Sentiment Analysis of Hipobuy Customer Feedback in Spreadsheets for Brand Reputation Management
Introduction
In the digital commerce era, consumer feedback serves as a critical indicator of brand perception. This article explores how Hipobuy leverages Natural Language Processing (NLP) techniques within spreadsheet platforms to analyze sentiment trends from its prox-purchase service feedback, enabling data-driven brand reputation strategies.
Methodology
1. Data Collection & Preprocessing
- Source:
- NLP Integration:
2. Sentiment Scoring
| Score Range | Sentiment Label | Response Example |
|---|---|---|
| 0.6 - 1.0 | Positive | "Packaging was eco-friendly and shipping exceeded expectations!" |
| -0.2 - 0.5 | Neutral | "Received order in 7 days as estimated." |
| -1.0 - -0.3 | Negative | "Fake product received - no quality control!" |
3. Visualization
Create dynamic dashboards with:
• Pie charts showing sentiment distribution (%)
• Time-series graphs to track fluctuations post-marketing campaigns
• Conditional formatting to highlight urgent issues (red: >3 negative mentions/hour)
Brand Reputation Strategy Framework
◉ Positive Feedback (82%)
Action Plan:
- Implement "UGC Amplification Program": Feature top-rated reviews in ads
- Create case study videos with consent from highly satisfied customers
- Offer referral bonuses for viral-worthy testimonials
◉ Neutral Feedback (12%)
Action Plan:
- Initiate "Experience Upgrade" touchpoints: Follow-up emails with personalized product suggestions
- Analyze recurring neutral phrases (e.g., "average delivery") for operational improvement
◉ Negative Feedback (6%)
Crisis Protocol:
- **6-Hour Response SLA**: Template responses forbidden – all replies must show:
✓ Problem comprehension ("We confirm your Issue X")
✓ Concrete solution timeline ("Refund processed within 24h")
✓ Preventative measures ("Adopting new anti-counterfeit scanners")
- Weekly root-cause analysis meetings with logistics partners
Impact Measurement
Post-implementation KPIs tracked in new spreadsheet tabs:
✓ NPS Lift:
✓ Complaint Resolution Rate:89% first-contact resolution
✓ Sentiment Velocity:15% week-over-week