Author
Abstract
Artificial intelligence (AI) has changed the realm of digital marketing through the use of personalized services which shape consumer behavior towards purchase. In this regard, the current study is conducted to find out the effect of personalized marketing services via AI on consumer purchase behavior, focusing specifically on consumer awareness, application, satisfaction, factors which shape this service as well as the challenges faced by the consumer. In the current study, a quantitative descriptive research approach is used where primary data from 200 online consumers are gathered using a structured questionnaire. Next, descriptive statistical methods including frequency, percentage, mean, standard deviation, and ranking analysis are used to examine the data using Microsoft Excel and SPSS version 26.0. The results show a high level of consumer awareness and application of AI based personalized marketing and majority of them stated that AI recommendations, advertisements and suggestions about products influence their purchase behavior positively. Personalized product recommendations and promotional offers turn out to be the main influencers of buying decisions, whereas most of the participants show their contentment with the use of AI technologies in marketing. On the other hand, privacy threats, data protection problems, risks of the misinterpretation of the information, and lack of transparency are important obstacles on the way to consumer trust. It is concluded from the research that personalized digital marketing based on the use of AI has a very strong potential to promote consumer involvement and intention to buy; however, its successful operation requires the application of ethical principles of AI.
Suggested Citation
Vagmi, 2026.
"Impact of Artificial Intelligence on Consumer Purchase Behaviour : A Study of Personalized Digital Marketing Strategies,"
International Journal of Scientific Research in Artificial Intelligence and Machine Learning, International Journal of Scientific Research in Artificial Intelligence and Machine Learning, vol. 2(4), pages 69-83, July.
Handle:
RePEc:jbo:ijsrml:v2:y2026:i4:id:98
Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML262416
Download full text from publisher
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:jbo:ijsrml:v2:y2026:i4:id:98. 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: Pankaj Sharma (email available below). General contact details of provider: https://ijsraiml.com/home .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.