Sentiment Analysis In Evaluating Customer Satisfaction On Online Transportation Services Gojek And Grab

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Introduction

The rapid growth of online transportation services has transformed the way people travel, making it more convenient and accessible. In Indonesia, Gojek and Grab are two of the most popular online transportation services, with millions of users relying on them for their daily commutes. However, despite their popularity, both platforms have experienced a decline in their Top Brand Index (TBI) in recent years, indicating customer dissatisfaction with their services. This phenomenon highlights the need for sentiment analysis to evaluate customer satisfaction and identify areas for improvement.

Background

Online transportation services have become an essential part of modern life, providing users with a convenient and affordable way to travel. Gojek and Grab, two of the most popular online transportation services in Indonesia, have been at the forefront of this revolution. With millions of users relying on them for their daily commutes, these platforms have become an integral part of the country's transportation infrastructure. However, despite their popularity, both platforms have experienced a decline in their TBI in recent years, indicating customer dissatisfaction with their services.

The Importance of Sentiment Analysis

Sentiment analysis is a crucial tool for evaluating customer satisfaction and identifying areas for improvement. By analyzing customer reviews and feedback, businesses can gain valuable insights into their customers' needs and expectations. In the context of online transportation services, sentiment analysis can help identify areas such as application performance, driver behavior, and promotions that are critical to customer satisfaction. By using machine learning algorithms, such as Support Vector Machine (SVM), sentiment analysis can provide accurate and reliable results, enabling businesses to make data-driven decisions.

Methodology

This study employed a mixed-methods approach, combining both qualitative and quantitative data collection and analysis methods. The qualitative data was collected through online questionnaires, while the quantitative data was obtained through sentiment analysis of customer reviews on the Google Play Store. The study involved 400 respondents of Gojek and Grab users in Medan, Indonesia. The data was analyzed using the SVM algorithm, which provided an accuracy of 87.404% for Gojek and 89.268% for Grab.

Results

The results of the study showed that customer reviews were focused on several aspects of service, including application performance, driver behavior, and promotions offered. The study also found that the servqual dimensions, which include tangible, reliability, responsiveness, assurance, and empathy, were critical to customer satisfaction. The results of the gap analysis showed that the whole attribute tested did not meet the user's expectations of all these dimensions.

Discussion

The results of the study highlight the importance of sentiment analysis in evaluating customer satisfaction and identifying areas for improvement. By analyzing customer reviews and feedback, businesses can gain valuable insights into their customers' needs and expectations. The study also found that the servqual dimensions were critical to customer satisfaction, and that the whole attribute tested did not meet the user's expectations of all these dimensions.

Conclusion

In conclusion, this study highlights the importance of sentiment analysis in evaluating customer satisfaction and identifying areas for improvement. By analyzing customer reviews and feedback, businesses can gain valuable insights into their customers' needs and expectations. The study also found that the servqual dimensions were critical to customer satisfaction, and that the whole attribute tested did not meet the user's expectations of all these dimensions. The results of the study provide valuable insights for Gojek and Grab, enabling them to improve their service quality and increase customer satisfaction.

Recommendations

Based on the results of the study, the following recommendations are made:

  • Gojek and Grab should prioritize improving their application performance, driver behavior, and promotions offered to increase customer satisfaction.
  • The servqual dimensions should be used as a framework for evaluating customer satisfaction and identifying areas for improvement.
  • The Importance-Performance Analysis (IPA) method should be used to identify priority attributes for improvement.
  • Gojek and Grab should invest in customer support and feedback mechanisms to ensure that customers' needs and expectations are met.

Limitations

This study has several limitations, including:

  • The study was conducted in Medan, Indonesia, and may not be generalizable to other regions.
  • The study only analyzed customer reviews on the Google Play Store and may not reflect the opinions of all customers.
  • The study only used the SVM algorithm for sentiment analysis and may not be applicable to other machine learning algorithms.

Future Research Directions

Future research should focus on:

  • Conducting a larger-scale study to generalize the findings to other regions.
  • Analyzing customer reviews on other platforms, such as the Apple App Store.
  • Using other machine learning algorithms for sentiment analysis.
  • Investigating the impact of sentiment analysis on customer satisfaction and loyalty.

References

  • Association of Indonesian Internet Service Providers. (2020). Top Brand Index (TBI) Report.
  • Gojek. (2020). Annual Report.
  • Grab. (2020). Annual Report.
  • Kumar, N., & Kumar, P. (2018). Sentiment Analysis of Customer Reviews on Online Transportation Services. Journal of Intelligent Information Systems, 51(2), 257-274.
  • Lee, J., & Kim, B. (2019). Sentiment Analysis of Customer Reviews on Online Shopping Platforms. Journal of Business Research, 103, 345-355.

Appendices

  • Appendix A: Questionnaire used for data collection.
  • Appendix B: Data analysis results.
  • Appendix C: Results of the gap analysis.
  • Appendix D: Results of the IPA analysis.

Q: What is sentiment analysis, and how does it relate to customer satisfaction?

A: Sentiment analysis is a technique used to analyze customer reviews and feedback to determine the overall sentiment or attitude towards a product or service. In the context of online transportation services, sentiment analysis can help evaluate customer satisfaction by identifying areas of strength and weakness.

Q: Why is sentiment analysis important for Gojek and Grab?

A: Sentiment analysis is crucial for Gojek and Grab as it provides valuable insights into customer needs and expectations. By analyzing customer reviews and feedback, these platforms can identify areas for improvement and make data-driven decisions to enhance their services.

Q: What are the servqual dimensions, and how do they relate to customer satisfaction?

A: The servqual dimensions are a framework used to evaluate customer satisfaction by measuring the gap between customer expectations and perceptions. The five dimensions are: tangible, reliability, responsiveness, assurance, and empathy. In the context of online transportation services, these dimensions are critical to customer satisfaction.

Q: What is the Importance-Performance Analysis (IPA) method, and how is it used in this study?

A: The IPA method is a tool used to identify priority attributes for improvement. In this study, the IPA method was used to analyze the gap between customer expectations and perceptions, and to identify areas for improvement.

Q: What are the limitations of this study?

A: This study has several limitations, including:

  • The study was conducted in Medan, Indonesia, and may not be generalizable to other regions.
  • The study only analyzed customer reviews on the Google Play Store and may not reflect the opinions of all customers.
  • The study only used the SVM algorithm for sentiment analysis and may not be applicable to other machine learning algorithms.

Q: What are the recommendations for Gojek and Grab based on the results of this study?

A: Based on the results of this study, the following recommendations are made:

  • Gojek and Grab should prioritize improving their application performance, driver behavior, and promotions offered to increase customer satisfaction.
  • The servqual dimensions should be used as a framework for evaluating customer satisfaction and identifying areas for improvement.
  • The Importance-Performance Analysis (IPA) method should be used to identify priority attributes for improvement.
  • Gojek and Grab should invest in customer support and feedback mechanisms to ensure that customers' needs and expectations are met.

Q: What are the future research directions for this study?

A: Future research should focus on:

  • Conducting a larger-scale study to generalize the findings to other regions.
  • Analyzing customer reviews on other platforms, such as the Apple App Store.
  • Using other machine learning algorithms for sentiment analysis.
  • Investigating the impact of sentiment analysis on customer satisfaction and loyalty.

Q: What are the implications of this study for online transportation services?

A: This study highlights the importance of sentiment analysis in evaluating customer satisfaction and identifying areas for improvement. By analyzing customer reviews and feedback, online transportation services can gain valuable insights into customer needs and expectations, and make data-driven decisions to enhance their services.

Q: What are the benefits of using sentiment analysis in online transportation services?

A: The benefits of using sentiment analysis in online transportation services include:

  • Improved customer satisfaction and loyalty
  • Enhanced service quality
  • Increased competitiveness
  • Better decision-making

Q: What are the challenges of using sentiment analysis in online transportation services?

A: The challenges of using sentiment analysis in online transportation services include:

  • Data quality and availability
  • Algorithm selection and training
  • Interpretation and actionability of results
  • Integration with existing systems and processes