Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics

Jutamat Jintana,Apichat Sopadang,Sakgasem Ramingwong

doi:10.3390/app10165585

Abstract

The purpose of this research was to create a Matching Consignees/Shippers Recommendation System (MCSRS). We used the association rule to identify product associations, the clustering technique to group shippers and consignees according to behaviors when receiving goods from similar shipper groups, and the decision tree to identify possible matches between shippers and consignees. Finally, Monte Carlo simulation was used to estimate potential revenue. The case study is a courier company in Thailand. The results showed that garment products and clothes were the products with the highest association. Shippers and consignees of these products were segmented according to recency, frequency, monetary factors, number of customers, number of product items, weight, and day. Three rules are proposed that enabled the assignment of 8 consignees to 56 shippers with an estimated increase in revenue by 36%. This approach helps decision-makers to develop an effective cost-saving new marketing, inclusive strategy quickly.

Highlights

Recommendation systems (RS) have increasingly been employed owing to technological advancements in understanding individual customer behavior resulting in increased customer satisfaction [1,2,3]
This study explores whether Data analytics (DA) can be used to identify association, raising the following research questions: RQ1 Which products/items do consignees most often receive together? Which items are likely to be recommended?
DA was performed using a of 461,708 sales transactions betweenbetween

Summary

Introduction

Recommendation systems (RS) have increasingly been employed owing to technological advancements in understanding individual customer behavior resulting in increased customer satisfaction [1,2,3]. In e-commerce, this commonly applies to product recommendations. An RS suggests products that customers will likely prefer by considering the relationship between a customer’s purchase history and the product’s review rating [4,5]. Amazon has developed an RS by identifying customers who have purchased and rated items. Based on the collaborative filtering algorithm and association rule, Amazon has increased its revenue by 30% [1]. The video rental and streaming service, held a competition to improve its RS, called Cinematch

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Conclusion