Optimizing Fertilizer Dealer Performance through a Transaction-Based Dealer Performance Index and Market Segmentation Evidence from FACT Dealers in Tamil Nadu, India

Fertilizer dealers are a critical last-mile link between manufacturers and farmers, yet dealer evaluation often relies on sales turnover alone and therefore overlooks differences in transaction intensity, product breadth and continuity of market activity. The purpose of this study is to optimize fertilizer dealer performance by developing a transaction-based Dealer Performance Index (DPI) and identifying statistically distinct dealer segments among Fertilisers and Chemicals Travancore Limited (FACT) dealers in Tamil Nadu, India. FACT transaction records for 2019–2024 were reviewed, while the empirical segmentation uses the verified 2024 active frame. The 2024 database contains 9,129 transactions, 874 active dealers across 38 districts and 113,715.30 metric tonnes of fertilizer sales. A proportionate district-by-dealer-type sample of 274 dealers was drawn, comprising 165 retailers and 109 wholesalers. Dealer-level indicators were total quantity, transaction frequency, product breadth and active months; the DPI combined min–max normalized indicators with equal weights, and K-means solutions from two to six clusters were compared using silhouette, Calinski–Harabasz and Davies–Bouldin criteria. A two-cluster solution provided the clearest parsimonious structure. The lower-activity segment contained 122 dealers with a mean DPI of 16.96, whereas the higher-activity segment contained 152 dealers with a mean DPI of 47.92. DPI differed strongly across clusters (F = 417.24, p < .001, η² = .605). Wholesalers also recorded a higher mean DPI than retailers (41.97 versus 28.96; Welch t = 5.33, p < .001). The findings show that transaction-based segmentation provides a more actionable basis for differentiated dealer development, service support and distribution planning than undifferentiated sales ranking.

fertilizer; dealers; segmentation; performance; distribution; agribusiness; clustering; Tamil Nadu

1. Anderson, E., & Oliver, R. L. (1987). Perspectives on behavior-based versus outcome-based salesforce control systems. Journal of Marketing, 51(4), 76–88. https://doi.org/10.1177/002224298705100407 [Google Scholar] [Crossref]

2. Anderson, J. C., & Narus, J. A. (1990). A model of distributor firm and manufacturer firm working partnerships. Journal of Marketing, 54(1), 42–58. https://doi.org/10.1177/002224299005400103 [Google Scholar] [Crossref]

3. Anisimova, T., & Mavondo, F. T. (2014). Alignment between manufacturer brand and dealer corporate brands. Journal of Business & Industrial Marketing. [Google Scholar] [Crossref]

4. Arodha, D. (2024). Optimizing strategy of subsidized fertilizer distribution to POKTAN (Farmer Groups). Idarotuna: Journal of Administrative Science, 5(2). https://doi.org/10.54471/idarotuna.v5i2.111 [Google Scholar] [Crossref]

5. Barker, N., & Sharma, P. (2023). An investigation on the distribution channels and demand analysis for National Fertilizers Limited (NFL) products in Sri Ganganagar District of Rajasthan, India. Asian Journal of Agricultural Extension, Economics & Sociology, 41(9). https://doi.org/10.9734/ajaees/2023/v41i92028 [Google Scholar] [Crossref]

6. Barrett, C. B., Reardon, T., Swinnen, J., & Zilberman, D. (2022). Agri-food value chain revolutions in low- and middle-income countries. Journal of Economic Literature, 60(4), 1316–1377. https://doi.org/10.1257/jel.20201539 [Google Scholar] [Crossref]

7. Biondi, S., Calabrese, A., Capece, G., Costa, R., & Di Pillo, F. (2013). A new approach for assessing dealership performance: An application for the automotive industry. Industrial Management & Data Systems, 113(8), 1138–1154. https://doi.org/10.1108/IMDS-01-2013-0020 [Google Scholar] [Crossref]

8. Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1–27. https://doi.org/10.1080/03610927408827101 [Google Scholar] [Crossref]

9. Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227. https://doi.org/10.1109/TPAMI.1979.4766909 [Google Scholar] [Crossref]

10. Dolnicar, S., Grün, B., & Leisch, F. (2018). Market segmentation analysis: Understanding it, doing it, and making it useful. Springer. https://doi.org/10.1007/978-981-10-8818-6 [Google Scholar] [Crossref]

11. Elakkiya, S., & Asokhan, M. (2021). Role and performance of agri-input dealers in extension services in Coimbatore district of Tamil Nadu, India. Journal of Applied and Natural Science, 13(SI), 139–145. https://doi.org/10.31018/jans.v13iSI.2819 [Google Scholar] [Crossref]

12. Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis (5th ed.). Wiley. [Google Scholar] [Crossref]

13. Fertiliser Association of India. (2025). Fertiliser and agriculture statistics: Southern region 2024–25. Fertiliser Association of India. [Google Scholar] [Crossref]

14. Gassenheimer, J. B., Calantone, R. J., & Scully, J. I. (1995). Supplier involvement and dealer satisfaction: Implications for enhancing channel relationships. Journal of Business & Industrial Marketing. [Google Scholar] [Crossref]

15. Gaurav, N., & Bhandari, A. (2016). Potential for dealers in Indian fertilizer industry: A case study. South Asian Journal of Business and Management Cases. https://doi.org/10.1177/2455265820160106S [Google Scholar] [Crossref]

16. Gloy, B. A., & Akridge, J. T. (1999). Segmenting the commercial producer market for agricultural inputs. American Agricultural Economics Association Annual Meeting. https://doi.org/10.22004/ag.econ.21592 [Google Scholar] [Crossref]

17. Gokulakrishna, P., Murugananthi, D., Chandrakumar, M., Parimala, D. R., & Gangai Selvi, R. (2024). Farmers buying behaviour towards secondary nutrients fertilizers in southern Tamil Nadu. Plant Science Today. https://doi.org/10.14719/pst.5686 [Google Scholar] [Crossref]

18. Goyal, A. (2010). Information, direct access to farmers, and rural market performance in Central India. American Economic Journal: Applied Economics, 2(3), 22–45. https://doi.org/10.1257/app.2.3.22 [Google Scholar] [Crossref]

19. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage. [Google Scholar] [Crossref]

20. Isyanto, P. (2024). Optimizing fertilizer distribution performance, in supporting food security programs. International Journal of Religion. https://doi.org/10.61707/b9n4v612 [Google Scholar] [Crossref]

21. Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011 [Google Scholar] [Crossref]

22. K. R., & Uma Maheswari. (2024). Agro retailer's opinion on 'Bharat' - One Nation, One Fertilizer scheme. Salud, Ciencia y Tecnología - Serie de Conferencias. https://doi.org/10.56294/sctconf2024696 [Google Scholar] [Crossref]

23. Kaplan, R. S., & Norton, D. P. (1996). The balanced scorecard: Translating strategy into action. Harvard Business School Press. [Google Scholar] [Crossref]

24. Ketchen, D. J., Jr., & Shook, C. L. (1996). The application of cluster analysis in strategic management research: An analysis and critique. Strategic Management Journal, 17(6), 441–458. https://doi.org/10.1002/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G [Google Scholar] [Crossref]

25. Kotler, P., & Keller, K. L. (2016). Marketing management (15th ed.). Pearson. [Google Scholar] [Crossref]

26. Kusuma, I. P., Suryani, E., & Siswanto, N. (2023). Optimizing the subsidized fertilizer distribution from Line I warehouse to Line IV warehouse at PT Pupuk Sriwidjaja Palembang in Central Java. Journal of Pharmaceutical Negative Results, 14. https://doi.org/10.52783/tjjpt.v44.i3.707 [Google Scholar] [Crossref]

27. Ladumor, D., Pundir, R. S., & Rajwadi, A. (2023). Farmers purchasing behaviour and satisfaction towards fertilizer in Kheda District. Asian Journal of Agricultural Extension, Economics & Sociology, 41(9). https://doi.org/10.9734/ajaees/2023/v41i92026 [Google Scholar] [Crossref]

28. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281–297). University of California Press. [Google Scholar] [Crossref]

29. Manasa, K., Jirli, B., Sen, S., Saifuddin, M. D., & Surya, K. T. (2025). Assessing agricultural input dealers' constraints and farmers' perception of services through SERVPERF model analysis. Agricultural Science Digest. https://doi.org/10.18805/ag.d-6201 [Google Scholar] [Crossref]

30. Mathuabirami, V., Karthikeyan, C., Mansingh, J. P., Ihou, A. F. Y., Malliah, N. D., & Ganesan, D. (2025). Assessment of service quality and farmers’ satisfaction of extension and advisory services received from agro-input dealers during COVID-19 in India. Multidisciplinary Science Journal. https://doi.org/10.31893/multiscience.2025191 [Google Scholar] [Crossref]

31. Mohr, J. J., Fisher, R. J., & Nevin, J. R. (1996). Collaborative communication in interfirm relationships: Moderating effects of integration and control. Journal of Marketing, 60(3), 103–115. https://doi.org/10.1177/002224299606000307 [Google Scholar] [Crossref]

32. Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38. https://doi.org/10.1177/002224299405800302 [Google Scholar] [Crossref]

33. Najibullah. (2013). Fertiliser consumption and marketing in India: An economic analysis [Doctoral dissertation]. ICAR-National Dairy Research Institute/Krishikosh. [Google Scholar] [Crossref]

34. Narasimhan, R., Talluri, S., & Mendez, D. (2001). Supplier evaluation and rationalization via data envelopment analysis: An empirical examination. Journal of Supply Chain Management, 37(3), 28-37. [Google Scholar] [Crossref]

35. Narayanan, T. J. (2021). Marketing behaviour of agro-input retailers—A critical analysis. International Journal of Current Microbiology and Applied Sciences. [Google Scholar] [Crossref]

36. Panja, A., Gowda, N. S. S., Kusumalatha, D. V., & Jayasingh, D. K. (2022). Role performance of agricultural input dealers in agro-advisory services in West Bengal. Indian Journal of Extension Education, 58(3). https://doi.org/10.48165/ijee.2022.58302 [Google Scholar] [Crossref]

37. Punj, G., & Stewart, D. W. (1983). Cluster analysis in marketing research: Review and suggestions for application. Journal of Marketing Research, 20(2), 134–148. https://doi.org/10.1177/002224378302000204 [Google Scholar] [Crossref]

38. Rahma, I., Arhandi, P. P., & Firdausi, A. T. (2020). Application of hierarchical clustering and K-means clustering to group potential sales locations. Jurnal Informatika Polinema, 6(1). https://doi.org/10.33795/jip.v6i1.287 [Google Scholar] [Crossref]

39. Rajagopal. (2008). Effects of customer services efficiency and market effectiveness on dealer performance. International Journal of Retail & Distribution Management, 36(2), 109–125. https://doi.org/10.1108/09590550810853099 [Google Scholar] [Crossref]

40. Rijaludin, S. H., & Ratnasari, V. (2025). Key factors influencing distributor purchase decisions for NPK Phonska Plus fertilizer. Jurnal Ilmiah Manajemen Kesatuan, 13(1). https://doi.org/10.37641/jimkes.v13i1.3069 [Google Scholar] [Crossref]

41. Rosenbloom, B. (2012). Marketing channels (8th ed.). Cengage Learning. [Google Scholar] [Crossref]

42. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7 [Google Scholar] [Crossref]

43. Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill. [Google Scholar] [Crossref]

44. Saklani, T., Shilpa, B., & Shirur, M. (2026). An analysis of products and services delivered by agri-input dealers trained under DAESI. Journal of Agricultural Extension Management, 26(1). https://doi.org/10.56093/jaem.v26i1.5 [Google Scholar] [Crossref]

45. Sharma, D., Sahay, B. S., & Sachan, A. (2004). Modelling distributor performance index using system dynamics approach. Journal of Modelling in Management. [Google Scholar] [Crossref]

46. Siva Balan, K. C., Umadikar, J., Prashant, S., Sangeetha, U., & Prasad, A. K. (2015). Agro-market information and farmers' market linkage: Way forward. SSRN. https://doi.org/10.2139/SSRN.2618963 [Google Scholar] [Crossref]

47. Smith, W. R. (1956). Product differentiation and market segmentation as alternative marketing strategies. Journal of Marketing, 21(1), 3–8. https://doi.org/10.1177/002224295602100102 [Google Scholar] [Crossref]

48. Tsai, C.-F., Hu, Y.-H., & Lu, Y.-H. (2015). Customer segmentation issues and strategies for an automobile dealership with two clustering techniques. Expert Systems with Applications. [Google Scholar] [Crossref]

49. Ward, J. H., Jr. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. https://doi.org/10.1080/01621459.1963.10500845 [Google Scholar] [Crossref]

50. Wella, Y. E., Okfalisa, O., Insani, F., Saeed, F., & Hussin, A. R. C. (2023). Service quality dealer identification: The optimization of K-means clustering. SINERGI. [Google Scholar] [Crossref]

51. Zavale, H., Matchaya, G., Vilissa, D., Nhemachena, C., Nhlengethwa, S., & Wilson, D. (2020). Dynamics of the fertilizer value chain in Mozambique. Sustainability, 12(11), 4691. https://doi.org/10.3390/su12114691 [Google Scholar] [Crossref]

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