Psychologically Informed Instagram Marketing Analytics Pipeline Using Funnel Metrics and Multi-Criteria Decision Analysis: A Daycare Case Study
DOI:
https://doi.org/10.35870/ljit.v4i2.8504Keywords:
Instagram Marketing Analytics; Funnel Metrics; CRITIC; TOPSIS; Weight Sensitivity Analysis; Daycare Case StudyAbstract
Micro-scale social media accounts often lack sufficient observations for predictive analytics but still require defensible content and campaign decisions. This design-science proof of concept develops an Instagram marketing analytics pipeline combining funnel ratios, CRITIC weighting, TOPSIS ranking, and full-range weight sensitivity analysis (FRWSA). The pipeline was applied to ten complete-case organic posts and three advertising campaigns from an Indonesian Islamic Montessori daycare account. For organic posts, CRITIC assigned weights of 0.2764 to reach efficiency, 0.3887 to engagement rate, and 0.3349 to follow conversion. TOPSIS ranked the Thursday 04:02 post first with a closeness coefficient of 0.7503, while FRWSA found it dominant in 59.74% of 231 weight vectors. For advertising campaigns, equal weights were used because correlation-based weighting was unsuitable for only three alternatives. The 22–27 November 2025 campaign achieved a TOPSIS score of 1.0000 and ranked first across all 1,771 weight vectors, reflecting Pareto dominance rather than causality. Psychological and consumer-behaviour theory informs the ordering of criteria by behavioural commitment, but no psychological state is measured or inferred. The study contributes a transparent decision-support pipeline for data-scarce social media accounts.
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