Predictive Analytics for Demand Forecasting in the FMCG Sector: A Study of Consumer Purchase Patterns in Bengaluru

Ashwin Kumar R; CHARITHRA C M

doi:10.36948/ijfmr.2026.v08i04.83513

Predictive Analytics for Demand Forecasting in the FMCG Sector: A Study of Consumer Purchase Patterns in Bengaluru

Author(s)	Mr. Ashwin Kumar R, Dr. CHARITHRA C M
Country	India
Abstract	In the present study, the authors investigate and assess the importance of predictive analytics in predicting the demand of Fast-Moving Consumer Goods (FMCG) with the analysis of consumer buying behavior using the urban consumers in Bengaluru. The primary data collected by the researcher were obtained by using a structured questionnaire from 110 respondents aged between 18 to 45 years, the monthly quantity of FMCG purchase served as the dependent variable while 5 consumer side demand drivers namely seasonality, promotional offers, price sensitivity, income effect, and product availability were the independent variables. Data analysis was conducted by using descriptive statistics, factor analysis, correlation analysis and multiple regression in Microsoft Excel (with XLSTAT add-on) and it was presented using Power BI dashboards with an ANOVA analysis. Five variables were identified as determinants of the demand: internal locus of control, entrepreneurial motivation, goal orientation, perceived behavioral control, and satisfaction with current working status. Factor analysis showed that these five variables load on a single component (Cronbach’s Alpha = 0.797) and the correlation analysis revealed no indications of multicollinearity among the predictors. The overall regression model was statistically significant at R² = 0.163, Adjusted R2 = 0.123, F = 4.051 (p = 0.002), where seasonality was the only variable with a statistically significant separate effect on purchase quantity (p = 0.034). The researchers found that the purchase amount was not significantly affected by promotional offers, price sensitivity, or the income effect at the 0.05 level, or product availability at the 0.01 level. The study results suggest that festive and seasonal cycles are the most significant on the consumer side for FMCG demand and that a FMCG purchase behaviour regression-based model derived from consumer survey data can meaningfully predict consumer purchase behaviour, providing a cost-effective, consumer-centric alternative to the traditional technique of transaction-based forecasting for FMCG companies.
Keywords	Predictive analytics, demand forecasting, FMCG sector, consumer purchase patterns, multiple regression, seasonality, Bengaluru
Field	Business Administration
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-11
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.83513

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Predictive Analytics for Demand Forecasting in the FMCG Sector: A Study of Consumer Purchase Patterns in Bengaluru

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