Predicting Customer Satisfaction by Replicating the Survey Response Distribution
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915026055462912 |
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| author | Manderscheid, Etienne Lee, Matthias |
| author_facet | Manderscheid, Etienne Lee, Matthias |
| contents | For many call centers, customer satisfaction (CSAT) is a key performance indicator (KPI). However, only a fraction of customers take the CSAT survey after the call, leading to a biased and inaccurate average CSAT value, and missed opportunities for coaching, follow-up, and rectification. Therefore, call centers can benefit from a model predicting customer satisfaction on calls where the customer did not complete the survey. Given that CSAT is a closely monitored KPI, it is critical to minimize any bias in the average predicted CSAT (pCSAT). In this paper, we introduce a method such that predicted CSAT (pCSAT) scores accurately replicate the distribution of survey CSAT responses for every call center with sufficient data in a live production environment. The method can be applied to many multiclass classification problems to improve the class balance and minimize its changes upon model updates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_12539 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting Customer Satisfaction by Replicating the Survey Response Distribution Manderscheid, Etienne Lee, Matthias Machine Learning Artificial Intelligence Computation and Language For many call centers, customer satisfaction (CSAT) is a key performance indicator (KPI). However, only a fraction of customers take the CSAT survey after the call, leading to a biased and inaccurate average CSAT value, and missed opportunities for coaching, follow-up, and rectification. Therefore, call centers can benefit from a model predicting customer satisfaction on calls where the customer did not complete the survey. Given that CSAT is a closely monitored KPI, it is critical to minimize any bias in the average predicted CSAT (pCSAT). In this paper, we introduce a method such that predicted CSAT (pCSAT) scores accurately replicate the distribution of survey CSAT responses for every call center with sufficient data in a live production environment. The method can be applied to many multiclass classification problems to improve the class balance and minimize its changes upon model updates. |
| title | Predicting Customer Satisfaction by Replicating the Survey Response Distribution |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2411.12539 |