Churn Prediction: How Customers' Usage under Contract is Linked to Churn
Master thesis
Permanent lenke
https://hdl.handle.net/11250/2686489Utgivelsesdato
2020Metadata
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- Master of Science [1613]
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.
Beskrivelse
Masteroppgave(MSc) in Master of Business Analytics - Handelshøyskolen BI, 2020