Modelling customer churn for the retail industry in a deep learning based sequential framework Article Swipe
Juan Pablo Equihua
,
Henrik Nordmark
,
Maged Ali
,
Berthold Lausen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.00575
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.00575
As retailers around the world increase efforts in developing targeted marketing campaigns for different audiences, predicting accurately which customers are most likely to churn ahead of time is crucial for marketing teams in order to increase business profits. This work presents a deep survival framework to predict which customers are at risk of stopping to purchase with retail companies in non-contractual settings. By leveraging the survival model parameters to be learnt by recurrent neural networks, we are able to obtain individual level survival models for purchasing behaviour based only on individual customer behaviour and avoid time-consuming feature engineering processes usually done when training machine learning models.
Related Topics
Concepts
Purchasing
Order (exchange)
Feature engineering
Marketing
Feature (linguistics)
Business
Work (physics)
Artificial neural network
Customer retention
Retail industry
Deep learning
Computer science
Artificial intelligence
Engineering
Finance
Service quality
Linguistics
Philosophy
Service (business)
Mechanical engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2304.00575
- https://arxiv.org/pdf/2304.00575
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362597664
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4362597664Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2304.00575Digital Object Identifier
- Title
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Modelling customer churn for the retail industry in a deep learning based sequential frameworkWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-04-02Full publication date if available
- Authors
-
Juan Pablo Equihua, Henrik Nordmark, Maged Ali, Berthold LausenList of authors in order
- Landing page
-
https://arxiv.org/abs/2304.00575Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2304.00575Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2304.00575Direct OA link when available
- Concepts
-
Purchasing, Order (exchange), Feature engineering, Marketing, Feature (linguistics), Business, Work (physics), Artificial neural network, Customer retention, Retail industry, Deep learning, Computer science, Artificial intelligence, Engineering, Finance, Service quality, Linguistics, Philosophy, Service (business), Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.individual | 80, 90 |
| abstract_inverted_index.leveraging | 63 |
| abstract_inverted_index.parameters | 67 |
| abstract_inverted_index.predicting | 15 |
| abstract_inverted_index.purchasing | 85 |
| abstract_inverted_index.engineering | 97 |
| abstract_inverted_index.time-consuming | 95 |
| abstract_inverted_index.non-contractual | 60 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |