Algorithmic microsegmentation model for vote capture in Ecuador
DOI:
https://doi.org/10.59169/pentaciencias.v8i4.1916Keywords:
Algorithmic microsegmentation; political marketing; segmentation; campaigning; votingAbstract
Algorithmic voter microsegmentation combines large-scale data processing with machine learning techniques to sort citizens into internally homogeneous collectives, moving beyond the conventional demographic approach. Objective: To build a microsegmentation model based on k-means that identifies differentiated voter profiles in Ecuador and supports vote capture. Method: A quantitative, cross-sectional and descriptive-explanatory design was adopted. The sample was set on the 2026 national electoral registry (N = 13,331,335), with 95 % confidence, 5 % error and 385 resulting surveys; the instrument reached content validity (Aiken's V = 0.95) and high overall reliability (Cronbach's alpha = 0.881). The analysis relied on k-means, z-score standardization, the elbow method and the Silhouette coefficient. Results: The algorithm sorted voters into two segments differing significantly across the six psychographic dimensions and in vote intention and participation (H = 86.851; p < 0.001): passive-conservative (59.7 %) and receptive-digital (40.3 %), with a positive moderate-strength association with vote capture (ρ = 0.561). Conclusions: The model offers an actionable input for crafting messages, choosing channels and allocating campaign resources within the ethical and legal framework of data protection; the null hypothesis is rejected and differentiated, usable electoral profiles for vote capture are confirmed.
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