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Customer Wallet Share Estimation for Manufacturers Based on Transaction Data

  • Xiang LiEmail author
  • Ali Shemshadi
  • Łukasz P. Olech
  • Zbigniew Michalewicz
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1127)

Abstract

The value of customers for any business cannot be over-emphasised, and it is crucial for companies to develop a good understanding of their customer base. One of the most important pieces of information is to estimate the share of wallet for each individual customer. In the literature a related concept is often referred to as customer equity that provides aggregated measures such as the business market share. The current trend in personalising marketing campaigns have led to more granular estimation of wallet share, than the entire customer base or aggregated segments of customers. The current trend in personalising marketing and business strategies have lead to more granular estimation of wallet shares than the entire customer base or aggregated segments of customers. Existing research in this area requires access to additional information about customers, often collected via various surveys. However, in many real-world scenarios, there are circumstances where survey data are unavailable or unreliable. In this paper, we present a new customer wallet share estimation approach. In the proposed approach, a predictive model based on decision trees facilitates an accurate estimation of wallet shares for customers relying only on transaction data. We have evaluated our approach using real-world datasets from two businesses from different industries.

Keywords

Wallet share estimation Customer equity Random Forest Real-world case study 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Xiang Li
    • 1
    • 2
    Email author
  • Ali Shemshadi
    • 2
  • Łukasz P. Olech
    • 2
    • 3
  • Zbigniew Michalewicz
    • 1
    • 2
    • 4
    • 5
  1. 1.School of Computer ScienceUniversity of AdelaideAdelaideAustralia
  2. 2.Complexica Pty Ltd.West LakesAustralia
  3. 3.Wroclaw University of Science and TechnologyWroclawPoland
  4. 4.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  5. 5.Polish-Japanese Academy of Information TechnologyWarsawPoland

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