Page 85 - IMDR Journal 2025

Research Article
AI also tracks what people like, which helps businesses DATA ANALYSIS
adjust their strategies in real-time to fit customer needs
better. Quantitative data from the survey was analyzed using
appropriate statistical methods to identify trends and
Challenges Businesses Face in Using Deep Tech for patterns in responses. Qualitative data from the literature
Marketing review was analyzed through thematic analysis to extract
Companies face challenges using deep tech for marketing key themes and insights
even though deep tech has many benefits but using it in
marketing is not always easy. Table no 1: Age Group
The main problem is the cost of these technologies
especially for the small companies. Another issue is lack of
skilled people who know how to use these technologies and
to train people the cost is needed and important concern is
hoe consumer data is is collected and used which raises
privacy and ethical questions so deal with this problem
companies need to invest in training and all this is need high
cost and have to choose tools that suit their budget, and be
transparent about how they use data. Following laws like the
GDPR and using data responsibly can also help build
consumer trust.
How Well AI Tools Work for Customer Engagement and
Sales
To interact with customers and to understand their wants
companies use AI-powered tools like personalized ads,
virtual try-on AR features, and chatbots have made a big
impact on companies and customers. These tools make
customer service faster and more helpful, which keeps
customers happy. For example, chatbots can answer
questions anytime, and personalized ads show people things
they’re actually interested in. AR lets customers see how a
product looks or works before buying it. These tools not only
make customers more engaged but also help increase sales
by making the shopping experience smoother and more
enjoyable.
RESEARCH METHODOLOGY
This research uses a primary research method by collecting
data from the survey responses . The goal was to understand
how deep tech is affecting marketing. The survey data were
collected from the college students and business . Along
with the survey, academic articles and studies were also
reviewed to support the findings. The systematic
questionnaire aimed at students, professionals, and Chart No. 1
entrepreneurs, emphasizing AI- based personalization, As seen in chart no 1 which is referred to from table no 1, the
privacy issues related to data, consumer behavior in online data we have collected consists of the candidates from
shopping, and new technologies such as AR. Non- various age groups such as 18-24 (approx. 81.8%), 25-34
probability convenience sampling was employed, and the (approx. 13.6%), 35-44 (0 %), 45-54(approx. 1%) and 55+
answers were analyzed based on descriptive statistics and (0%).
represented using charts.
Secondary data from Google Scholar, JSTOR, and business Table no 2: Occupation of the respondents
reports were used for further context. Ethics measures were
put in place to maintain anonymity and voluntary response
without collecting any sensitive information. Although the
research presents useful information, limitations in sample
size and geographic location exist. More research can build
on this study with a larger sample size and population to
further understand the role of deep tech in marketing.

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