The objective of the present work is to describe how Business Analytics can be leveraged to analyze customer feedback and derive sentiment-driven insights for product improvement. The scope of the project includes, but is not limited to, understanding various forms of customer feedback (structured and unstructured), applying sentiment analysis techniques, and interpreting results to inform product development decisions.
The project will explore methods of collecting customer feedback from multiple channels such as surveys, reviews, social media, and support tickets. It will also cover the process of cleaning, analyzing, and visualizing customer sentiment data using analytical tools.
The study will emphasize the role of descriptive, diagnostic, and predictive analytics in identifying product pain points, improvement opportunities, and customer satisfaction trends. The work also aims to recommend data-driven strategies for product enhancement based on customer voice.
Designing a questionnaire or collecting real-time data from platforms such as Google Reviews, app feedback, or social media to capture customer sentiments about a product or service.
Using tools like Google Forms or MS Forms to gather structured feedback.
Performing basic text preprocessing and sentiment classification using tools such as MS Excel (for structured responses), or basic Python/NLP tools (for open-ended/unstructured feedback).
Using Power BI or Tableau to visualize trends, sentiment scores, and customer preferences.
Identifying key themes and pain points from sentiment analysis and mapping them to product features for improvement suggestions.
Preparing a detailed report summarizing the methodology, tools used, findings, and recommendations for product enhancement.
Presenting the project findings and recommendations in a group presentation to the mentor, highlighting how Business Analytics techniques influenced decision-making.
