Citation:

Ajinkya P. Chatur, Shiragi S. Pattalwar, 2024. Enhancing Customer Segmentation with Azure Cognitive Services, ESP Journal of Engineering & Technology Advancements 4(4): 7-12.

Abstract:

In the field of marketing, customer segmentation is crucial for targeted work and improved customer engagement. Traditional customer segmentation mainly focuses on age, gender, location etc., which mainly misses the emotional context of customer feedback. To improve the efficiency of this segmentation, we are using Azure Cognitive Services’ sentiment analysis. This paper dwells into implementation details, outcomes and analysis. We found that sentiment analysis can substantially enhance precision and accuracy of segmentation, leading to more effective marketing approach.

References:

[1] Bailey, C., Baines, P. R., Wilson, H., & Clark, M. (2009). Segmentation and customer insight in contemporary services marketing practice: why grouping customers is no longer enough.

[2] Journal of Marketing Management, 25(3–4), 227–252. https://doi.org/10.1362/026725709X429737

[3] Das, S., Nayak, J. (2022). Customer Segmentation via Data Mining Techniques: State-of-the-Art Review. In: Nayak, J., Behera, H., Naik, B., Vimal, S., Pelusi, D. (eds)

[4] Computational Intelligence in Data Mining. Smart Innovation, Systems and Technologies, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-9447-9_38

[5] Ş. Ozan, "A Case Study on Customer Segmentation by using Machine Learning Methods," 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey, 2018, pp. 1-6, doi: 10.1109/IDAP.2018.8620892.

[6] Boyu Shen. 2021. E-commerce Customer Segmentation via Unsupervised Machine Learning. In The 2nd International Conference on Computing and Data Science (CONF-CDS 2021). Association for Computing Machinery, New York, NY, USA, Article 45, 1–7. https://doi.org/10.1145/3448734.3450775

[7] Ranjan, A., & Kumar, P. (2022). Machine learning in sentiment-based customer segmentation: A case study of retail. Journal of Retailing and Consumer Services, 65, 102895. Ranjan, A., & Kumar, P. (2022). Machine learning in sentiment-based customer segmentation: A case study of retail. Journal of Retailing and Consumer Services, 65, 102895.

[8] Smith, W. R. (1956). Product differentiation and market segmentation as alternative marketing strategies. Journal of Marketing, 21(1), 3-8.

[9] Wedell, M., & Vasileiou, K. (2005). Psychological constructs in consumer research: A review of the literature. Journal of Consumer Research, 32(2), 234-243.

[10] Kumar, V., & Reinartz, W. (2016). Creating Enduring Customer Value. Harvard Business Review Press.

[11] Wang, Y., & Li, L. (2017). Emotion-based marketing: The importance of emotional intelligence in customer engagement. Journal of Marketing Research, 54(3), 429-442.

[12] Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1-135.

[13] Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (pp. 2-7).

[14] Chaturvedi, A., & Sharma, D. (2019). Predicting consumer behavior using sentiment analysis: A study of online reviews. International Journal of Information Management, 47, 227-236.

[15] Mishra, A., & Sharma, S. (2020). Enhancing brand management through sentiment analysis of social media: A case study. Journal of Brand Management, 27(4), 462-477.

[16] González, J., & Jorrín, J. (2019). Sarcasm detection in social media: A survey. Information Processing & Management, 56(3), 512-530.

[17] Green, S., & Marlon, C. (2023). Extracting actionable insights with Azure Cognitive Services: A framework for business decision-making. Journal of Business Analytics, 6(1), 14-25.

[18] Anderson, P., & Lee, J. (2022). Overcoming challenges in technology adoption: The case of Azure Cognitive Services. Journal of Business Research, 132, 128-138.

[19] Gupta, R., & Kumar, V. (2021). The impact of sentiment analysis on marketing effectiveness: Evidence from customer segmentation. Marketing Intelligence & Planning, 39(2).

[20] Microsoft Azure, “Azure Cognitive Services Documentation,” Available: https://azure.microsoft.com/en-us/services/cognitive-services/.

Keywords:

Azure Cognitive Services, Sentiment Analysis, Customer Segmentation, Personalization, Business Analytics, Natural Language Processing.