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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63833
Volume:
10 |
5 |
May 2026
Pages:
1-9
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
School Of Computational Sciences
JSPM UNIVERSITY
School Of Computational Sciences
JSPM UNIVERSITY
On-demand shopping sites have seen a lot of growth lately because more consumers are wanting their items delivered to them very quickly. Despite the high demand for fast delivery, there are still a lot of problems facing on-demand shopping platforms such as: high user churn (loss of customers), depen- dence on discounts, and high operational costs. For this paper, I analyzed consumer behaviours, identified which consumers are likely to churn, created meaningful user segments and estimated average order values (AOV) through various machine learning techniques using survey data from 603 consumers. Data used to create and train the machine learning models consisted of different variables such as: how often the consumer uses a delivery service; how urgently they are ordering; if delivery and order services are important to a customer; customer satisfaction; and AOV. Logistic regression and decision trees were used to identify consumers that will likely churn, while K-Means clustering provided meaningful segments of consumers within the data set. Linear regression was used to estimate AOV for targeted promotions. Findings from the above analysis showed that delivery delays had a negative effect on customer satisfaction, a majority of consumers were influenced by discounts, and weekly consumers had the highest AOV on average. The above findings have led to some recommendations that would improve on- demand delivery service providers such as: optimising delivery services, ensuring product availability during peak hours, and targeting consumers that generate AOVs for premium pricing. This research contributes to a better understanding of consumer behaviour in the field for academics and can provide practical solutions for the business side of the on-demand marketplace.
KEYWORDS
The findings from the current study indicate that this company has a solid core demand but also has significant room for improvement on the frequency of use by current users. It is critical for this organization to ensure that morning delivery schedules are stabilized, allowing for good customer satisfaction without the overwhelming negative feelings pro- duced when customers experience delays. In addition, a loyalty program that targets ”Occasional” users in order to turn them into ”Weekly” users will be very effective due to the amount of discounting currently required. Finally, technical changes must be made in order to be able to retrain predictive models using balanced data; otherwise, the ability to forecast and minimize customer churn will be nothing more than a theory. Aligning operational capacity with morning volume peaks and improving predictive analytics will allow this platform to move from wide-ranging occasional usage to long-term, high-value customer loyalty.
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Publication Date
5/23/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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