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Strategic Rearrangement of Retail Shelf Space Allocations: Using Data Insights to Encourage Impulse Buying

SSRN, ISSN: 1556-5068
2022
  • 0
    Citations
  • 611
    Usage
  • 1
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Usage
    611
    • Abstract Views
      459
    • Downloads
      152
  • Captures
    1
  • Ratings
    • Download Rank
      390,681

Article Description

Brick-and-mortar retailers are struggling to compete with online retailers, who increasingly use customer-generated data to provide highly customized experiences for their patrons to drive impulse purchases. Although brick-and-mortar retailers cannot offer such customization, they must identify innovative ways to transform their own point-of-sale transaction data into increased customer impulse buying. Using association rule mining and optimization-based heuristics, we propose a dynamic shelf allocation-relocation scheme for rearranging storewide product allocations over time to maximize impulse buying behavior. The proposed method rearranges items based on customer behavior with the current arrangement. The method applies insights from association rule mining to group highly affine and profitable product pairs, optimize the assignment of departments to store aisles, and determine the optimal within-aisle space allocations for the products of each department. This strategic rearrangement technique consistently outperforms visual shelf space rearrangement and, in many instances, exceeds the profit potential of a more traditional unchanged (one-time optimal) shelf space arrangement, depending on the nature of a retailer’s target market. Our results highlight the importance of a retailer selecting the most appropriate shelf space rearrangement strategy that fits the characteristics of its customers, especially their discretionary income level and their familiarity with the store layout.

Bibliographic Details

Gihan S. Edirisinghe; Charles Lee Munson

Elsevier BV

Multidisciplinary; Retailing; Shelf space allocation; Data Mining; Multi-level association rules; Nonlinear programming; Heuristics

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