In today's digital era, companies constantly receive vast amounts of customer interactions across multiple channels, including social media and emails. As one of the largest online marketplaces in Indonesia, Tokopedia actively engages in daily interactions with its customers. The sentiment becomes a benchmark for customer satisfaction and experience. However, the manual processing of customer analysis poses a time-consuming and challenging task. A challenge arises: How to identify emerging trends and address issues from these vast amounts of interactions instantly and accurately? This article delves into transforming customer opinions into actionable insights using AI Sentiment Analysis, focusing on 7.000 Tokopedia customer tweets.
What is Sentiment Analysis?
Sentiment Analysis, a part of Natural Language Processing (NLP), is the process of extracting valuable insights from textual data to find out the sentiments expressed. When analysing customer opinions through tweets, it is common to find multiple aspects addressed in a single tweet. Traditional sentiment analysis typically extracts sentiment from the full text, which leads to oversimplification. Moreover, the approach could incorrectly classify words with different meanings depending on context, resulting in inaccurate sentiment classification. On other hand, Advanced Sentiment Analysis provides a more detailed understanding by associating sentiments with specific topics or issues.
For instance, the term "crazy" tend to be associated with negativity regardless of whether it describes an exciting experience or an irrational situation. We analyse an example of tweet that says, "The new features are crazy, but sometimes the service is lacking."
Traditional Sentiment Analysis Perspective:
The sentiment output will be only negative, ignoring the positive aspects of excitement.
Advanced Sentiment Analysis Perspective:
The sentiment output will be positive for Features and negative for Service, providing a more nuanced and accurate sentiment analysis.
By utilizing This Sentiment Analysis, we uncover the stories embedded in 7,000 Tokopedia customer tweets. Each tweet was categorized based on sentiment and related topics, allowing us to quickly pinpoint various topics related to customer sentiment. This Advanced Sentiment Analysis reveals several issue topics of Tokopedia Customer, including top-up issues, registration issues, refund issues, and many more.
Real-World Case Study: Tokopedia Reports
After categorizing the sentiment and topic of all the tweets, we identify the most pressing customer issues. Using AI sentiment analysis, we can summarize every issue that happened over a specific time frame, such as a month. Beyond merely highlighting the emerging topic issues, we also delve into a comprehensive issue summary. As seen in the following image, payments, finance, and customer support rank as the top issues in June 2023. Additionally, the issue summaries for these topics are readily apparent. This provides immediate actionable insight into the specific issue occurring within these topics.
Businesses frequently encounter recurring issues that arise repeatedly. To truly grasp the evolving dynamics of customer opinion, we track the development of each issue over time using AI Sentiment Analysis. This tool enables companies to proactively observe the progression of popular issues. In the image below, notice how easily we identify recurring “top-up” issues. This continuous monitoring ensures a better understanding of customer sentiment and allows companies to address issues before they become persistent challenges.
Companies are often also involved with significant challenges, especially in early identification of viral issues. The important aspect is to uncover insights into issue trends. A real example is illustrated in the image below. Using AI Sentiment Analysis, we quickly uncover a viral issue related to a mismatch between the goods received and those purchased by customers. We also observe that this issue peaked in mid-September. This tool facilitates early detection of potential viral issues, providing quick awareness for companies to take immediate action.
In conclusion, sentiment analysis integration provides a valuable opportunity for businesses to gain fast, accurate and reliable insights into customer sentiment. This strategic integration not only improves understanding of customer sentiment, but also empowers businesses to anticipate their needs and proactively offer solutions to meet evolving customer requirements. The implementation of sentiment analysis ensures that businesses can effectively navigate the complex consumer sentiment landscape.
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