Generative artificial intelligence personalization and consumer behavior analytics

Abstract

Batik micro, small and medium enterprises (MSMEs) sustain Indonesia's textile heritage and local livelihoods but struggle with customer retention due to conventional marketing and limited artificial intelligence (AI) adoption. The shift to digital commerce requires affordable, culturally relevant AI strategies. This study proposes an integrated framework combining recency, frequency, monetary (RFM) analytics, K-means clustering (K = 5), random forest churn prediction, and transformer-based generative AI for personalized promotions delivered via WhatsApp business API. Evaluated over 12 months with 20 batik MSMEs in Central Java (300 customers, 873 transactions), the framework employed temporal data splitting (9 months training, 3 months testing) and A/B testing against generic campaigns. Human evaluation assessed message relevance and cultural appropriateness, while all data were anonymized with informed consent. Results show improvements of +17% in retention prediction accuracy, +23% in click-through rate, and +21% in purchase frequency versus baselines (p < 0.05, Cohen's d = 0.68, power > 0.8). The system costs below USD 20 per MSME per month, offering a scalable, human-centered pathway to strengthen loyalty in emerging markets.