Harnessing Social Media Analytics for Business Intelligence in the Cosmetics Industry

Description

Research Hypothesis This study hypothesizes that Indonesian consumers’ preferences for sunscreen, particularly the local brand Azarine, are shaped by product attributes (active ingredients, SPF, packaging), user experience, and social proof. It also assumes that communication patterns differ across platforms: TikTok (visual, testimonial-driven) versus X/Twitter (textual, information-driven). Description of the Data The dataset consists of 400 consumer comments (200 from TikTok and 200 from X/Twitter) collected manually between January 2024 and June 2025 using relevant hashtags (#SunscreenLokal, #AzarineSunscreen, #SunscreenSpray, #Azarine). The Raw Data sheet contains the original comments, posting dates, and links. The Preprocessing & Coding Process sheet documents open, axial, and selective coding. The Frequency Table summarizes the distribution of main themes, while the Comparative Analysis sheet highlights platform differences. The Keyword Attribute sheet captures specific categories such as skin type, skin issues, and product variants. Notable Findings Three main themes emerged: (1) Product Attributes (composition, SPF, packaging), (2) Product Performance (visible results on skin), and (3) Social Proof (peer recommendations). TikTok discussions were dominated by performance and social validation (87% and 85%), while X users focused more on technical details (61% attributes). Keywords such as variant, skin type, and skin issue indicate that consumers increasingly seek personalized solutions rather than one-size-fits-all products. How to Interpret and Use the Data This dataset reflects how consumers evaluate and discuss sunscreen across different platforms. Wordclouds and frequency tables provide an overview of frequently used terms, but deeper insights come from grounded theory analysis that organizes comments into thematic categories. Researchers and practitioners can use the dataset to understand consumer discourse patterns, design platform-specific marketing strategies, and develop skincare products that are more personalized, effective, and aligned with Indonesian consumers’ needs.

Steps to reproduce

1. Data collection period → January 2024 – June 2025. 2. Data sources → public comments from TikTok and X/Twitter. 3. Amount of data → 400 comments (200 TikTok, 200 X). 4. Collection method → manual scraping/copy-paste based on hashtags (#SunscreenLokal, #AzarineSunscreen, #SunscreenSpray, #Azarine). 5. Inclusion/exclusion criteria → only comments in Indonesian, relevant to experiences/opinions about the product; excluding promotions, spam, or irrelevant comments. 6. Tools/software → Microsoft Excel for recording (comment content, date, link, user pseudonym). 7. Analysis process → grounded theory (open coding → axial coding → selective coding).

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