<?xml version="1.0" encoding="UTF-8"?>
<article_metadata generated_at="2026-06-06T11:14:10+00:00">
  <journal>
    <title>Journal of Business and Retail Management Research</title>
    <acronym>JBRMR</acronym>
    <issn_print></issn_print>
    <issn_online>2056-6271</issn_online>
    <doi_prefix>https://doi.org/10.24052/JBRMR/</doi_prefix>
  </journal>
  <article>
    <id>154</id>
    <title>Markdown policies for optimizing revenue, towards optimal pricing in retail</title>
    <abstract>Due to inadequate initial allocation of supply, retail organizations are usually confronted with markdowns during a sales season. In this article the apparent paradox between the traditional Law of Demand and retailing pricing is solved. Through the use of Survival Analysis, as is commonly used in Biomedical Research, a framework is developed for determining optimal price paths in a retail context. When thought of in analogy to birth and death processes, sell through curves can be seen as life cycles of retail goods and can be applied in Revenue Optimization. We show that two significant events in calendar time exist: the moment a probable markdown occurs and the instance the underlying good is sold. Next, we apply convolution to define the markdown point in calendar time and estimate the price elasticity with an exhaustive search yielding the markdown moment that optimizes revenue</abstract>
    <doi></doi>
    <url>https://mail.ijhem.com/details&amp;cid=154</url>
    <pdf_url>https://mail.ijhem.com/cdn/article_file/i-17_c-154.pdf</pdf_url>
    <volume>Volume 08</volume>
    <issue>Issue 1</issue>
    <issue_id>17</issue_id>
    <issue_published_month>2013-10-01</issue_published_month>
    <published_date>2014-06-02</published_date>
    <online_first_status>no</online_first_status>
    <online_first_date></online_first_date>
    <history>
      <received></received>
      <revised></revised>
      <accepted></accepted>
    </history>
    <keywords>
      <keyword>Survival analysis</keyword>
      <keyword>sell through</keyword>
      <keyword>regression</keyword>
      <keyword>convolution</keyword>
      <keyword>markdowns</keyword>
      <keyword>optimal revenue.</keyword>
    </keywords>
    <declarations>
      <funding></funding>
      <conflict_of_interest></conflict_of_interest>
      <data_availability></data_availability>
      <author_contributions></author_contributions>
    </declarations>
    <publication_notice>
      <type>none</type>
      <text></text>
    </publication_notice>
    <metrics>
      <views>559845</views>
      <downloads>11</downloads>
      <citations>0</citations>
    </metrics>
    <authors>
      <author>
        <name>Rudi Meijer</name>
        <organization>VU University Amsterdam Dep. of Mathematics/Bijenkorf Departmental Stores</organization>
        <country></country>
      </author>
      <author>
        <name>Michael Pieffers</name>
        <organization>VU University Amsterdam Dep. of Mathematics/Bijenkorf Departmental Stores</organization>
        <country></country>
      </author>
      <author>
        <name>Sandjai Bhulai</name>
        <organization>VU University Amsterdam, Department of Mathematics</organization>
        <country></country>
      </author>
    </authors>
    <supplementary_materials/>
  </article>
</article_metadata>
