Shopping marketplace analysis based on customer insights using social media analytics

Area of Science:

• Data Science and Business Analytics
• Digital Marketing and Consumer Behavior

Background:

• Social media platforms have transformed digital marketing, offering rich data for analysis.
• Companies can utilize data analytics and sentiment analysis to understand customer sentiments and predict behavior.

Purpose of the Study:

• To explore the application of data analytics on social media for market strategy and operational planning.
• To provide insights for businesses to optimize product offerings and enhance customer satisfaction.
• To develop a conceptual model for extracting shopping experiences and customer purchasing decision factors.

Main Methods:

• Conducted a literature review on data analytics in social media and sales strategies.
• Designed a case study to analyze customer perceptions and satisfaction across different store types.
• Explored the utilization of social media and customer feedback for insight generation.

Main Results:

• Identified potential implications for businesses to optimize products and customer satisfaction within cultural contexts.
• Gained understanding of customer perceptions and satisfaction levels towards various retail environments.
• Proposed a conceptual model for extracting key factors influencing customer purchasing decisions.

Conclusions:

• Sentiment analysis and data analytics are crucial for understanding customer behavior and improving business strategies.
• The conceptual model provides a framework for businesses to leverage social media data for market insights.
• Understanding cultural contexts is vital for tailoring product offerings and enhancing customer satisfaction.