A finished BUS-655 Topic 6 renewal risk model example, scoring full-season members for lapse risk, validating the ranking by decile and cutting a call list to staff capacity. Searches like "bus 655 topic 6 assignment example", "bus655 topic 6 sample" and "bus-655 topic 6 example" land here.
What a finished BUS-655 Topic 6 renewal risk model looks like
The finished model begins with a definition, because lapse can mean dropping out entirely, moving to a smaller plan or switching seats, and each carries a different cost. Full cancellation is the outcome modeled, with downgrades tracked separately. Drivers come from the club's own records: the share of games a member's tickets were scanned, how often seats were transferred or left unused, tenure, plan size, payment method, service complaints and the price change the member faced last renewal. Estimates are illustrative and labeled. The model is judged by how well it ranks members, shown in a table of lapse rates by risk decile on a held-back season. A final section argues that the call list should favor members whose risk is high and whose decision is still open, rather than those already certain to leave.
How a BUS-655 Topic 6 example is structured
The model reads from outcome to drivers to ranking to action. An opening section defines lapse, sets the renewal deadline as the point of prediction and states that only information available before outreach begins may enter. The data section describes the account histories and the seasons covered. Drivers are then introduced in groups, usage, account features and experience, each with the direction expected and the reason. Estimation follows, with the method named and the illustrative results reported as changes in lapse probability rather than coefficients. Validation comes next: the decile table on a held-back season, showing whether the top-scored members did lapse more often. The action section sizes the call list to the service team's hours and makes the case for favoring undecided members. A limits paragraph closes the model, noting that a score predicts leaving and does not prove a call will prevent it.
Lapse defined before it is modeled
Cancellation, downgrading and seat moves are distinguished at the outset, since each costs the club differently and a model of one says little about the others.
Unused seats as the loudest signal
The share of a member's games that went unscanned or were transferred away enters first, because tickets sitting unused are the plainest sign of fading commitment.
Only pre-deadline information allowed
Anything the club learns after outreach begins is excluded from the drivers, which keeps the model honest about what could have been known in time to act.
Ranking tested on a held-back season
Members from a season the model never saw are sorted into risk deciles, and the lapse rate in each decile shows whether the ordering holds up.
A call list sized to real hours
The service team's available hours set how many members receive a call, so the list is cut at a length the staff can actually work before the deadline.
Open decisions ahead of certain ones
Members almost sure to leave and those almost sure to stay both gain little from a call, so outreach concentrates on the undecided middle of the ranking.
Where marks go in BUS-655 Topic 6
The deduction graders reach for first is a model that predicts lapses and never says who gets called. An accurate score with no outreach plan attached answers a question the service team did not ask. Drivers that leak the outcome cost heavily, such as a flag for members who already declined the renewal offer, which produces excellent accuracy and no warning at all. Papers judging the model by overall accuracy miss that most members renew, so a model predicting universal renewal scores well and helps no one; ranking by decile is the test that matters. Treating every lapse as one outcome blurs a lost account with a member who moved to a smaller plan. Finally, a call list longer than the staff can work, or aimed at members certain to leave, spends effort where it cannot change the result.
Get a BUS-655 Topic 6 example written to your instructions
Send the BUS-655 Topic 6 instructions and the rubric in your classroom, plus any account data or case your section is using. A custom example is written to those criteria, with lapse defined, drivers limited to what was known in time, the ranking validated and a call list sized to staff hours, in 24 to 48 hours. The first one is free.
BUS-655 Topic 6 questions, answered
Why not judge a retention model by its accuracy?
Because when most members renew, a model that simply predicts everyone renews looks accurate and is useless. What the club needs is ranking: are the members scored highest actually more likely to leave? A table of lapse rates by risk decile on a held-back season answers that directly, and it translates straight into how far down the list a call campaign should go.
What data predicts season ticket cancellations?
Usage is typically the strongest signal, since members whose seats go unscanned or are routinely passed on have less reason to renew. Tenure, plan size, payment method, service complaints and the price change at the last renewal often add to it. Team results matter too, but the club cannot act on them, so the model is most useful for the signals outreach can address.
Does a high risk score mean a call will save the member?
Not necessarily. The score estimates who is likely to leave, not who will respond to contact. A member moving out of the region will leave regardless, while one frustrated by unused seats might stay with an exchange option. Testing outreach on part of the list and comparing renewal rates is how a club learns whom a call actually helps.