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<p>In the rapidly evolving food delivery ecosystem, faulty packaging has significantly contributed to food wastage and customer dissatisfaction. This study investigates the multi-dimensional impact of packaging failures in last-mile meal delivery through a combination of qualitative, emotional, and predictive analyses. Leveraging a dataset of customer reviews extracted from food delivery apps, the research applies open AI source for thematic coding, sentiment scoring, and emotional categorization to extract patterns of consumer complaints across food types and packaging formats. The research also builds a predictive model, and from the comments used, the model's classifier was determined to be ‘Naïve Bayes’, which achieved an F1-score of 0.99 in finding negative sentiments about wastage of food because of poor packaging. The analysis reveals that spillage, structural damage, and poor insulation are recurring issues, especially for cakes and Indian curries. Packaging types such as paper bags and plastic containers were often linked to high anger, frustration, and disappointment levels. Embracing consumer voice in packaging evaluation provides insights on where food delivery platforms and packaging manufacturers should focus their attention to help resolve strategic issues and gaps that need to be met. It complements existing knowledge of why strong food packaging serves as a necessity and a brand element for customer experience and why customer satisfaction drives the need to avoid wasteful food disposal.</p>

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From sentiment to spoilage: predictive and qualitative insights into food wastage due to faulty packaging in delivery apps

  • 期刊论文

  • 发表日期：

    2025年12月15日

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  • Sagar Dutt Phuloria,
  • Deepti Aggrawal,
  • Kusum Lata

摘要

In the rapidly evolving food delivery ecosystem, faulty packaging has significantly contributed to food wastage and customer dissatisfaction. This study investigates the multi-dimensional impact of packaging failures in last-mile meal delivery through a combination of qualitative, emotional, and predictive analyses. Leveraging a dataset of customer reviews extracted from food delivery apps, the research applies open AI source for thematic coding, sentiment scoring, and emotional categorization to extract patterns of consumer complaints across food types and packaging formats. The research also builds a predictive model, and from the comments used, the model's classifier was determined to be ‘Naïve Bayes’, which achieved an F1-score of 0.99 in finding negative sentiments about wastage of food because of poor packaging. The analysis reveals that spillage, structural damage, and poor insulation are recurring issues, especially for cakes and Indian curries. Packaging types such as paper bags and plastic containers were often linked to high anger, frustration, and disappointment levels. Embracing consumer voice in packaging evaluation provides insights on where food delivery platforms and packaging manufacturers should focus their attention to help resolve strategic issues and gaps that need to be met. It complements existing knowledge of why strong food packaging serves as a necessity and a brand element for customer experience and why customer satisfaction drives the need to avoid wasteful food disposal.