A finished BUS-655 Topic 4 fan data audit example, grading each fan data source for the segmentation, lifetime value and sponsor claims it can support, with buyers kept apart from attendees. Searches like "bus 655 topic 4 assignment example", "bus655 topic 4 sample" and "bus-655 topic 4 example" land here.
What a finished BUS-655 Topic 4 fan data audit looks like
The finished audit is a claims table more than a data catalog. Each source appears with what it records and who it actually describes, and the first finding is that a ticketing account describes a purchaser, not the three or four people who used the tickets. App scans come closer to attendance, but only for fans who use the app. From there the audit tests specific claims the marketing team wanted to make. Segments by purchase frequency and recency are supported; segments by motivation are not, since nothing collected records why anyone came. Lifetime value is estimated only as a range, because the club holds three seasons of retention history. Sponsor reporting is limited to what the contest's consent terms allow, which means totals. Every figure is illustrative and labeled, and no real club's records appear.
How a BUS-655 Topic 4 example is structured
The audit runs source by source and then claim by claim. An opening paragraph lists the decisions waiting on fan data: which segments receive which renewal offer, how much the club should spend acquiring a new buyer, and what to report to a sponsor at season end. The source inventory follows, one entry per system, recording who each record describes, how it was collected and what its consent terms permit. Linkage comes next, showing how far accounts, app users and store customers can be matched and where matching would require guesswork. The claims table is the center of the audit, with each proposed segmentation, value or sponsor claim marked supported, supported as a range, or unsupported, and the reason given. A lifetime value section works the range through with labeled figures. The closing part lists the cheapest collection changes that would let the unsupported claims be tested.
Purchasers distinguished from attendees
A ticketing account records who paid, so the audit keeps buyers and the people actually in the seats as separate populations in every claim that follows.
Consent terms read before any use
Each source is checked against the privacy terms fans accepted when it was collected, and a use those terms never covered is excluded however useful it looks.
Segments the records can carry
Groupings by recency, frequency and spend survive the audit, while groupings by motive or lifestyle fail because no source ever recorded why anyone attended.
Lifetime value as a range
Three seasons of illustrative retention history support only a band of plausible values per buyer, and the audit shows how that band would narrow with more seasons.
Sponsor reporting in aggregate only
Contest entries can show a sponsor how many opted-in fans engaged with an activation, reported as totals and never as individual records passed across.
Cheap additions that close gaps
A short list of collection changes, such as one postgame survey question, would let an unsupported claim be tested next season, and each is costed in staff time.
Where marks go in BUS-655 Topic 4
Where the audit fails, it usually fails by trusting the ticketing account as a person. Segment profiles built on account holders describe the group organizer who buys for a family or an office, and papers calling them fans have mislabeled most of the crowd. Motivation segments inferred from purchase records lose credit for claiming what the data never captured. Lifetime value delivered as a single precise figure, from a handful of seasons, overstates what a retention history that short can establish. Graders in many sections also mark down any plan to pass individual fan records to a sponsor, which consent terms rarely allow and which rubrics count against professional judgment. A final deduction goes to an audit that grades sources in general terms and never tests the specific claims the club wanted to make.
Get a BUS-655 Topic 4 example written to your instructions
Send us the BUS-655 Topic 4 instructions, the rubric posted in your classroom and a description of the fan data your case provides. We write a custom example to those criteria, with sources graded, buyers separated from attendees, consent checked, claims marked supported or not and lifetime value shown as a range, back in 24 to 48 hours. Your first one is free.
BUS-655 Topic 4 questions, answered
Can ticketing data tell a club who its fans are?
Only partly. It tells the club who bought, how often, how far ahead and at what price, which is valuable for renewal and offer decisions. It does not tell the club who sat in the seats or why they came. Many clubs add app scans, postgame surveys or email preference questions to fill those gaps, and the audit says which gap each addition would close.
How is fan lifetime value calculated?
In broad terms, as the expected margin a buyer produces each season, carried through the seasons they are likely to stay and discounted to the present. The difficult input is retention, which a club with only a few seasons of history cannot estimate precisely. The example reports a range for that reason, and it uses margin rather than revenue so the figure can guide acquisition spending.
Can the club share fan data with a sponsor?
Only as the consent terms and applicable privacy law permit, and course papers should treat that as a constraint rather than an obstacle. Aggregated results, such as the number of opted-in entrants or redemption counts, usually satisfy a sponsor's reporting needs. The example reports activation results as totals. Your instructions or case may specify the rules to apply, and nothing here is legal advice.