Artificial Intelligence Influence on Consumer Buying Decisions and Market Engagement
Research Scholar,
Department of MBA,
K L Business School,
Koneru Lakshmaiah, Education Foundation,
A Deemed to be University,
Andhra Pradesh, Guntur
Dr. A. Udaya Shankar
Associate Professor,
Department of MBA,
K L Business School,
Koneru Lakshmaiah, Education Foundation,
A Deemed to be University,
Andhra Pradesh, Guntur
Corresponding Author
Dr.udayashankar@kluniversity.in
Dr. Sharon Gonsalves Durham
Associate Professor
ATLAS SkillTech University
Dr. Durgaprasad Navulla
Assistant Professor,
KL Business School, KLEF,
Guntur, AP, India
Dr. Mohit Sharma
Associate Professor,
Symbiosis Law School, Noida,
Symbiosis International (Deemed University),
Pune
Abstract
The rapid advancement of artificial intelligence (AI) has significantly transformed the landscape of modern marketing and consumer behavior. AI-driven tools such as personalized recommendations, chatbots, and predictive analytics are increasingly shaping how consumers interact with brands and make purchasing decisions. This study explores the extent to which AI influences consumer buying decisions and enhances market engagement in a dynamic digital environment.
The major objective of this research was to examine the role of artificial intelligence in influencing consumer buying decision and market engagement. The study is done in four prominent industries of Delhi-NCR i.e. healthcare, IT, e-commerce and FMCG. A sample of 239 companies was finalized with the application of convenience sampling. The respondents were requested to fill the questionnaire via Google docs app, especially designed for the study. The collected data was analyzed and it was observed that several companies have fully adopted AI in marketing activities and their major objectives behind this adoption were improving customer retention and targeting along with improving customer engagement. It was concluded that there is significant positive impact of artificial intelligence on consumer buying decision and market engagement. Majority of marketers were found to be satisfied with the applications of artificial intelligence in marketing activities.
Key Words: Machine learning insights, Consumer Preferences, Buying Intentions, marketing prediction
Introduction:
Artificial Intelligence (AI) has brought forth paradigm shift within the manner business is conducted and consumers are catered for. With the advent of cutting-edge technology including Machine learning, natural language processing and data analytics, firms are able to accumulate, manipulate and interpret the plethora of consumer information on a real-time basis. This shift in technological approach has not only allowed companies to better perceive customer's requirements but also accurately predict their purchase and provide them with highly personalized responses, revolutionizing marketing concepts and traditional methods.
In today's digital market, AI applications such as recommending engines, virtual assistants and chatbots significantly influences decision-making process of the consumers, making it to personalize suggestions of the products along with improving convenience with instant support. As more consumers resort to digital platforms for information and to make purchases, AI serves as a prime influencer in affecting their perspective, attitudes and purchasing behavior.
AI has further contributed towards augmenting market engagement with creation of responsive and interactive communication channels. Through targeted advertising, analysis of sentimen and immediate feedback mechanism, businesses can interact with customers in a personalized and effective manner. The level of engagement created leads to an improved brand relations, higher customer satisfaction and in turn higher purchasing rate for business in competitive environment.
Even with increase in usage of AI in marketing strategies, there still exists a need to evaluate its overall impact on market engagement and consumers' decision-making process. Topics such as data security, ethical issues and consumer's confidence in AI generated strategies impact its efficiency. The present research intends to evaluate the effect of AI on purchase decision and market engagement.
Review of Literature:
Acatrinei et al. (2025) conducted research on artificial intelligence in digital marketing with specific emphasis on consumer engagement and sustainable behavior on social and mobile networks. Their research presents a review of how AI- powered features like personalization, recommendation systems, and behavior analysis helps in consumer engagement, and encourages a sustainable approach to consumption. The authors suggest that in digital media, AI enhances engagement and sustainability through conscious impact on the consumers' cognition and decisions.
Filipovi and api (2025) found that elements of artificial intelligence as key antecedents of social media consumer engagement and purchase intention were significant. Their research focuses on the utilization of chatbot, automated answers and personalized news feed powered by artificial intelligence, as major drivers of consumer engagement on social media platforms. They report that AI contributes to consumer engagement which in turn positively affects consumer purchase intention.
Gupta and Khan (2024) conducted a systematic review on the role of AI in customer engagement, and determined areas for further research. They found the classification of the applications of AI in personalization, predictive analysis, customer interaction management. The research concludes on the importance of AI's impact on customer engagement and proposes future research areas like ethics, customer data privacy and relationships.
Behera et al. (2024) investigated the changes brought by AI on customer engagement from the view of the e-retailers in the Retail 4.0. They illustrate that the applications of AI such as machine learning, automated virtual agents and intelligent recommenders help in creating a good experience and customer- retailers relationship.
SuraaSnchez and AramendiaMuneta (2024) reviewed extensively the effect of AI on customer and advertising engagement. Their research provides a thorough analysis of how AI-enabled marketing approaches, like customized ads and programmatic ads, increase engagement levels by offering relevant and time-sensitive content to customers. Furthermore, they conclude by offering future research perspectives, which are primarily related to an analysis on ethics, the level of transparency and the potential shift of the role of AI in consumers-brand interactions.
Asante et al. (2023) explored the process of consumer engagement optimization through artificial intelligence features in e-commerce platforms. This study provides insights of AI elements, such as recommendation engines, dynamic pricing and personalized user interfaces, which strongly increase the engagement and user experience. It is found that optimization through AI not only enhances customer satisfaction but also increases conversion rate by calibrating platform's functionalities according to individual user needs.
Bag et al. (2022) discussed and investigated the modern digital customer journey through artificial intelligence technologies for user engagement and conversion. Their findings prove that AI contributes throughout different stages of the customer journey (awareness, purchase and post-purchase interaction) with features such as predictive analytics and conversational interfaces, enhancing therefore customer engagement and conversion by providing tailored and relevant interventions.
Perez-Vega et al. (2021) propose a conceptual framework that explains the mechanisms by which artificial intelligence is transforming the contexts of online customer engagement behavior. They explain that AI is transforming traditional ways of customer engagement by introducing intelligent interaction, adaptation and automation into customer-user interface. This framework explains the effects of AI on the cognitive, affective and behavioral states of customer engagement.
Prentice et al. (2020) investigated the connection between AI quality performance and customer engagement and determined that consumer attitudes toward AI moderate this relationship. This study reveals that by improving perceived quality of AI systems customer engagement increases. However, when consumer attitude toward the use of AI systems is favorable, the relationship between quality and engagement becomes even stronger.
Theodoridis and Gkikas (2019) explore how AI has affected digital marketing and offer predictions on how AI is going to change the practice. They have stated that AI is creating effective and efficient ways for firms to engage consumers through data driven decision making, automation, and improved targeting in marketing efforts and also indicates a growing need to incorporate AI in marketing strategy in order to gain competitive advantages.
The article published by Kumar et al. (2019) in "The Role of Artificial Intelligence in Personalized Engagement Marketing" focused on the ways a firm can utilize artificial intelligence to personalize engagement marketing at scale. The study reported that firms can use artificial intelligence to process large amount of consumer data in order to create personalized content, recommendations, and interactions in real-time. Further more, they reported that artificial intelligence-enabled personalized engagement will enhance customer satisfaction, loyalty, and value generation to an organization and this makes AI-driven personalized engagement a critical aspect of modern marketing.
The article published by Bader and Kaiser (2019) titled "Human Users in Algorithmic Decision-Making Supported by Artificial Intelligence" investigated the use of human interfaces and the extent in which they influence human interaction and the level of human involvement in the decision-making process with the aid of AI. The authors of this study claimed that interface design is key to achieving human-AI collaboration that enables users to trust AI-driven systems and to provide better control and transparency in the decision-making process with AI. They noted that this aspect is particularly important in circumstances where the outcome of AI-driven decision-making affect consumers in several ways.
The article published by Dimitrieska et al. (2018) on "Artificial Intelligence in Marketing-The Trend of Growing Importance" discussed the trend where artificial intelligence plays a greater role in marketing. According to them, artificial intelligence tools such as data mining, predictive analytics and automated tools are expected to increase the effectiveness of marketing through improved analysis of markets, market segmentation and market targeting. They concluded that these processes are better implemented with the aid of AI technologies, thereby increasing the effectiveness and efficiency of the firm's marketing efforts.
In the study published by Vishnoi et al. (2018) on "AI enabled marketing solutions" they focused on how intelligent marketing systems are utilized in various areas of marketing. They gave examples such as AI-enabled chatbots, recommendation engines, and automatically directed marketing systems and stated that they improve how firms engage with customers, offer personalization, and increase marketing outcomes. The authors also noted that AI systems provide better customer insights while enhancing overall marketing performance.
Davenport and Ronanki (2018) introduced "Augmenting the Enterprise with Artificial Intelligence: Use Cases across Businesses" and how companies can utilize AI effectively by applying a categorisation structure into cognitive insights, cognitive engagement and process automation. However they emphasize the success of utilizing artificial intelligence requires clear business purposes, significant quantity of data and organization readiness and particularly with consumer-facing elements such as service and marketing operations.
Rathore (2017) researched "Fashion Marketing in Digital Age", where he introduced early concepts such as the utilization of immersive media, especially focusing on how artificial intelligence can bring brand innovation through consumer engagement. According to Rathore, AI-driven personalized advertising and interactive virtual experiences can "revolutionize consumer-firm relationship", providing new methods of marketing in fashion industry.
Nadimpalli (2017) in his research titled "Artificial Intelligence-Impact on Consumers and Industries" described how the presence of AI influences consumers' behavior by enhancing personalized services, improving automation of interactions, and service efficiency. His research suggests that for industries, AI will bring efficient, and cost-saving mechanisms, as well as an opportunity for innovation; however he does warn that adoption can be difficult, mainly considering technological and ethical issues involved in AI integration.
Ivanov and Webster (2017) have suggested the introduction of 'robots as consumers' in their article and proposed future research on this notion. The authors expand the notion of 'consumer behavior' by bringing 'agents of AI' into account in market transactions; therefore suggesting that in the future, the role of AI in market mechanisms might transform consumption patterns and marketing processes.
Rathore (2016) also analyzed AI and marketing practices related to ethical fashion in the field of sustainability and consumer engagement. The study suggested that AI can promote marketing to be ethical through providing transparence and traceablity as well as personalization. Moreover, AI has the potential to bring marketing closer to both ethics and environments.
Sifa et al. (2015) analyze the adoption of AI techniques to predict purchase behavior in free-to-play mobile games. It provided that through the use of machine learning models it could predict purchase intent. These prediction were made by analyzing user behavior data in order to increase conversions by maximizing the effective of their strategy of monetization. It could highlight how AI can better understand consumer behavior in a digital space.
Research Gap
Although a large amount of literature on how AI is changing marketing strategies, there are still a few important gaps that have not been studied in depth. Previous research has mainly centered on either conceptualization, technology, or a specific concept such as consumer engagement or purchase intent and has not conducted in-depth analysis and empirical research in the context of overall organizational use of AI in marketing. Although personalization driven by AI is already discussed well, there is lack of detailed research on its direct impact on consumers buying decision. Customer engagement is only evaluated separately rather than being looked at as a concept influenced directly by AI and its effective over digital space. Marketers’ opinion on usage of AI technologies in their strategy, their satisfaction towards AI features in relation to effectiveness, usability and outcome has not received much attention in previous studies. Therefore this study aimed to cover up the above gaps by providing in-depth analysis on the adoption of AI in marketing, their impact on the consumers' buying behavior and how market engagement is influenced by them, marketers' satisfaction on using the AI-based systems and solutions.
Objectives
Hypotheses
Research Methodology
Analysis of Data
The demographic profile of respondents is shown in table 1
Table 1: Demographic Profile of Respondents
|
Gender |
N |
Percentage |
|
Male |
184 |
76.99 |
|
Female |
55 |
23.01 |
|
Total |
239 |
100 |
|
Age |
N |
Percentage |
|
Up to 25 Years |
21 |
8.79 |
|
25 to 40 Years |
72 |
30.13 |
|
40 to 55 Years |
89 |
37.24 |
|
Above 55 Years |
57 |
23.85 |
|
Total |
239 |
100 |
|
Qualification |
N |
Percentage |
|
Graduate |
47 |
19.67 |
|
Postgraduate |
109 |
45.61 |
|
Professional Degree |
83 |
34.73 |
|
Total |
239 |
100 |
Table 2 is depicting the job profile of respondents as explained in following sub sections:-
Table 2: Job profile of Respondents
|
Industry Type |
N |
Percentage |
|
Healthcare |
58 |
24.27 |
|
IT |
72 |
30.13 |
|
E-commerce |
40 |
16.74 |
|
FMCG |
69 |
28.87 |
|
Total |
239 |
100 |
|
Current Designation |
N |
Percentage |
|
Marketing Executive |
37 |
15.48 |
|
Marketing Manager |
91 |
38.08 |
|
Digital Marketer |
58 |
24.27 |
|
Brand Manager |
29 |
12.13 |
|
CRM Manager |
24 |
10.04 |
|
Total |
239 |
100 |
|
Work Experience |
N |
Percentage |
|
Up to 5 Years |
7 |
2.93 |
|
5 to 10 Years |
13 |
5.44 |
|
10 to 15 Years |
87 |
36.40 |
|
More than 15 Years |
132 |
55.23 |
|
Total |
239 |
100 |
Table 3 is showing the Adoption and usage of machine learning tools in marketing activities of selected companies.
Table 3: Organizational Usage of AI in Marketing
|
Current adoption status of Artificial Intelligence in marketing activities |
||
|
Response |
N |
Percentage |
|
Fully adopted |
136 |
56.90 |
|
Partially adopted |
62 |
25.94 |
|
Pilot stage |
41 |
17.15 |
|
Total |
239 |
100 |
|
Which AI-based marketing tools are currently used in your organization? |
||
|
Response |
N |
Percentage |
|
Predictive Analytics |
174 |
72.80 |
|
Personalized Recommendations |
154 |
64.44 |
|
Chatbots/Virtual Assistants |
192 |
80.33 |
|
Programmatic Advertising |
179 |
74.90 |
|
Customer Segmentation Tools |
127 |
53.14 |
|
Dynamic Pricing |
87 |
36.40 |
|
Primary objectives of using AI in marketing |
||
|
Response |
N |
Percentage |
|
Improve customer targeting |
167 |
69.87 |
|
Enhance customer engagement |
158 |
66.11 |
|
Increase conversion rates |
143 |
59.83 |
|
Improve customer retention |
179 |
74.90 |
|
Reduce marketing costs |
123 |
51.46 |
Table 4 is showing the respondents’ perception regarding impact of AI-driven personalization in shaping consumer buying decisions. Respondents highlighted that AI not only predict accurate consumer demand (Mean=4.07) but it also influence consumers’ impulse buying behavior (Mean=3.92) and helps in better understanding of buying behaviour (Mean=3.78). AI is extremely beneficial as it improve the accuracy of marketing decisions (Mean=3.62), increase sales conversions (Mean=3.55) and improve effectiveness of promotional campaign (Mean=3.48). As per respondents AI enables more effective product recommendations to consumers (Mean=3.46) and AI-based personalization positively influences consumers’ purchase decisions (Mean=3.41).
Table 4: Impact of AI-driven personalization in shaping consumer buying decisions
|
AI Impact on Consumer Buying Decisions |
Mean |
S.D. |
C.V. |
Agreement Level |
|
AI helps in better understanding consumer buying behavior. |
3.78 |
0.97 |
0.26 |
Agree |
|
AI enables more effective product recommendations to consumers. |
3.46 |
0.84 |
0.24 |
Agree |
|
AI-driven insights improve the accuracy of marketing decisions. |
3.62 |
1.05 |
0.29 |
Agree |
|
AI helps predict consumer demand more accurately. |
4.07 |
0.74 |
0.18 |
Agree |
|
AI-based personalization positively influences consumers’ purchase decisions. |
3.41 |
0.69 |
0.20 |
Agree |
|
AI improves the effectiveness of promotional campaigns. |
3.48 |
1.09 |
0.31 |
Agree |
|
AI-based tools influence consumers’ impulse buying behavior. |
3.92 |
1.12 |
0.29 |
Agree |
|
AI-driven marketing strategies increase sales conversions. |
3.55 |
0.76 |
0.21 |
Agree |
Overall according to 82.85% respondents, AI-driven personalization has positive impact on consumer buying decisions.
Table 5: Overall Impact of AI-driven personalization in shaping consumer buying decisions
|
Overall Impact |
N |
Percentage |
|
Negative |
41 |
17.15 |
|
Positive |
198 |
82.85 |
|
Total |
239 |
100 |
To check the significance of AI-driven personalization in shaping consumer buying decisions following hypothesis has been taken:-
H01: There is no significant impact of AI-driven personalization in shaping consumer buying decisions.
Ha1: There is a significant impact of AI-driven personalization in shaping consumer buying decisions.
The one sample t-test was applied against the theoretical mean 24, for testing of above hypothesis. The computed mean (29.25) is higher than theoretical mean along with the significant value of t-statistic so it can be concluded that there is a significant impact of AI-driven personalization in shaping consumer buying decisions.
Table 6: One Sample t-test result to measure significance of Impact of AI-driven personalization in shaping consumer buying decisions
|
Variable |
Observed Mean |
Test Value = 24 |
Result |
||
|
t-value |
degree of freedom |
p-value |
|||
|
Impact of AI-driven personalization in shaping consumer buying decisions |
29.25 |
11.18 |
238 |
0.000 |
Significant |
Level of Significance=5%
Table 7 is highlighting the respondents’ agreement with the statements related to role of Artificial Intelligence in enhancing market engagement. Respondents indicated that AI-powered engagement strategies increase repeat purchases (Mean=4.07), AI-based chatbots enhance customer experience and satisfaction (Mean=3.91) and AI-driven content personalization increases customer retention (Mean=3.82). Respondents further indicated various benefits of AI in market engagement like it improves customer engagement (Mean=3.74), enables real-time interaction with customers (Mean=3.65), helps in building long term relationship with customers (Mean=3.52) and improves brand-consumer interaction across digital platforms (Mean=3.42). The coefficient of variation for all the statements ranges from 0.17 to 0.32, which shows the homogeneity in the opinion of respondents.
Table 7: Role of Artificial Intelligence in enhancing market engagement
|
AI and Market Engagement |
Mean |
S.D. |
C.V. |
Agreement Level |
|
AI-driven marketing improves consumer engagement with brands. |
3.74 |
1.05 |
0.28 |
Agree |
|
AI helps in building long-term relationships with customers. |
3.52 |
0.74 |
0.21 |
Agree |
|
AI enables real-time interaction with customers. |
3.65 |
0.62 |
0.17 |
Agree |
|
AI-powered engagement strategies increase repeat purchases. |
4.07 |
0.88 |
0.22 |
Agree |
|
AI-based chatbots enhance customer experience and satisfaction. |
3.91 |
1.06 |
0.27 |
Agree |
|
AI improves brand-consumer interaction across digital platforms. |
3.42 |
1.11 |
0.32 |
Agree |
|
AI-driven content personalization increases customer retention. |
3.82 |
0.99 |
0.26 |
Agree |
More than 80% of the respondents (86.61%) indicated the positive role of Artificial Intelligence in enhancing market engagement.
Table 8: Overall Role of Artificial Intelligence in enhancing market engagement
|
Overall Role |
N |
Percentage |
|
Negative |
32 |
13.39 |
|
Positive |
207 |
86.61 |
|
Total |
239 |
100 |
To check the significance of role of Artificial Intelligence in enhancing market engagement following hypothesis has been taken:-
H02: There is no significant role of Artificial Intelligence in enhancing market engagement.
Ha2: There is a significant role of Artificial Intelligence in enhancing market engagement.
The one sample t-test was applied against the theoretical mean 21, for testing of above hypothesis. The calculated average (26.12) is more than theoretical mean along with the significant value of t-statistic so it can be concluded that there is a significant role of Artificial Intelligence in enhancing market engagement.
Table 9: One sample t-test result to measure Role of Artificial Intelligence in enhancing market engagement
|
Variable |
Observed Mean |
Test Value = 21 |
Result |
||
|
t-value |
degree of freedom |
p-value |
|||
|
Role of Artificial Intelligence in enhancing market engagement |
26.12 |
12.27 |
238 |
0.000 |
Significant |
Level of Significance=5%
Table 10 is depicting the marketers’ satisfaction with artificial intelligence applications. It can be seen that respondents were highly satisfied with Enhancement in customer engagement levels (Mean=4.17, Rank=1), Reduction in manual effort and decision-making time (Mean=4.09, Rank=2), Accuracy of customer targeting and segmentation (Mean=3.94, Rank=3) and Effectiveness of personalized marketing campaigns (Mean=3.88, Rank=4). Respondents were satisfied with Increase in conversion rates and sales performance (Mean=3.72, Rank=5), Support for real-time marketing and quick response to market changes (Mean=3.64, Rank=6) and Reliability and consistency of AI-driven outputs (Mean=3.58, Rank=7), however they were comparatively less satisfied with Ease of use and integration with existing marketing systems (Mean=3.49, Rank=8), Quality and usefulness of consumer insights generated (Mean=3.41, Rank=9) and Return on marketing investment (Mean=3.31, Rank=10).
Table 10: Marketers’ satisfaction with artificial intelligence applications
|
Parameters |
Mean |
S.D. |
C.V. |
Rank |
|
Accuracy of customer targeting and segmentation |
3.94 |
1.18 |
0.30 |
3 |
|
Ease of use and integration with existing marketing systems |
3.49 |
1.09 |
0.31 |
8 |
|
Effectiveness of personalized marketing campaigns |
3.88 |
0.78 |
0.20 |
4 |
|
Enhancement in customer engagement levels |
4.17 |
0.64 |
0.15 |
1 |
|
Increase in conversion rates and sales performance |
3.72 |
0.89 |
0.24 |
5 |
|
Quality and usefulness of consumer insights generated |
3.41 |
0.76 |
0.22 |
9 |
|
Reduction in manual effort and decision-making time |
4.09 |
0.65 |
0.16 |
2 |
|
Reliability and consistency of AI-driven outputs |
3.58 |
1.05 |
0.29 |
7 |
|
Return on marketing investment (ROMI) |
3.31 |
1.01 |
0.31 |
10 |
|
Support for real-time marketing and quick response to market changes |
3.64 |
0.72 |
0.20 |
6 |
Overall 87.45% respondents indicated satisfaction with artificial intelligence applications but 12.55% respondents were dissatisfied with artificial intelligence applications.
Table 11: Marketers’ overall satisfaction with artificial intelligence applications
|
Overall Satisfaction |
N |
Percentage |
|
Low |
30 |
12.55 |
|
High |
209 |
87.45 |
|
Total |
239 |
100 |
To check the significance of difference in marketers’ satisfaction towards artificial intelligence applications with respect to their designation and work experience following hypothesis has been taken:-
H03: Marketers’ satisfaction with artificial intelligence applications is indifferent with respect to their designation and work experience.
Ha3: Marketers’ satisfaction with artificial intelligence applications is indifferent with respect to their designation and work experience.
To test this hypothesis the ANOVA test was applied as shown in table 12 and 13. In both the cases the computed value of F-ratio is significant so it can be concluded that marketers’ satisfaction with artificial intelligence applications is indifferent with respect to their designation and work experience.
Table 12: ANOVA test result to measure difference in Marketers’ satisfaction with artificial intelligence applications with respect to their designation
|
Source of Variation |
Sum of Squares |
Degree of Freedom |
Mean Sum of Squares |
F-Ratio |
p-value |
Result |
|
Between Samples |
49857.65 |
4 |
12464.413 |
34.506 |
0.000 |
Significant |
|
Within Samples |
84526.36 |
234 |
361.224 |
|||
|
Total |
134384.01 |
238 |
|
Level of Significance=5%
Table 13: ANOVA test result to measure difference in Marketers’ satisfaction with artificial intelligence applications with respect to their work experience
|
Source of Variation |
Sum of Squares |
Degree of Freedom |
Mean Sum of Squares |
F-Ratio |
p-value |
Result |
|
Between Samples |
54896.3 |
3 |
18298.767 |
33.447 |
0.000 |
Significant |
|
Within Samples |
128569.69 |
235 |
547.105 |
|||
|
Total |
183465.99 |
238 |
|
Level of Significance=5%
Implications of the Study:
The conclusions drawn from this study have various important managerial implications for firms that use artificial intelligence in their marketing mix. The widespread usage of AI by firms that employ these technologies to increase customer retention, targeting and engagement illustrates the growing prevalence of data based decisions in today's markets. Since most of the respondents affirmed that AI fueled personalization influenced their purchasing decision the organizations must concentrate on implementing the various AI technologies available like prediction analysis, customized recommendations, etc to impact buying behavior and to make campaigns more successful. Furthermore, the role of artificial intelligence in predicting customer demand, as well as creating the impulse for consumers to purchase a product, proves that the firms can exploit these tools effectively to drive sales conversions and to achieve competitive advantage.
Furthermore, this study indicates the importance of AI in fostering better market engagement through real-time engagement, enhanced customer service and lasting relations. While the respondents express satisfaction in aspects such as improved engagement, accurate targeting and reduction in decision making time which all point towards improved marketing efficiency and effectiveness due to the use of AI technologies; it needs to be noted that the respondents are not satisfied with the system integration, data analysis quality and return on investment. Companies need to address the issue of system compatibility and investment for improvement of data quality and metrics to maximize the potential of the investment.
References
