Machine learning based customer churn prediction in home appliance rental business
Abstract Customer churn is a major issue for large enterprises. In particular, in the rental business sector, companies are looking for ways to retain their customers because they are their main source of revenue. The main contribution of our work is to analyze the customer behavior information of a...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-04-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-023-00721-8 |
| _version_ | 1797850042415972352 |
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| author | Youngjung Suh |
| author_facet | Youngjung Suh |
| author_sort | Youngjung Suh |
| collection | DOAJ |
| description | Abstract Customer churn is a major issue for large enterprises. In particular, in the rental business sector, companies are looking for ways to retain their customers because they are their main source of revenue. The main contribution of our work is to analyze the customer behavior information of actual water purifier rental company, where customer churn occurs very frequently, and to develop and verify the churn prediction model. A machine learning algorithm was applied to a large-capacity operating dataset of rental care service in an electronics company in Korea, to learn meaningful features. To measure the performance of the model, the F-measure and area under curve (AUC) were adopted whereby an F1 value of 93% and an AUC of 88% were achieved. The dataset containing approximately 84,000 customers was used for training and testing. Another contribution was to evaluate the inference performance of the predictive model using the contract status of about 250,000 customer data currently in operation, confirming a hit rate of about 80%. Finally, this study identified and calculated the influence of key variables on individual customer churn to enable a business person (rental care customer management staff) to carry out customer-tailored marketing to address the cause of the churn. |
| first_indexed | 2024-04-09T18:53:58Z |
| format | Article |
| id | doaj.art-895997b5822b4cb2bf5297e76f4f0747 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-04-09T18:53:58Z |
| publishDate | 2023-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-895997b5822b4cb2bf5297e76f4f07472023-04-09T11:18:54ZengSpringerOpenJournal of Big Data2196-11152023-04-0110113510.1186/s40537-023-00721-8Machine learning based customer churn prediction in home appliance rental businessYoungjung Suh0LG Electronics IncAbstract Customer churn is a major issue for large enterprises. In particular, in the rental business sector, companies are looking for ways to retain their customers because they are their main source of revenue. The main contribution of our work is to analyze the customer behavior information of actual water purifier rental company, where customer churn occurs very frequently, and to develop and verify the churn prediction model. A machine learning algorithm was applied to a large-capacity operating dataset of rental care service in an electronics company in Korea, to learn meaningful features. To measure the performance of the model, the F-measure and area under curve (AUC) were adopted whereby an F1 value of 93% and an AUC of 88% were achieved. The dataset containing approximately 84,000 customers was used for training and testing. Another contribution was to evaluate the inference performance of the predictive model using the contract status of about 250,000 customer data currently in operation, confirming a hit rate of about 80%. Finally, this study identified and calculated the influence of key variables on individual customer churn to enable a business person (rental care customer management staff) to carry out customer-tailored marketing to address the cause of the churn.https://doi.org/10.1186/s40537-023-00721-8Big data applicationsCustomer churn predictionMachine learningChurn in rental businessFeature selectionCustomer retention management |
| spellingShingle | Youngjung Suh Machine learning based customer churn prediction in home appliance rental business Journal of Big Data Big data applications Customer churn prediction Machine learning Churn in rental business Feature selection Customer retention management |
| title | Machine learning based customer churn prediction in home appliance rental business |
| title_full | Machine learning based customer churn prediction in home appliance rental business |
| title_fullStr | Machine learning based customer churn prediction in home appliance rental business |
| title_full_unstemmed | Machine learning based customer churn prediction in home appliance rental business |
| title_short | Machine learning based customer churn prediction in home appliance rental business |
| title_sort | machine learning based customer churn prediction in home appliance rental business |
| topic | Big data applications Customer churn prediction Machine learning Churn in rental business Feature selection Customer retention management |
| url | https://doi.org/10.1186/s40537-023-00721-8 |
| work_keys_str_mv | AT youngjungsuh machinelearningbasedcustomerchurnpredictioninhomeappliancerentalbusiness |