Attitudinal perspectives for predicting churn

Steffen Zorn (UWA Business School, The University of Western Australia, Crawley, Australia)
Wade Jarvis (UWA Business School, The University of Western Australia, Crawley, Australia)
Steve Bellman (Interactive Television Research Institute, Murdoch University, Murdoch, Australia)

Journal of Research in Interactive Marketing

ISSN: 2040-7122

Publication date: 4 June 2010



As acquiring new customers is costly, putting effort into satisfying and keeping customers over the long term can improve profitability. Firms usually do not know how each individual customer is feeling at any time (their attitude to the firm), so typically a customer's likelihood of leaving (“churning”) is predicted from behavioural data. The purpose of this paper is to investigate how a firm can add attitudinal variables to these churning models by deriving proxy indicators of satisfaction and commitment from behavioural data. The paper tests whether adding these proxies improved predictions of churning compared to a typical model based on purchasing behaviour (PB).


Analysing data from 6,000 regular customers from an Australian digital versatile disc rental company, logistic regression predicted membership termination (i.e. churning=1) versus continuation (=0). A baseline model used three traditional behavioural variables directly linked to members' PB. A second model including proxies for satisfaction and commitment from the customer database was compared against the baseline model to investigate improvement in churn prediction.


The most significant predictor of churn is an indicator of commitment: the uncertainty of a customer's commitment, indicated by number of times they changed their subscription plan.

Practical implications

The more customers change their plan, the more likely they are to quit the relationship with the firm, most likely because they are uncertain about how they can benefit from a long‐term commitment to the firm. Monitoring uncertainty indicators, such as plan changing, allows firms to intervene with special offers for uncertain customers, and, therefore, increase the likelihood of them staying with the firm.


The paper discusses the use of customer behaviour recorded in databases to identify proxy indicators of attitude before this attitude translates into churning behaviour.



Steffen Zorn, Wade Jarvis and Steve Bellman (2010) "Attitudinal perspectives for predicting churn", Journal of Research in Interactive Marketing, Vol. 4 No. 2, pp. 157-169

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