Customer feedback analysis is pivotal for e-commerce platforms like Hipobuy, a shopping agent service facilitating international purchases. By leveraging natural language processing (NLP) within spreadsheet tools, businesses can systematically evaluate user sentiment—whether positive, negative, or neutral—to derive actionable insights. This paper explores how Hipobuy can analyze feedback data programmatically and implement tailored strategies to enhance brand reputation.
Aggregate user feedback from review platforms (e.g., Trustpilot, social media, or Hipobuy’s portal) into a structured spreadsheet (Google Sheets/Excel). Include columns for review text, date, and rating.
Using built-in scripting (e.g., Google Apps Script’s LanguageService
Create pivot charts to track sentiment trends monthly, highlighting spikes in negativity or advocacy.
| Sentiment | Action Plan | KPI |
|---|---|---|
| Positive |
|
+20% user-generated content shares |
| Neutral |
|
Survey response rate ≥35% |
| Negative |
|
Reduce negative sentiment by 15% quarterly |
After analyzing 1,200 reviews, Hipobuy found 18% negativity around delayed shipments. Strategies included:
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