It is no doubt that now a day’s Artificial intelligence (AI) is anemergent technology in the digital marketing industry. Commercial Artificial Intelligence (AI) is an important topic for research. Artificial intelligence is growing quickly more sensitive and popular in the digital marketing field due to the capacity of advertisers to employ it efficiently. In order to provide the capability of the highest level of originality and efficiency in digital marketing ground, artificial intelligence techniques are more fruitful for customers, marketers and, retailers. This paper depicts the impact of artificial intelligence techniques on advertising on a diminutive and average scale in the digital marketing field. Ad targeting, predictive analysis, voice search are different artificial intelligence techniques for digital marketing purposes throughout the consumer life cycle. The topic of Artificial Intelligence (AI) in digital marketing itself can be outsized enough to verifyeliminating the area of research into precise application and application frameworks.However, our objective in this paper is to provide a comparative analysis of the digital marketing field using artificial intelligence techniques and to propose some specific guidelines based on different parameters, tools, and frameworks.Through this paper, we have observed thelast five years (2016-2021) research trends which reflect that out of different parameters, customer relationship and promotional activity are the most widely used parameter in the digital marketing field. Another significant point is, most of the research identifies that artificial intelligence is a compulsory technique used in the modern digital marketing field. Some data collection and data analysis tools are also being used in this field using artificial intelligence techniques. Our research methods try to get a complete view on artificial intelligence (AI) Marketing were multifaceted but were very focused on secondary research sources. Finally in this paper, we propose some recommendationsthat will offer better Artificial Intelligence (AI) implementation in the digital marketing field.

1.
L.
Ma
and
B.
Sun
, “
Machine learning and AI in marketing – Connecting computing power to human insights
,”
Int. J. Res. Mark.
, vol.
37
, no.
3
, pp.
481
504
,
2020
, DOI: .
2.
U.
Kose
and
S.
Sert
, “
Intelligent Content Marketing with Artificial Intelligence
,”
Int. Conf. Sci. Coop. Futur.
, no.
September
, pp.
837
843
,
2016
.
3.
J.
Lies
, “
Marketing Intelligence and Big Data: Digital Marketing Techniques on their Way to Becoming Social Engineering Techniques in Marketing
,”
Int. J. Interact. Multimed. Artif. Intell.
, vol.
5
, no.
5, p. 134
,
2019
, DOI: .
4.
Y. K.
Dwivedi
 et al., “
Setting the future of digital and social media marketing research: Perspectives andresearch propositions
,”
Int. J. Inf. Manage.
, vol.
59
, no.
June, p. 102168
,
2021
, DOI: .
5.
D.
Dumitriu
and
M. A. M.
Popescu
, “
Artificial intelligence solutions for digital marketing
,”
Procedia Manuf.
, vol.
46
, no.
2019
, pp.
630
636
,
2020
, DOI: .
6.
A.
Murgai
, “
Transforming Digital Marketing with Artificial Intelligence
,”
Int. J. Latest Technol. Eng. Manag. Appl. Sci.
, vol.
VII
, no.
Iv
, pp.
259
262
,
2018
.
7.
F. M.
Pangkey
,
L. M.
Furkan
, and
L. E. H.
Mulyono
, “
Pengaruh Artificial Intelligence dan Digital Marketing terhadap Minat Beli Konsumen
,”
Jmm Unram - Master Manag. J.
, vol.
8
, no.
3
, pp.
258
269
,
2019
, DOI: .
8.
M. H.
Huang
and
R. T.
Rust
, “
A strategic framework for artificial intelligence in marketing
,”
J. Acad. Mark. Sci.
, vol.
49
, no.
1
, pp.
30
50
,
2021
, DOI: .
9.
P. K.
Theodoridis
and
D. C.
Gkikas
, “
How Artificial Intelligence Affects Digital Marketing,” no. May
, pp.
1319
1327
,
2019
, DOI: .
10.
J.
Hu
,
B.
Liu
, and
H.
Peng
, “
Role of AI for application of marketing teaching -A research perspective
,”
J. Intell. Fuzzy Syst.
, vol.
40
, no.
2
, pp.
3711
3719
,
2021
, DOI: .
11.
J.
Yin
and
X.
Qiu
, “
Ai technology and online purchase intention: Structural equation model based on perceived value
,”
Sustain.
, vol.
13
, no.
10
,
2021
, DOI: .
12.
A. E.
Fayed
, “
Artificial Intelligence for marketing plan: the case for e-marketing companies
,”
Mark. Manag. Innov.
, vol.
6718
, no.
1
, pp.
81
95
,
2021
, DOI: .
13.
U.
Arsenijevic
and
M.
Jovic
, “
Artificial Intelligence Marketing: Chatbots
,”
Proc. - 2019 Int. Conf. Artif. Intell. Appl. Innov. IC-AIAI 2019
, pp.
19
22
,
2019
, DOI: .
14.
M.
Mustak
,
J.
Salminen
,
L.
Plé
, and
J.
Wirtz
, “
Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda
,”
J. Bus. Res.
, vol.
124
, no.
January
, pp.
389
404
,
2021
, DOI: .
15.
D.
Leone
,
F.
Schiavone
,
F. P.
Appio
, and
B.
Chiao
, “
How does artificial intelligence enable and enhance value co-creation in industrial markets? An exploratory case study in the healthcare ecosystem
,”
J. Bus. Res.
, vol.
129
, no.
March 2019
, pp.
849
859
,
2021
, DOI: .
16.
B. G. C.
Dellaert
 et al., “
Consumer decisions with artificially intelligent voice assistants
,”
Mark. Lett.
, vol.
31
, no.
4
, pp.
335
347
,
2020
, DOI: .
17.
J.
Holmi
, “
Förnamn Efternamn Artificial Intelligence in Digital Marketing Now and in the future
,”
2021
.
18.
M. Z.
Shahid
and
G.
Li
, “
Impact of Artificial Intelligence in Marketing: A Perspective of Marketing Professionals of Pakistan
,”
Glob. J. Manag. Bus. Res. E Mark.
, vol.
19
, no.
2
, pp.
26
33
,
2019
, [Online]. Available: https://www.journalofbusiness.org/index.php/GJMBR/article/view/2704.
19.
T.
Thiraviyam
, “
Artificial Intelligence Marketing
,”
Int. J. Recent Res. Asp.
, vol.
19
, no.
4
, pp.
449
452
, 2018, [Online]. Available: https://albert.ai/.
20.
G.
Overgoor
,
M.
Chica
,
W.
Rand
, and
A.
Weishampel
, “
Letting the computers take over: Using Ai to solve marketing problems
,”
Calif. Manage. Rev.
, vol.
61
, no.
4
, pp.
156
185
,
2019
, DOI: .
21.
H. H.
Syed
, “
ROLE OF ARTIFICIAL INTELLIGENCE IN DIGITAL Abstract
:,” no. May,
2021
.
22.
J.
Tiautrakul
and
J.
Jindakul
, “
The Artificial Intelligence (AI) with the Future of Digital Marketing
,”
SSRN Electron. J.
,
2019
, DOI: .
23.
A.
Rahman
,
S. P.
Paul
,
M.
Das
,
M.
Hossain
, and
R.
Haque
, “
Convolutional Neural Networks based multi-object recognition from a RGB image
,”
2019 Int. Conf. Electr. Comput. Commun. Eng.
, pp.
1
6
,
2019
.
24.
S.
Rashid
and
S. P.
Paul
, “
Proposed Methods of IP Spoofing Detection & Prevention
,” vol.
2
, no.
8
,
2013
.
25.
S. P.
Paul
and
S.
Saha
, “
Empirical Study on Big Data Analysis for Supply Chain Management
,”
Insist
, vol.
4
, no.
2
, pp.
236
239
,
2020
, DOI: .
26.
M. N.
Alam
and
S. P.
Paul
, “Security Engineering towards Building a Secure Software,”
vol. 81
,
no. 6
, pp.
32
37
,
2013
.
27.
A.
Zaslavsky
and
D.
Georgakopoulos
, “
Internet of Things: Challenges and State-of-the-Art Solutions in Internet-Scale Sensor Information Management and Mobile Analytics
,”
Proc. - IEEE Int. Conf. Mob. Data Manag.
, vol.
2
, no.
Section II
, pp.
3
6
,
2015
, DOI: .
28.
G.
D’Aniello
,
M.
Gaeta
, and
T. P.
Hong
, “
Effective Quality-Aware Sensor Data Management
,”
IEEE Trans. Emerg. Top. Comput. Intell.
, vol.
2
, no.
1
, pp.
65
77
,
2018
, DOI: .
29.
W. S.
Networks
and
I.
Application
, “
An Intelligent Opportunistic Routing Algorithm for
.”
30.
S.
Rana
,
A. N.
Bahar
,
N.
Islam
, and
J.
Islam
, “
Fuzzy Based Energy Efficient Multiple Cluster Head Selection Routing Protocol for Wireless Sensor Networks
,”
Int. J. Comput. Netw. Inf. Secur.
, vol.
7
, no.
4
, pp.
54
61
,
2015
, DOI: .
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