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A Study on Brand ROIs for the Professional Product Division of LOPAK Products
No full textIn this Experiential Learning Project (ELP), we examined the return on investment (ROI) of the brand-building activities of the Professional Product Division (PPD) of L\u27Oréal Pakistan. The PPD targets high-end, salon-only brands and is in an expanding, competitive beauty industry where marketing investment must be maximized to get the greatest effect. Nevertheless, before this study, the division had not been able to fit a method to the depth of its different brand investment categories in terms of measuring and evaluating their efficiency and effectiveness in a systematic and data-driven manner.
The main aim of the project was to create a program of specified and individual ROI models of the six most important types of trade investment: Beauty Advisors, Event Sponsoring, Gifts with Purchase, Transactional Paid Media, Influencer Marketing, and Point-of-Sale (POS) Animation. The study was done on independently created datasets by secondary research, industry standards, and logical estimates because no proprietary company data was available. Machine learning models of potential statements, Python-based and econometric regressions, along with other advanced analytical instruments, were utilized to recreate probable performance conditions and recommend actions.
The current research can be linked to Sustainable Development Goal 9, as it facilitates sustainable economic growth by maximizing marketing investments in employment, and it encourages innovation through data-based decision-making models in the industry. Despite the constraints associated with data inaccessibility, the project lays the groundwork for a more transparent, accountable, and effective brand investment behavior within the beauty industry in Pakistan
An Enhanced Grey Wolf Optimizer for Interpretable Decision Tree Induction in Healthcare
No full textAccurate and interpretable clinical prediction models are essential for decision-making in healthcare, where transparency and reliability directly influence patient outcomes. Decision Trees (DTs) remain one of the most interpretable machine learning models, yet their performance is often limited by greedy, locally optimal splitting strategies. To overcome these limitations, swarm intelligence algorithms have been increasingly applied for DT optimization; however, standard metaheuristics such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and the traditional Grey Wolf Optimizer (GWO) still suffer from convergence stagnation, premature exploitation, and reduced diversity, particularly on noisy, nonlinear, and imbalanced clinical datasets. In response to these challenges, this study proposes an Enhanced Grey Wolf Optimizer (EGWO) specifically designed to improve the optimization of DT structures across multiple healthcare domains, including sepsis, diabetes, cancer, heart disease, and burn injury. The proposed EGWO introduces two major methodological innovations. First, a fuzzy logic–based adaptive controller dynamically adjusts the parameter a to maintain an appropriate balance between exploration and exploitation throughout the search process. Instead of relying on GWO’s original linearly decreasing schedule, the fuzzy system incorporates hybrid membership functions, triangular and trapezoidal, to compute smooth, context-aware adjustments based on population diversity and convergence status. Second, the algorithm implements δ-wolf layer removal, simplifying the leadership hierarchy and mitigating early dominance of suboptimal leaders. This modification enhances search diversity, prevents premature convergence, and allows more wolves to contribute meaningfully to the search dynamics
Social internship report - The Citizens Foundation (TCF)
No full textAn excellent opportunity to learn about the backend operations of the biggest NGO in Pakista
Impact of Board Gender Diversity on Firm Risk: Evidence from Pakistan
No full textThis study examines the impact of the presence of female board directors on leverage and capital allocation in Pakistani firms. Results show that the proportion of female board directors in Pakistani listed companies lowers firm leverage; however, it does not significantly affect the efficiency of capital allocation which may be influenced by the senior decision-making environment within the company.I applied the Two-staged least squares (2SLS) and the Generalized Method of Moments (GMM) to evaluate the relationship. For robustness, I also employed Difference-in-Differences (DiD) and the Markov Switching models. My thesis contributes to the literature by analysing the effect of rise in the percentage of female board directors in Pakistani firms following the passing of the Companies Act, 2017 by the Securities and Commission of Pakistan (SECP) in 2017
آئی بی اے All Pakistan Inter-University Archery Championship 2025 to be held at IBA University
No full textآئی بی اے یونیورسٹی میں آل پاکستان انٹریونیورسٹی آرچری چیمپئن شپ All Pakistan Inter-University Archery Championship 2025 to be held at IB
Program (G Utha Pakistan) Discussion About SYNERGY Event to be held at IBA Karachi
No full textThis is Jan GTV News Program (G Utha Pakistan) Dicussion About SYNERGY Event to be held at IBA Karach
IMF team discusses economic challenge with IBA students
No full textA delegation from the International Monetary Fund (IMF), led by Pakistan’s Mission Chief Nathan Porter and IMF Resident Representative Mahir Binici, visited IBA Karachi to engage with students and faculty. The visit included a session titled Pakistan’s Way Forward , where Mr. Porter discussed the country’s macroeconomic conditions, policy development, and future economic challenges. The event also featured an interactive Q&A session with students and a prior faculty discussion with members of the IBA School of Economics and Social Sciences
ClaimPilot
No full textThe rapid digitization of the insurance sector demands innovative solutions to automate traditionally manual, time-consuming, and error-prone processes. In the domain of auto insurance, claim processing remains a significant bottleneck, causing customer dissatisfaction and operational inefficiencies. This project, IntelliClaims, addresses these challenges by leveraging artificial intelligence to automate vehicle damage detection and repair cost estimation from photographic evidence. Drawing from current advancements in computer vision and machine learning, IntelliClaims integrates a secure, web-based platform with state-of-the-art deep learning models for semantic segmentation and regression analysis. The system streamlines claim submission for insurance employees, automates the assessment of vehicle images using a Mask R-CNN model, and predicts repair costs via advanced regression algorithms. Experimental results demonstrate significant improvements in claim processing speed and accuracy, reducing human intervention while maintaining reliability. The IntelliClaims approach not only expedites workflows but also provides a scalable foundation for future enhancements, including advanced fraud detection and real-time customer feedback mechanisms
The Relationship between User Experience, Player-Avatar Identification and Co-Creation Behaviour in the context of Metaverse Gaming
No full textThe metaverse is a dynamic and rapidly expanding virtual environment that integrates gaming, social interactions, and advanced technologies. Marketers increasingly recognize the metaverse as a key avenue for reaching younger demographics and as a crucial channel for engaging with younger audiences The metaverse offers interactive platforms for users to engage with brands leading to collaborative content and personalized experiences This research examines the relationship between user experience, player avatar identification, and co-creation behaviour within gaming platforms in the metaverse, with a particular focus on the moderating effect of interaction with technology and social influence. Grounded in social presence theory, the study explores how avatar identification influences users’ engagement in co-creation activities, including content creation, strategic planning, and knowledge exchange. Additionally, it examines the role of social influence, such as community involvement and peer norms, in shaping these behaviours. By addressing these interconnections, the research provides critical insights into how organizations can utilize the metaverse to drive user engagement, strengthen brand loyalty, and promote value co-creation. This is a quantitative research study and will be using PLS4 to evaluate the results. The findings will contribute to the expanding literature on the metaverse, offering a deeper understanding of its implications for enhancing consumer experiences and redefining marketing and communication strategie