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Pregled bibliografske jedinice broj: 1137288

Advanced Analytics Techniques for Customer Activation and Retention in Online Retail


Matić, Igor; Mršić, Leo; Keppler, Joachim
Advanced Analytics Techniques for Customer Activation and Retention in Online Retail // International Conference on Intelligent Computing & Optimization ICO 2020: Intelligent Computing and Optimization / Pandian Vasant, Ivan Zelinka, Gerhard-Wilhelm Weber (ur.).
Zürich: Springer, 2021. str. 1-15 doi:10.1007/978-3-030-68154-8_62


CROSBI ID: 1137288 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Advanced Analytics Techniques for Customer Activation and Retention in Online Retail

Autori
Matić, Igor ; Mršić, Leo ; Keppler, Joachim

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, ostalo

Knjiga
International Conference on Intelligent Computing & Optimization ICO 2020: Intelligent Computing and Optimization

Urednik/ci
Pandian Vasant, Ivan Zelinka, Gerhard-Wilhelm Weber

Izdavač
Springer

Grad
Zürich

Godina
2021

Raspon stranica
1-15

ISBN
978-3-030-68154-8

Ključne riječi
Online, Retail, Web store, E-commerce, Big data analytics, Machine learning, Churn prediction, Prevention and retention

Sažetak
In an age of ubiquitous, super-fast internet, online orders have been increasing exponentially. This, in turn, significantly increases the customer's options in terms of product range and price, and thus has an impact on the increased competition between companies. It was known that customers are often switching between offers and thus between companies or just stayed dormant. The associated decrease in the average order frequency therefore managing customer churn has a huge profit potential for each online retailer. For online retailers, customer loyalty and regular purchase behaviour is an important part of achieving the sales and margin targets so that maintaining and preserving the customer base. This paper uses the key performance indicators of one big online retail company to examine the current situation in detail and provide methods to reduce the churn. For this purpose, several aspects are used, ranging from the use of tracking software to record customer activities and interests in the online shop itself, to the resulting segmentation into various customer types and the precise calculation of customer lifetime value. These aspects converted to the numerical values are used to train machine learning model with goal to calculate a probable churn score. Additionally, the probability calculation for reordering is used as an input for further marketing activities together with estimation of financial uplift and profit potential.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove:
Visoko učilište Algebra, Zagreb

Profili:

Avatar Url Leo Mršić (autor)

Avatar Url Igor Matić (autor)

Poveznice na cjeloviti tekst rada:

doi

Citiraj ovu publikaciju:

Matić, Igor; Mršić, Leo; Keppler, Joachim
Advanced Analytics Techniques for Customer Activation and Retention in Online Retail // International Conference on Intelligent Computing & Optimization ICO 2020: Intelligent Computing and Optimization / Pandian Vasant, Ivan Zelinka, Gerhard-Wilhelm Weber (ur.).
Zürich: Springer, 2021. str. 1-15 doi:10.1007/978-3-030-68154-8_62
Matić, I., Mršić, L. & Keppler, J. (2021) Advanced Analytics Techniques for Customer Activation and Retention in Online Retail. U: Pandian Vasant, Ivan Zelinka, Gerhard-Wilhelm Weber (ur.) International Conference on Intelligent Computing & Optimization ICO 2020: Intelligent Computing and Optimization. Zürich, Springer, str. 1-15 doi:10.1007/978-3-030-68154-8_62.
@inbook{inbook, author = {Mati\'{c}, Igor and Mr\v{s}i\'{c}, Leo and Keppler, Joachim}, year = {2021}, pages = {1-15}, DOI = {10.1007/978-3-030-68154-8\_62}, keywords = {Online, Retail, Web store, E-commerce, Big data analytics, Machine learning, Churn prediction, Prevention and retention}, doi = {10.1007/978-3-030-68154-8\_62}, isbn = {978-3-030-68154-8}, title = {Advanced Analytics Techniques for Customer Activation and Retention in Online Retail}, keyword = {Online, Retail, Web store, E-commerce, Big data analytics, Machine learning, Churn prediction, Prevention and retention}, publisher = {Springer}, publisherplace = {Z\"{u}rich} }
@inbook{inbook, author = {Mati\'{c}, Igor and Mr\v{s}i\'{c}, Leo and Keppler, Joachim}, year = {2021}, pages = {1-15}, DOI = {10.1007/978-3-030-68154-8\_62}, keywords = {Online, Retail, Web store, E-commerce, Big data analytics, Machine learning, Churn prediction, Prevention and retention}, doi = {10.1007/978-3-030-68154-8\_62}, isbn = {978-3-030-68154-8}, title = {Advanced Analytics Techniques for Customer Activation and Retention in Online Retail}, keyword = {Online, Retail, Web store, E-commerce, Big data analytics, Machine learning, Churn prediction, Prevention and retention}, publisher = {Springer}, publisherplace = {Z\"{u}rich} }

Citati:





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