http://dspace.dtu.ac.in:8080/jspui/handle/repository/16282 2026-09-22T10:24:10Z 2026-09-22T10:24:10Z JYOTI Seema (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/23105 2026-09-18T04:10:49Z 2026-08-01T00:00:00Z

Title: CART ABANDONMENT ANALYSIS: IDENTIFYING DROP-OFF PATTERNS AND RECOVERY STRATEGIES Authors: JYOTI; Seema (SUPERVISOR) Abstract: Cart abandonment — the phenomenon where online shoppers add products to their digital cart but exit without completing the purchase — represents one of the most significant yet underutilised revenue opportunities in e-commerce. With global cart abandonment rates averaging 70–75% and India-specific rates ranging from 60–85% depending on category, the cumulative revenue loss runs into billions of dollars annually. This study investigates cart abandonment from two complementary angles: identifying where and why drop-offs occur across the purchase funnel, and evaluating the effectiveness of various recovery strategies deployed post-abandonment. Primary data was collected through a structured survey of 150 online shoppers across Indian metro and Tier-2 cities, supplemented by secondary data from Baymard Institute, Statista, Unicommerce, IAMAI, Klaviyo, and peer reviewed academic literature. Key findings reveal that high shipping costs, price comparison behaviour, complicated checkout processes, and the absence of preferred payment methods — particularly Cash on Delivery and EMI options — are the primary abandonment triggers in the Indian market. The study also identifies that 73% of respondents shop via mobile devices, yet mobile has a disproportionately high abandonment rate, pointing to significant UX friction in mobile checkout flows. Factor analysis grouped abandonment reasons into three clusters: Cost Friction, Process Friction, and Trust and Readiness factors. On the recovery side, personalised email campaigns with a discount incentive emerged as the most effective touchpoint (52% recovery rate), followed closely by WhatsApp reminders (44%) — a finding that reflects India's WhatsApp-first communication culture and has significant implications for D2C brand strategy. Recovery probability drops sharply after 72 hours, validating the industry-standard three-email sequence. The study concludes with the STAR Framework — Segment, Target, Act, refine — a structured, four-step approach to abandonment recovery that emphasises behavioural segmentation before recovery action, helping brands maximise ROI on their retention marketing spend. Practical recommendations are offered across checkout UX, pricing transparency, mobile optimisation, and multi-channel recovery strategy.

2026-08-01T00:00:00Z JINDAL, MANAV Lata, Kusum (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/23104 2026-09-17T04:18:14Z 2026-08-01T00:00:00Z

Title: PREDICTING PERSONAL SAVINGS GOAL ATTAINMENT USING MACHINE LEARNING: A COMPARATIVE ANALYSIS OF CLASSIFICATION MODELS Authors: JINDAL, MANAV; Lata, Kusum (SUPERVISOR) Abstract: The current study evaluates the potential and efficiency of classification models using machine learning for individual savings goals predictions. The study was inspired by an evident gap within the academic literature on personal finance. While the phenomenon of macroeconomic saving behavior has been studied thoroughly, there is virtually no discussion of predictive modeling at the individual level based on machine-learned behavioral patterns. Modern systems used in retail banking and financial advisory services operate on static heuristic algorithms that do not take into consideration the diversity of individual financial profiles. The dataset analyzed is artificial, containing 32,424 samples and 20 initial features related to personal finance theory. Seven new behavioral features have been created based on initial ones using personal finance theory, such as expense-income ratio, savings ratio, and logarithm of total savings. The resulting feature space contains 27 dimensions. There is almost an equal distribution between the two classes (50.5% meet the savings target, while 49.5% fail to do so). Models which were considered for training and performance measurement increased in complexity as follows: Logistic Regression being the baseline model, followed by Decision Tree, Random Forest, XGBoost, and Long Short-Term Memory Neural Network. In terms of performance metrics, AUC-ROC was used primarily, while the other performance metrics included F1 Score, Precision, Recall, Accuracy, and Cross Validation AUC-ROC There was found to be a reliable pattern of performance gains with greater model complexity. The optimized XGBoost model proved to be the superior classifier, having achieved an AUC-ROC of 0.9999, an F1 Score of 0.9959, and an Accuracy score of 0.9958 – decreasing overall classification mistakes by a factor of over 22x (from 621, Logistic Regression). The LSTM network provided good performance with AUC-ROC of 0.9997, but fell short compared to the gradient boosting approaches. Using SHAP analysis on the optimal model, it was discovered that the two most predictive factors in saving goal achievement were total savings (log-transformed) and income per month. Importantly, demographic features such as age, gender, education, employment, and geography did not play a meaningful role in predictions. The research led to the conclusion that the application of machine learning, especially XGBoost along with suitable feature engineering, provides a feasible platform for personalized and proactive savings advice systems.

2026-08-01T00:00:00Z SHARMA, ASHISH Gupta, Shelly (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/23103 2026-09-17T04:08:42Z 2026-08-01T00:00:00Z

Title: A STUDY ON THE IMPACT OF PERCEIVED AUTHENTICITY AND TRUST ON PURCHASE INTENTION- INFLUENCER-OWNED BRANDS VS CORPORATE BRANDS Authors: SHARMA, ASHISH; Gupta, Shelly (SUPERVISOR) Abstract: The fast-changing nature of digital platforms and social media has greatly impacted the world of marketing, leading to the emergence of a new type of brand called influencer-owned brands. Influencers, who used to build their following by creating great content, are now using their individual brand image, credibility, and fan interactions to produce their own brands. This trend has brought about a clear rivalry between influencer-owned brands and corporate brands, affecting consumer decision-making processes. In spite of the rising popularity of influencer-owned brands, one important issue still remains – their ability to impact consumer purchase behavior. Influencers have been proven to drive consumer awareness and interaction, but it is yet to be determined if consumers trust these brands enough to purchase from them. To fill this research void, this study will explore the effects of authenticity, brand trust, social influence, and price on purchase intent. The present research, titled “A Study on the Impact of Perceived Authenticity and Trust on Purchase Intention: Influencer-Owned Brands vs Corporate Brands,” primarily focuses on understanding consumer perception and behaviour in the context of these two competing brand categories. The study seeks to evaluate whether influencer-owned brands are able to match or surpass corporate brands in terms of trust, authenticity, and overall consumer acceptance. A descriptive research design was used during the study. Primary data collection was performed using a structured questionnaire developed according to the criteria set by a five-point Likert scale. The following measures have been included in the questionnaire: awareness, perceived authenticity, brand trust, social influence, price perception, and purchase intention. A total of 150 respondents were chosen randomly using convenience sampling, and the obtained results were processed using Microsoft Excel. Data analysis techniques, including descriptive statistics, correlation analysis, and multiple regression analysis, were utilized. As can be seen from the results obtained, the factor that has the highest impact on consumers' awareness and perception is social influence. Social media trends, peer behaviour, influencers, and other factors significantly affect the awareness of consumers. Nevertheless, the impact of social influence on purchase intentions may be rather low because people often get persuaded to buy things that do not correspond to their needs and preferences. It should also be noted that perceived authenticity is moderate. In other words, consumers relate well to influencers; however, they are skeptical towards the products that influencers promote and brands that they own. Brand trust is one more variable that impacts consumer awareness and perception. As it has already been mentioned above, corporate brands enjoy higher levels of trust because of their reputation and consistency. Finally, price perception can be highlighted as one more variable that impacts consumer awareness and perception. In this case, people tend to make purchases based on the principle of value for money; they may be interested in products promoted by influencers but will not buy them if the prices are too high.

2026-08-01T00:00:00Z MIRWANI, ASHISH Gupta, Shelly (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/23102 2026-09-17T04:04:37Z 2026-08-01T00:00:00Z

Title: FINANCIAL REPORTING STANDARDS: A GLOBAL PERSPECTIVE AND THE SIGNIFICANCE OF IND AS IN INDIA Authors: MIRWANI, ASHISH; Gupta, Shelly (SUPERVISOR) Abstract: Financial reporting, at its core, is about trust. It’s how businesses communicate to the outside world whether they are doing well, struggling, or somewhere in between. As global capital markets have grown increasingly interconnected, the ability of investors, analysts, and lenders to meaningfully compare companies across borders has become both more important and more complicated. This research project takes a broad look at the global landscape of financial reporting standards from the International Financial Reporting Standards (IFRS) and US Generally Accepted Accounting Principles (GAAP) to regional variants like UK GAAP and Chinese Accounting Standards. It traces the long and sometimes difficult journey toward international harmonization, examines where major standards diverge, and explores what those differences mean in practice for financial statement users. A significant part of the project is dedicated to Indian Accounting Standards (Ind AS) India’s own version of IFRS, developed through a convergence process led by the Ministry of Corporate Affairs (MCA) and the Institute of Chartered Accountants of India (ICAI). The research explores the story behind Ind AS, what makes it different from both old Indian GAAP and full IFRS, and why it matters so much for India’s economic future. To complement the theoretical and comparative analysis, this project incorporates primary empirical data collected through a structured Google Form survey of 120 respondents a mix of finance students, chartered accountants, corporate finance professionals, investment analysts, and bankers. The survey findings provide a grounded, user-level view of how Ind AS is actually being experienced in practice.

2026-08-01T00:00:00Z