Identifying customer priority for new products in target marketing: Using RFM model and TextRank

Seongbeom Hwang,Yuna Lee

doi:10.21511/im.17(2).2021.12

Abstract

Target marketing is a key strategy used to increase the revenue. Among many methods that identify prospective customers, the recency, frequency, monetary value (RFM) model is considered the most accurate. However, no RFM study has focused on prospects for new product launches. This study addresses this gap by using website access data to identify prospects for new products, thereby extending RFM models to include website-specific weights. An RF model, built using frequency and recency information from website access data of customers, and an RwF model, built by adding website weights to frequency of access, were developed. A TextRank algorithm was used to analyze weights for each website based on the access frequency, thus defining the weights in the RwF model. South Korean mobile users’ website access data between May 1 and July 31, 2020 were used to validate the models. Through a significant lift curve, the results indicate that the models are highly effective in prioritizing customers for target marketing of new products. In particular, the RwF model, reflecting website-specific weights, showed a customer response rate of more than 30% among the top 10% customers. The findings extend the RFM literature beyond purchase history and enable practitioners to find target customers without a purchase history.

Highlights

The statistics of the prospective customer values calculated with the RF and RwF models is shown in Table 2 and Table 3
This study proposed an RwF model with utility weights by website and confirmed that the actual long message service (LMS) campaign, which was based on the RwF model, showed a higher lift value than the RF model
The primary output of the proposed procedure is a decision-making process that enables an entity entering a new business to identify customers to be targeted with new offerings

Summary

INTRODUCTION

With the development of the Internet and the growth of online market, online consumption has increased, and recently COVID-19 has accelerated this trend (Bhatti et al, 2020) In this environment consumers can access and purchase products from multiple online stores. Analyzing their Internet access history allows to deduce certain items of interest and measure the likelihood of customers purchasing those items Using this approach, companies that lack customers’ purchasing histories can still efficiently identify marketing targets to launch their new products. It is important to identify a customer base with a high probability of consumption to increase revenue During this process, companies can use various types of data. If the identification of targets and related activities are delayed compared to its competitors, this could result in a significant loss for the company

LITERATURE REVIEW

Evaluation

Data collection

Prospective customer value by models

DISCUSSION

CONCLUSIONS AND FUTURE WORK