An Empirical Investigation into Seasonal Demand and Revenue Variability at Multiple Super markets in Guntur

Author

Veerapaneni Akhil, M. Raja Pavan Kumar

Abstract

 This study examines seasonal demand and revenue variability across supermarkets in Guntur, Andhra Pradesh. Organised retail in semi-urban India is exposed to festival seasons, agricultural income cycles, wedding seasons and school reopening, which make revenue fluctuate through the year. Using a descriptive, cross-sectional, quantitative design, the study analyses primary questionnaire data from 115 consumers through percentage analysis, a Seasonal Demand Index (SDI) and six chi-square tests. Of the respondents, 93.9% spend more during festivals and special occasions, by about 22.5% on average (plausible range 14.4%–30.8%) (χ² = 92.78, p < 0.001). Sankranti, Dasara and Diwali are named by 69.6% as the most influential occasions (χ² = 24.57, p < 0.001), although the share of respondents raising purchases does not differ significantly across the five seasons and occasions (χ² = 3.74, p = 0.44); festivals nevertheless record the highest SDI (135, where 100 is average). Discounts and promotions are named by 37.4% as the biggest driver of changes in sales (χ² = 33.30, p < 0.001). Older respondents report larger festival-time increases (p = 0.008), although this is not significant among Guntur residents alone, while satisfaction does not differ significantly across supermarket types. Overall, 49.6% are satisfied or very satisfied with supermarkets’ ability to meet higher demand and 11.3% are dissatisfied. The paper recommends festival-calendar-based inventory planning, deliberate promotion planning and close monitoring of product availability.

Keywords

seasonal demand, revenues, grocery retail, seasonal variations, supermarkets, festival spending, consumer behaviour, Guntur, chi-square test

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References

Cochran, W. G. (1954). Some methods for strengthening the common χ² tests. Biometrics, 10(4), 417–451.

Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2nd ed.). Lawrence Erlbaum Associates.

Cramér, H. (1946). Mathematical methods of statistics. Princeton University Press.

Dharani, S. K., & Praveen, S. V. (2023). A study of consumer buying behaviour towards supermarkets.

Haberman, S. J. (1973). The analysis of residuals in cross-classified tables. Biometrics, 29(1), 205–220.

Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.

Jeyachandran. (2019). Grocery shopping pattern of Indian retail customers: Traditional stores vs. supermarkets.

Kothari, C. R. (2004). Research methodology: Methods and techniques (2nd rev. ed.). New Age International Publishers.

Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, Series 5, 50(302), 157–175.

Raguraman. (2013). A seasonal approach for analysis of temporal trends in retail marketing using association rule mining. International Journal of Computer Applications, 71(13), 10–15.

Ranjan, & Singh. (2025). A study on consumer behaviour towards organised retail stores in Bhopal city.

Shetye. (2026). A study on market basket analysis of consumer buying behaviour with reference to D-Mart.

Wilson, E. B. (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22(158), 209–212.

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