Analytical Study of Automatic Speech Recognition and Linked Profile as a Tool for Effective Advertising
Authors
https://doi.org/10.15415/jtmge/2023.142002
Voice assistants, Voice commerce, Linked profiles, Consumer buying behavior
Abstract
Background: Automatic speech recognition, more commonly known as voice search, and linked profiles have emerged as powerful tools for advertising. Voice search allows users to perform searches using their voice instead of typing, while linked profiles enable users to connect their various online accounts to create a comprehensive profile. These technologies offer new opportunities for advertisers to reach their target audience in a more personalized and effective way.
Purpose: This study sheds light on the potential impact that voice assistants have on consumer brands. It also intends to study the impact of LinkedIn profiles as a key effective advertising tool.
Methods: The data was collected from a sample size of 100 customers. It was analyzed using correlation and regression analysis.
Results: The results of the study reveal that voice-linked search is fast becoming a focal point in marketing because of its swift adoption and disruptive potential in creating buying dynamics.
Conclusions: Correlation analysis helps conclude that there is a moderately positive correlation between age and usage patterns of voice search. The results suggest that effective advertising through voice search and linked profiles requires a deep understanding of the target audience, their interests, and their behavior. Advertisers must also develop creative and engaging ad content that aligns with the user’s intent and preferences. Additionally, they must carefully consider the context in which the ad is presented, as voice-based advertising may feel intrusive if not executed properly.
References
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How to Cite
Ruchika Jeswal , Ruchi Jain and Shreya Tripathi. Analytical Study of Automatic Speech Recognition and Linked Profile as a Tool for Effective Advertising. J.Technol. Manag. Grow. Econ.. 2023, 14, 9–16Current Issue
| Periodicity | Biannually |
| Issue-1 | May |
| Issue-2 | November |
| ISSN Print | 0976-545X |
| ISSN Online | 2456-3226 |
| RNI No. | CHAENG/2013/50088 |
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