The reliability and validity of the results can be jeopardized by techniques like deletion and imputation of the simple mean when there is a high percentage of missing data, which presents a significant challenge to decision and estimation theory. This challenge becomes more challengeable if population suffers with heterogeneity and the parameters of auxiliary variable are unknown because they are used to improvise the estimation by suggesting the improved estimators. In this paper, for a population with observed heterogeneity, we propose new exponential estimators to estimate the mean of the study variable using auxiliary variables when survey variables suffer from non-ignorable missing data at two sampling phases. To verify the efficacy of the suggested estimators, a number of pertinent and promising estimators have been modified in this context; the text offers the theoretical constraints of the comparative analysis along with the mathematical expressions of these estimators’ bias and mean square error . The suggested estimators’ performance has been mechanized through a numerical analysis using statistical packages of R-software on both real and simulated data-sets (symmetric and asymmetric), validating the theoretical results and their significance.
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