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ZENODO
Article . 2020
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2020
License: CC BY
Data sources: Datacite
ZENODO
Article . 2020
License: CC BY
Data sources: Datacite
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Cracking Business Growth: Introducing Dynamic Discount Coupon Systems and Successive Discount Models to Attract Users Using Data Analytics

Authors: Durga Prasad Amballa;

Cracking Business Growth: Introducing Dynamic Discount Coupon Systems and Successive Discount Models to Attract Users Using Data Analytics

Abstract

In the competitive landscape of e-commerce, businesses are constantly seeking innovative strategies to attract and retain customers. This paper explores the implementation of dynamic discount coupon systems and successive discount models as powerful tools to drive business growth. By leveraging user purchasing patterns and behaviors, these strategies aim to incentivize both new and existing customers through targeted and time-limited offers. We delve into the psychological aspects of discounts and present synthetic data to support the effectiveness of each model. Additionally, we discuss the technological infrastructure and data analysis techniques required to successfully implement these systems. Our findings suggest that dynamic discount coupons and successive discount models can significantly enhance customer acquisition, retention, and overall business performance when executed strategically.

Keywords

dynamic discounts, purchasing patterns, business growth, successive discounts, user behavior, e-commerce

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green