Breaking Barriers: NLP-driven Customer Experience for Hindi Devanagari Script E-commerce Users – Dr.S.Chithra

Medium Link: https://medium.com/@chithra.kdc/breaking-barriers-nlp-driven-customer-experience-for-hindi-devanagari-script-e-commerce-users-7b377f7c69ff

Course Relevance: BCA V semester – Data Analytics, MCA II semester – Machine Learning , II PGDM – Text Analytics &Digital Marketing

Teaching Notes:

It shows how ApnaBazaar used NLP with transfer learning (IndicBERT & mBERT) to process Hindi (Devanagari) reviews, queries, and complaints. Traditional English-focused AI missed key insights, while the Hindi-enabled system improved sentiment analysis, search, and complaint handling. The focus is on how language inclusivity enhances customer experience and expands reach in Tier-2/3 markets.

Learning Objectives

• Understand the Fundamentals of NLP
• Identify Key NLP Applications
• Analyze Real-World NLP Implementation
• Assess the Business Impact of NLP in Multilingual Markets

Introduction

“Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) and Computational Linguistics that enables computers to understand, interpret, and generate human language”. Positioned at the intersection of computer science, linguistics, and machine learning, NLP bridges the gap between human communication and digital systems. It powers tasks such as sentiment analysis, text classification, machine translation, information retrieval, and speech recognition

The evolution of natural language processing: from rule-based systems to multilingual transformers

From early rule-based systems (Weizenbaum et al., 1966) to statistical models utilizing n-grams and Hidden Markov Models (Jelinek et al., 1997), natural language processing (NLP) has advanced to machine learning with feature engineering, such as SVMs and CRFs (Joachims, 1998). Deep learning and word embeddings such as Word2Vec and GloVe (Mikolov et al., 2013; Pennington et al., 2014) provided a significant breakthrough by allowing for richer semantic representations. Since 2018, transformer-based models such as BERT and GPT (Vaswani et al., 2017; Devlin et al., 2019) have dominated, allowing for transfer learning and multilingual NLP. Models like IndicBERT and MuRIL (Kakwani et al., 2020; Khanuja et al., 2021) are essential for making e-commerce, education, and governance more inclusive through Devanagari and other regional scripts in India, where over 57% of internet users speak Indic languages (et al., IAMAI, 2023).

Research Gap and Motivation

Although transformer-based models have demonstrated remarkable success in English and other high-resource languages, practical implementations for Indic scripts remain limited in business applications such as e-commerce. This gap motivates the present study, which explores the application of NLP with transfer learning in Devanagari script to enhance customer interaction, sentiment analysis, and complaint resolution in the Indian e-commerce sector.

Devanagari Script

The Devanagari script, an abugida with 47 primary characters (14 vowels and 33 consonants), is widely used for languages like Hindi, Marathi, Nepali, and Sanskrit. Written left to right with the distinctive shirorekha (headline), it is one of the most important writing systems in South Asia. Hindi in Devanagari is the third most spoken language globally after English and Mandarin (Census of India et al., 2011; Ethnologue et al., 2022), making its integration into Natural Language Processing (NLP) essential. However, challenges such as ligatures, rich morphology, and spelling variations complicate tokenization and analysis.

From Text to Intelligence: The NLP Workflow

• Text Preprocessing – Cleaning, tokenization, stop-word removal, stemming/lemmatization.
• Feature Extraction entails converting text data into numerical representations like Bag-of-Words, TF-IDF, or word embedding’s.
• Modeling – Applying machine learning or deep learning models for tasks like classification or translation.
• Evaluation – Measuring performance using metrics (accuracy, F1-score, BLEU, etc.).
• Deployment – Integrating NLP models into real-world applications.

NLP in Devanagari Script for E-Commerce

With the exponential growth of digital commerce in India, a significant share of customer interactions—such as reviews, search queries, and complaints—are expressed in regional languages, predominantly Hindi in the Devanagari script. Traditional AI systems in e-commerce, however, are designed mainly for English, resulting in missed insights, poor user experiences, and inefficient complaint resolution.

ApnaBazaar, a leading Indian e-commerce company, receives thousands of product reviews, search queries, and customer complaints in Hindi (Devanagari script). However, since most AI systems were originally designed for English, the platform faced critical challenges such as missed insights from Hindi reviews, poor search experiences for customers typing in Devanagari, and delayed responses to customer complaints. To overcome these limitations, ApnaBazaar adopted Natural Language Processing (NLP) with transfer learning techniques to make its platform more Hindi-friendly and multilingual-aware.

This shift is strategically important as India’s e-commerce market already exceeds US$60B in e-retail GMV and continues to expand rapidly (Bain, 2023). With nearly 900 million internet users, a majority of whom prefer Indic languages, enabling NLP for Hindi/Devanagari is critical to unlocking customer value (India Brand Equity Foundation, 2023). By leveraging NLP, ApnaBazaar enhances search accuracy, sentiment analysis, and complaint handling, thereby boosting conversion rates, customer satisfaction, and operational efficiency—particularly in fast-growing Tier-2 and Tier-3 markets.

NLP with Transfer Learning (IndicBERT & mBERT)

A model learned on a big dataset can be adjusted for a smaller, more focused task thanks to transfer learning. As a result, NLP algorithms become quicker, more precise, and particularly useful for Hindi, where there is a dearth of labeled data. While IndicBERT is especially made for Indian languages, mBERT (Multilingual BERT) is trained on more than 100 languages. These pre-trained models can be refined for tasks like sentiment analysis, search query comprehension, and complaint classification using transfer learning on Hindi text (Devanagari script). As a result, even with little Hindi training data, accurate NLP solutions are possible.

Workflow

• Preprocessing – Normalize Hindi script, handle spelling variations.
• Embedding – Use pretrained models (IndicBERT, fastText, mBERT) trained on large Hindi corpora.
• Fine-Tuning – Adapt these models with e-commerce-specific data (reviews, queries, complaints).
• Classification & Recommendation – Classify sentiment, extract attributes (color, size, product type), and improve product search.
• Output – Personalized shopping experience in Hindi with faster complaint resolution.

Hindi to Insights: How NLP + Transfer Learning Powers E-commerce

Conclusion

ApnaBazar’s use of NLP with IndicBERT and mBERT transformed Hindi-language search, reviews, and complaints into actionable insights, leading to better product discovery, faster service, and stronger customer satisfaction. Looking ahead, expanding this solution to more Indian languages will unlock new growth opportunities and deepen market penetration in Tier-2 and Tier-3 cities.

Suggested Activity:

• Compare ApnaBazaar’s Hindi-friendly approach with an English-only e-commerce interface.
• Brainstorm additional future enhancements like voice-based Hindi search,

Sample Questions:

1. How does enabling NLP in Hindi (Devanagari) improve customer experience in e-commerce?
2. What role does transfer learning (IndicBERT, mBERT) play in making NLP applications feasible for regional languages?
3. What challenges might arise when scaling NLP for other Indian languages beyond Hindi?
4. In what ways does improving complaint resolution in Hindi impact customer trust and retention?

References

1. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL-HLT, 4171–4186.
2. IAMAI. (2023). Digital India Report 2023. Internet and Mobile Association of India.
3. Jelinek, F. (1997). Statistical methods for speech recognition. MIT press.
4. Joachims, T. (1998). Text categorization with Support Vector Machines. Proceedings of ECML, 137–142.
5. Kakwani, D., Kunchukuttan, A., Golla, S., … & Khapra, M. (2020). IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages. Findings of EMNLP 2020, 4948–4961.
6. Khanuja, S., Bansal, D., Mehta, S., & Shrivastava, M. (2021). MuRIL: Multilingual Representations for Indian Languages. arXiv preprint arXiv:2103.10730.
7. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. ICLR.
8. Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global vectors for word representation. EMNLP, 1532–1543.
9. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. NeurIPS, 5998–6008.
10. Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45.
11. Bain & Company. (2023). How India Shops Online 2023. Bain & Company. Retrieved from https://www.bain.com
12. India Brand Equity Foundation (IBEF). (2023). E-commerce Industry in India. IBEF. Retrieved from https://www.ibef.org/industry/ecommerce