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

  • Received April 19, 2021;
    Accepted June 10, 2021;
    Published June 11, 2021
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/im.17(2).2021.12
  • Article Info
    Volume 17 2021, Issue #2, pp. 125-136
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This work is licensed under a Creative Commons Attribution 4.0 International License

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.

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    • Figure 1. Conceptual framework to identify prospective customers
    • Figure 2. Example of the difference in the website pages accessed by customers
    • Figure 3. Cumulative lift chart of the RwF and RF models (R = recency; w = utility-weighted frequency; F = frequency)
    • Figure 4. Cumulative distribution of website weight
    • Table 1. Partial website access data
    • Table 2. RF value statistics
    • Table 3. RwF values statistics
    • Table 4. Performance results of the models
    • Conceptualization
      Seongbeom Hwang, Yuna Lee
    • Data curation
      Seongbeom Hwang
    • Formal Analysis
      Seongbeom Hwang
    • Investigation
      Seongbeom Hwang, Yuna Lee
    • Methodology
      Seongbeom Hwang
    • Software
      Seongbeom Hwang
    • Supervision
      Seongbeom Hwang
    • Validation
      Seongbeom Hwang
    • Visualization
      Seongbeom Hwang, Yuna Lee
    • Writing – original draft
      Seongbeom Hwang
    • Writing – review & editing
      Seongbeom Hwang, Yuna Lee