# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 1 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]. For example, the popular e-commerce site 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]. Netflix, the video rental and streaming service, held a competition to improve its RS, called Cinematch. In 2009, a team combined 107 recommendation algorithms resulting in a single prediction, improving predictive accuracy. LinkedIn and Facebook provide recommendations for people you might know, jobs you might like, groups you might want to follow, or companies in which you may be interested. LinkedIn uses Apache Hadoop to build its specialized collaborative-filtering capabilities [2]. This is due to technological advancements that allow data to be collected, stored, and processed effectively, resulting in proactive data that enhances business opportunities, captures new markets and customers, and increases margins. Big data, artificial intelligence, and the Internet of things are examples of how datafication is applied in today's businesses with many algorithmic approaches available for recommendation engines depending on the type of organization [6]. Data analytics (DA) techniques have been successfully applied in RS to predict user preferences. Suitable algorithms were applied to analyze different characteristics of customer data [3]. DA used in RS includes association rules, clustering techniques, and classification techniques [7], which are used if there are suitable input data [8]. DA algorithms find consumer profiles and purchase patterns in corporate databases that can be used for effective marketing and business decision making. In e-commerce, these procedures can be applied to develop an RS, but they have been extended to deal with specific problems inherent in this kind of application [5]. Likewise, an established database system can apply DA to an RS in other industries, including tourism, hotels, banks, or entertainment [4]. A courier is a company or person responsible for performing shipping services [9]. Courier businesses became a key service industry playing an important role in industrialized economies due to the globalization of e-commerce. The competition in price and quality forces companies to adjust their strategies for new competitive advantages. There are opportunities in courier businesses to utilize data and information of customers and goods which are millions of transactions yearly. The time of delivery, origin, destination, size, weight, and type of goods are recorded and processed around the world [10]. This transaction data can generate at each instance of service activity. The data contain vast numbers of customer profile (e.g., shipper and receiver names, addresses, contacts) and sale transaction details (e.g., date, time, parcel weight, and freight charges). Such profiles analyze a set of behavior-based characteristics, such as shipment preferences, showing what the customer is actually doing. This database can allow businesses to gain information for decision-making as it identifies target customers, ascertains the needs of these customers, and then specifies how the product satisfies these needs. However, it is essential to determine if this information is used effectively or offers operational improvement by developing insights about customers and analyzing new business models. The concrete implementation of RS in the courier business has thus far been lacking because of the limitations of the database structure.