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Churn Prediction: How Customers' Usage under Contract is Linked to Churn

Limseth, Amanda Cecilie Limseth
Master thesis
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2604392.pdf (1.762Mb)
Appendix I_ R Code.docx (27.44Kb)
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https://hdl.handle.net/11250/2686489
Utgivelsesdato
2020
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Samlinger
  • Master of Science [963]
Sammendrag
Businesses will always have a certain amount of customers leaving for various

reasons, whether it's for another competitor or because their perception of value

isn’t met. The concept of churn prediction is based on investigating the pattern of

behavior that has shown to lead to customers not renewing their membership in

the past, and by identifying similar behavior in the current customer base to find

the at-risk customers before they churn.

There exists large variability in the customer usage under contract, both in the

total number of visits and in the proportional type of activity attended. The study

will use two different statistical approaches to construct a binary model for how

usage under contract is linked to churn. Both approaches will differentiate

between the usage amount and type of activity under contract to ultimately

explain and predict the likelihood of churn in the fitness industry. The results from

the binary prediction model indicate that there is a significant difference in the

estimated likelihood of churn in the fitness industry based on the customer usage.

It found that members with monthly averages over the contract period at the mean

or higher is generally less likely to churn and less affected by the change in

explanatory variables like activity type, age, and proportional usage in the last

months leading up to the renewal decision. The members that showed lower usage

over the contract period had churn likelihoods that were considerably more

affected by the change in usage variables.

The research will provide a statistical model that can be altered in order to work

for any business that has contractual memberships where the usage amount and

type of activity is recorded. The model will provide churn prediction probabilities

before they occur, which can enable businesses to take targeted action ahead of

time and attempt to lower their associated customer attrition by creating tailored

promotions to entice the customer to renew their contracts.
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Masteroppgave(MSc) in Master of Business Analytics - Handelshøyskolen BI, 2020
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