IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/uzsaf.html
   My bibliography  Save this paper

“Effect AI powered Email Automation”: An Analysis of Email Marketing Automation

Author

Listed:
  • Froolik, Alderd J.

    (Quest Sales & Marketing Automations BV)

Abstract

This research study focuses on investigating the effectiveness of AI powered email automation in enhancing customer retention, increasing sales, and improving customer satisfaction. Email automation has emerged as a powerful marketing tool, allowing businesses to automate and personalize their email communication with customers. The objective of this study is to examine the impact of implementing email automation on the real live Shopify webshop PeponiXL.nl which is opening up webshops in different countries (Portugal, Italy, Austria, Belgium, Spain, France and Germany). The boundary for this expansion is the language barrier for each country the webshop is expanding to. Due to the lack of multilingual personnel, the rising costs, and the declining margins the study will focus on possibilities of a Large Language Model (ChatGPT) to overcome these issues. The hypotheses are that ChatGPT will not only speed up the replies of emails but also provide answers within the guidelines of the webshop and maintaining the branding in each country, developing global awareness and brand at scale without putting additional stress on the workforce.

Suggested Citation

  • Froolik, Alderd J., 2024. "“Effect AI powered Email Automation”: An Analysis of Email Marketing Automation," OSF Preprints uzsaf, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:uzsaf
    DOI: 10.31219/osf.io/uzsaf
    as

    Download full text from publisher

    File URL: https://osf.io/download/66cdd7baec5933c893180a6e/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/uzsaf?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:uzsaf. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.