# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 4 Case Study  
### The Case and Data Collection  
This research used the biggest bus company in northern Thailand, the G Company, for its case study. Over the years, the company has faced strong competition from other types of public transportation, such as taxis, trains, and especially low-cost airlines. The boom in low-cost airlines has affected many market segments owing to competitive prices and promotions that impact customer numbers and profits. To address this crisis, the company has tried to develop two new business units. The first is a charter service, a non-regular route public carrier. The second is a courier service that uses the available space under routed buses. Since 2010, the G Company has provided a port-to-port courier service along its bus routes, using 115 service ports covering 22 provinces from northern to southern  
Figure 2: Training and testing data for decision tree learning.  
Thailand. It provides a courier service as well as packing, short-term warehousing, and door-to-door services in some areas.  
The company's courier service has become an important business with several types of customers, exhibiting a 17.03% revenue growth rate in 2018. The strength of this business is derived from the high frequency of bus travel and low cost, given that the service capitalizes on already available space under passenger service buses. The company has a customer database, providing scope for development of further business opportunities. All sale activities are recorded in the transaction database. However, this is not used for any further analysis at present.  
Sales transaction data from 2018 were generated and collected in an Excel spreadsheet. Courier revenue during this year totaled 79 million THB. Attributes collected in the database included shippers'/consignees' names, dates, times, product group names, product category names, start/destination stations, quantities, weights, freight charges, membership application dates, and payment types (Table 2). Products were originally classified into 66 categories (items), then further aggregated into 13 general groups (Figure 3). The products most frequently transported were food and drink (23%); clothes, garments, and accessories (21%); agricultural products (16%); and furniture, decor/kitchenware (10%), respectively.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Attributes** & **Data Type** & **Description and Data Examples** \\ \hline Shipper name & Text & Name of shipper e.g., Mr. [PERSON] \\ Consignee name & Text & Name of consignee e.g., Mr. [PERSON] \\ Date & Numeric & Transaction date e.g., 12/03/2018 \\ Time & Numeric & Transaction time e.g., 10:31:44 AM \\ Products group name & Nominal & Name of product group e.g., food and drink, clothes, garments, \\ Products category name & Nominal & Name of product category e.g., perishable foods, clothes \\ Start station & Nominal & Name of start station e.g., Chiang Mai \\ Destination station & Nominal & Name of destination station e.g., Chiang Rai \\ Quantity & Numeric & Quantity of cargo per transaction \\ Weight & Numeric & Weight of cargo per transaction (kg) e.g., 15 kg. \\ Freight charges & Numeric & Freight charges per transaction (THB) e.g., 695 THB. \\ Membership application date & Numeric & Date of member registration (first transaction) e.g., 05/12/2017 \\ Employee name & Text & Name of sale officer who recorded the transaction \\ Payment type & Nominal & Payment type e.g., cash, debit, agent \\ \hline \hline \end{tabular}
\begin{tabular}{l|l|l|l} \hline
**1. Computer, communication** & **\& Food and drink** & **7. Electrical machines** & **11. Sport equipment and** \\
**tools and electronic equipment** & Scasoning & Audio and equipment & **Musical instrument** \\ \hline Copy machines and your parts & Office & General electrical machine & Game player and equipment \\ \hline Discs, CD ROMs & Snacks & Electrical equipment & Musical instrument \\ \hline Mobile tools, SIM cards, money top-up cards, related goods & Mik & **8. Industrial products and** & **12. Handeraf