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International Journal of Innovations & Research Analysis (IJIRA) [ Vol. 6 | No. 3(I) | July - September, 2026 ]

Determinants of Customer Churn and Retention in the IT Industry: An Empirical Analysis

Arunmozhi P & Dr. Sudha S

Customer retention has emerged as an important issue for information technology (IT) firms as customers are able to compare providers, services offered and price quotes rather easily. The current paper investigates the relationship between customer engagement, service quality and perception about pricing and customer churn and customer retention in the IT industry. The research is conducted based on primary data gathered through a structured survey in the form of a five-point Likert scale questionnaire and analyzed with SPSS software. In total, the sample size comprised 279 respondents after removing invalid observations. The reliability test showed the Cronbach’s Alpha of 0.982 for 40 items, which demonstrates extremely high reliability of data. According to descriptive statistics, the sample expressed positive attitudes towards customer engagement, service quality and pricing, as well as strong intentions to retain their business with the IT company. Pearson correlations among five research variables ranged from 0.909 to 0.935 and were statistically significant at p < 0.001. Multiple regression found that customer engagement (β = 0.205), service quality (β = 0.401) and pricing perception (β = 0.256) had a significant impact on customer retention whereas customer churn was insignificant (p = 0.062). The model explained 89.8% of the variance in customer retention.

Arunmozhi, P. & Sudha, S. (2026). Determinants of Customer Churn and Retention in the IT Industry: An Empirical Analysis. International Journal of Innovations & Research Analysis, 06(03(I)), 112–118. https://doi.org/10.62823/IJIRA/06.3(I).9339
  1. Ambuli, T. V., Venkatesan, S., Ramu, V., & Devi, K. (2025, November). Predictive Analytics Framework for Strategic Decision-Making in Retail and Customer Churn Management. In 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN) (pp. 1-6). IEEE.
  2. Berry, S. (2025). An experiment in price perception error. ArXiv
  3. Chi, H.-K., & Phan, H.-T. (2025). Revealing the role of corporate social responsibility, service quality, and perceived value in determining customer loyalty: A meta-analysis study. Sustainability, 17(10), 4304.
  4. Desveaud, K., Mandler, T., & Eisend, M. (2024). A meta-model of customer brand loyalty and its antecedents. Journal of Business Research, 176, 114589.
  5. Divya, D., Savita, S., & Kaur, S. (2025). Unveiling excellence in Indian healthcare: a patient-centric PRISMA analysis of hospital service quality, patient satisfaction and loyalty. International Journal of Pharmaceutical and Healthcare Marketing, 19(3), 874-914.
  6. Gupta, Y., & Khan, F. M. (2024). Role of artificial intelligence in customer engagement: a systematic review and future research directions. Journal of Modelling in Management, 19(5), 1535-1565.
  7. Li, I. (2024). Analyzing Member Churn and Retention at CPM Credit Union.
  8. Lim, W. M., Saha, V., & Das, M. (2025). From service failure to brand loyalty: Evidence of service recovery paradox. Journal of Brand Management, 32, 257–281.
  9. Praba, R. S., Suganthi, P., Samraj, J. H., Arthi, V. K., & Bhuvaneswari, M. (2025, November). AI-Powered Customer Churn Prediction in Banking with R-based Gradient Boosting Models and Power BI Dashboards. In 2025 IEEE First International Conference on Innovations in Engineering and Next-Generation Technologies for Sustainability (ICINVENTS) (Vol. 1, pp. 1-8). IEEE.
  10. Rahman, S. M., Carlson, J., Gudergan, S. P., Wetzels, M., & Grewal, D. (2025). How do omnichannel customer experiences affect customer engagement? Theory and empirical validation. Journal of Business Research, 189, 115196.
  11. Rasheed, R., & Rashid, A. (2024). Role of service quality factors in word of mouth through student satisfaction. Kybernetes, 53(9), 2854-2870.
  12. Ribeiro, H., Barbosa, B., Moreira, A. C., & Rodrigues, R. G. (2024). Determinants of churn in telecommunication services: a systematic literature review. Management Review Quarterly, 74(3), 1327-1364.
  13. Roy, S. K., Singh, G., Sadeque, S., Harrigan, P., & Coussement, K. (2023). Customer engagement with digitalized interactive platforms in retailing. Journal of Business Research, 164, 114001.
  14. Schemm, J., Schwarz, C., & Strickrodt, M. (2026). Geiger Uses Machine Learning to Reduce Customer Churn in the Promotional Products Industry. INFORMS Journal on Applied Analytics.
  15. Srivastava, R., Gupta, P., Kumar, H., & Tuli, N. (2025). Digital customer engagement: A systematic literature review and research agenda. Australian Journal of Management, 50(1), 220-245.
  16. Subagja, A. D., Ausat, A. M. A., Sari, A. R., Wanof, M. I., & Suherlan, S. (2023). Improving customer service quality in MSMEs through the use of ChatGPT. Jurnal Minfo Polgan, 12(1), 380-386.
  17. Sumanasri, A., Vyshnavi, M., Manogna, R. N., Keerthi, K., Kesavarshini, A., & Pande, S. D. (2023, November). Predictive Insights for Enhancing Customer Retention in Banking: Integrating Power BI and Random Forest. In International Conference on Machine Vision and Augmented Intelligence (pp. 389-397). Singapore: Springer Nature Singapore.
  18. Tedja, B., Al Musadieq, M., Kusumawati, A., & Yulianto, E. (2024). Systematic literature review using PRISMA: exploring the influence of service quality and perceived value on satisfaction and intention to continue relationship. Future Business Journal, 10(1), 39.
  19. Zeinali, M., Ramezani Asli, L., & Khalili, M. A. (2026). Integrating Business Intelligence and CRM Systems With a Machine Learning Approach for Predictive Customer Retention in E‐Commerce. The Scientific World Journal, 2026(1), 1946904.
  20. Zietsman, M. L., Mostert, P., & Svensson, G. (2023). Precursors and outcomes of perceived value in B2B banking services: A nomological framework. Journal of Relationship Marketing, 22(4), 330–353.

DOI:

Article DOI: 10.62823/IJIRA/06.3(I).9339

DOI URL: https://doi.org/10.62823/IJIRA/06.3(I).9339


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