Improving customer valuation models with word-of-mouth: directions and results

Authors

  • Ákos András Nagy Pécsi Tudományegyetem Közgazdaságtudományi Kar
  • Ildikó Kemény Budapesti Corvinus Egyetem Gazdálkodástudományi Kar
  • Krisztián Szűcs Pécsi Tudományegyetem Közgazdaságtudományi Kar
  • Judit Simon Budapesti Corvinus Egyetem Gazdálkodástudományi Kar

Keywords:

customer valuation, segmentation, word-of-mouth, opinion leadership, RFM

Abstract

AIM OF THE PAPER
In our study we have focused on the incorporation attempts of word-of-mouth into customer valuation and examined the moderating role of the RFM value and its dimensions on a previously established model.

METHODOLOGY
We have implied covariance based structural equitation modelling in AMOS. In order to test the moderating effect of the R, F, M dimensions and the RFM value first and foremost we had to calculate them. We conducted an invariance analysis in order to be able to perform several Multi-Group comparisons based on the low and high levels of the different value components.

MOST IMPORTANT RESULTS
We have divided the respondents into two groups - lower and higher scores based on their R (recency), F (frequency), M (monetary) as well as RFM values and highlighted differences among them. However in order to find the exact causes for these discrepancies further research is needed.

RECOMMENDATIONS
The results are targeted toward those managers who operate online stores and make choices based on RFM scores. As the most important aspect we can highlight the positive relationship of quality assessment to customer satisfaction, which is stronger among those who purchase less frequently and/or in higher amounts, besides have lower RFM scores.

Downloads

Published

2017-08-01

How to Cite

Nagy, Ákos A., Kemény, I., Szűcs, K. and Simon, J. (2017) “Improving customer valuation models with word-of-mouth: directions and results”, The Hungarian Journal of Marketing and Management, 51(EMOK klsz), pp. 14–27. Available at: https://journals.lib.pte.hu/index.php/mm/article/view/790 (Accessed: 17 May 2024).

Most read articles by the same author(s)

1 2 > >>