Javeria Hassan Khan
Lecturer, Department of Management Science
This research attention at identifying the role of perceived coolness in generating brand equity in smart watch users through customer experience. The data is collected from smart watch users from the twin cities. We will explore how some products can increase brand equity i.e., brand awareness, brand associations, perceived quality and brand loyalty. The AI analytics will help discover consumers insights, find new patterns and discover relationships in the data.
1- Data Collection and Preprocessing:
Description: Successful acquisition and preprocessing of diverse datasets containing information related to brand performance, consumer sentiment, and market trends.
2- Feature Selection and Model Training:
Description: Identification of relevant features for brand equity prediction and training of machine learning models for accurate analysis.
3- Prediction Model Development:
Description: Development of a robust prediction model to assess brand equity, considering factors such as consumer perception, market
trends, and competitive landscape.
4- Analysis and Insights Generation:
Description: Conducting in-depth analysis using the developed model to generate insights into the determinants of brand equity and its
impact on market performance.