position again, a reinforcing loop drawn from the store's published ranking rules.

Increasing returns and lock-in

Arthur's account of small historical events locking a market onto one outcome explains why a two-day lead hardened into a permanent one instead of a gap that closed.

Parallel worlds and unpredictable winners

In the Salganik, Dodds and Watts experiment the same songs ranked very differently from one world to the next, which is why no forecast could have named this winner.

An attribution sentence rewritten

The review's claim that the campaign made the hit becomes a narrower one: the campaign raised both games' odds, and feedback after day three chose between them.

Where marks go in MKT-838 Topic 2

Crediting the winning campaign because it came before the hit is the characteristic error here, and a critique that repeats it in softer language has only renamed the review. Papers also slip when they call the result pure luck, which ignores the loop that turned one chance placement into a lasting lead. The twin game is the strongest evidence available, yet many drafts analyze the hit alone and leave nothing to compare it against. Readers who take MusicLab to show that quality is irrelevant have misread it, since the study found the best songs rarely finishing last and the worst rarely winning. Post-launch ratings are frequently offered as proof of superior quality, although they were collected from an audience the hit's visibility assembled. A rewritten attribution that still names a single cause leaves the review's logic intact.

Get an MKT-838 Topic 2 example written to your instructions

Send the MKT-838 Topic 2 instructions, the rubric your classroom shows and the launch or case your section assigned. A custom example is written to those criteria, with the compared cases set side by side, the amplifying loop located, the quality objection answered and the attribution restated in defensible terms, and it arrives in 24 to 48 hours. The first one is free.

MKT-838 Topic 2 questions, answered

Why can't a studio predict which launch will become a hit?

Because in markets where people see what others choose, early differences compound. The MusicLab study by Salganik, Dodds and Watts demonstrated it in an online music market split into separate worlds: where participants saw download counts, the same songs finished at very different ranks from world to world. Quality set limits, since the strongest songs seldom came last, but the middle of the field stayed open. The example applies that finding to game charts.

What are increasing returns in a market?

A condition in which an option becomes more attractive the more people have already adopted it, through visibility, compatibility or social proof. W. Brian Arthur showed how, under increasing returns, minor chance events early on can lock a market onto one of several possible outcomes, not necessarily the best. The example finds that condition in the app store's ranking rules and uses it to explain why one feature slot decided a launch.

So did the campaign do nothing?

Not necessarily, and the example avoids that overcorrection. Both games received matching campaigns, which likely raised each one's chance of catching the early attention the loop amplifies. What the evidence cannot support is crediting the campaign with the difference between them, since the campaigns were the same and the outcomes were not. The rewritten attribution sentence keeps the campaign's plausible role and drops the claim of decisive cause.