Two-sided platform customer classification system based on network DEA: an application to an on-demand freight platform

Abstract

Two-sided platforms include two parts of users, customers and suppliers, which are the key strategic resources in two-sided platforms. Through Customer Intelligence Classification (CIC) management provided in this study, a platform can distinguish different values of customers to allocate resources efficiently and maximise customer resource value and enterprise investment returns. Meanwhile platform can also distinguish collusive drivers through CIC. Network Data Envelopment Analysis (NDEA) is applied in this research to open the black box of a two-sided platform operation system by dividing it into two sub-processes: Marketing and Service (MS) sub-process and Value-Creating (VC) sub-process. Four categories of customers are classified through performance evaluated by NDEA from data of an on-demand freight platform Z from March to August in 2018. In addition, six influencing factors are discovered through Panel Regression Model. The main conclusions are: (1) Customer with high marketing service valuation and low value-creating valuation, needs proper operation methods to promote their order quality. (2) Customer with low marketing service valuation and high value-creating valuation, needs to take attention to collusion between customers and driver. (3) Shipping fee, client number, driver number and coupons per order should become customers’ Key Performance Indicators to evaluate their potential.

KEYWORDS:

• Customer Intelligence Classification
• network data envelopment analysis
• panel regression analysis
• two-sided platform

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

Data not available due to the nature of the research, due to commercial supporting data is not available.

Notes

1 Data source: Wuhan Municipal Commerce Bureau http:// sw.wuhan.gov.cn/xwdt/mtbd/202202/t20220221_1925901 .shtml.

2 Data source: QYR Research https://xueqiu.com/4184148841/235519440.

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 72301074, 72101192].