Forecasting the Effects of In-Store Marketing on Conversion Rates for Online Shops

Holger Fink and Yvonne Graf
Additional contact information
Holger Fink: Department of Computer Science and Mathematics, Munich University of Applied Sciences, Lothstrasse 64, 80335 Munich, Germany
Yvonne Graf: Chair of Strategic Industrial Marketing, Institute of Business Administration, University of Regensburg, Universitätsstrasse 31, 93053 Regensburg, Germany

Forecasting, 2018, vol. 1, issue 1, 1-20

Abstract: As webstores usually face the issue of low conversion rates, finding ways to effectively increase them is of special interest to researchers and practitioners alike. However, to the best of our knowledge, no one has yet empirically investigated the usefulness of various in-webstore marketing tools like coupons or different types of product recommendations. By analysing clickstream data for a shoe and a bed online store, we are contributing to closing this gap. In particular, we use our present data to build more general hypotheses on how such purchasing incentives might function and on how they could be used in practice.

Keywords: online shop; purchase incentive; conversion rate; coupon; clickstream (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-9394/1/1/6/pdf (application/pdf)
https://www.mdpi.com/2571-9394/1/1/6/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:1:y:2018:i:1:p:6-89:d:169664

Access Statistics for this article

Forecasting is currently edited by Ms. Joss Chen

More articles in Forecasting from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().