Hyperautomation in Super Shop Using Machine Learning

Ahmed, Shuvro; Karmoker, Joy; Mojumder, Rajesh; Rahman, Md. Mahmudur; Alam, Md. Golam Rabiul; Reza, Md Tanzim

doi:10.3390/engproc2023039063

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…MBA and customer segmentation are prominent approaches to extracting insights from customer purchase behaviours [7]. However, the current body of literature reveals a lack of agreement regarding the most effective approaches and limitations related to the ability to apply findings to a broader population [8][9][10][11][12][13][14][15].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Multiple studies propose novel approaches to overcome existing constraints. Ahmed et al [11] introduced the product reduction technique for MBA, while concerns were raised over its scalability for bigger datasets. The model suggested by Xiahou and Harada [12] for predicting online sales is limited in its ability to react to changing customer behaviour due to its lack of integration with real-time data streams.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimizing Decision-Making Through Customer-Centric Market Basket Analysis

Hoque,

Islam,

Mohtasim

2024

JOSA

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In the realm of understanding consumer purchasing behaviors and refining decision-making across diverse sectors, Market Basket Analysis (MBA) emerges as a pivotal technique. Traditional algorithms, such as Apriori and Frequent Pattern Growth (FP-Growth), face challenges with computational efficiency, particularly under low minimal support settings, which precipitates an excess of weak association rules. This study introduces an innovative approach, termed Customer-Centric (CC)-MBA, which enhances the identification of robust association rules through the integration of consumer segmentation. By employing Recency, Frequency, and Monetary (RFM) analysis coupled with K-means clustering, customers are categorized based on their purchasing patterns, focusing on segments of substantial value. This targeted approach yields association rules that are not only more relevant but also more actionable compared to those derived from conventional MBA methodologies. The superiority of CC-MBA is demonstrated through its ability to discern more significant association rules, as evidenced by enhanced metrics of support and confidence. Additionally, the effectiveness of CC-MBA is further evaluated using lift and conviction metrics, which respectively measure the observed co-occurrence ratio to that expected by chance and the strength of association rules beyond random occurrences. The application of CC-MBA not only streamlines the analytical process by reducing computational demands but also provides more nuanced insights by prioritizing high-value customer segments. The practical implications of these findings are manifold; businesses can leverage this refined understanding to improve product positioning, devise targeted promotions, and tailor marketing strategies, thereby augmenting consumer satisfaction and facilitating revenue growth.

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