Large language models (LLMs) have become integral components of various AI solutions, with the reinforcement learning from human feedback (RLHF) stage playing a critical role in aligning model outputs with human preferences. However, generating the human preference data required for RLHF is often costly and time-consuming due to its reliance on human evaluation. This study addresses this challenge within the dialogue scenarios of the fintech industry. We leverage rich, non-confidential, multi-turn dialogue data, such as call center dialogue records, which include associated business metrics (e.g., problem-solving rates, turnover ratios) to construct preference-aligned data. We introduce Self-Preference, an automated method for creating preference-aligned data guided by these objective business metrics. The approach involves clustering dialogue histories based on their semantic representations and calculating a well-designed conditional probability ratio that correlates sequences with business metrics to generate preference data. In contrast to traditional preference alignment data generation methods that depend on subjective human evaluations, Self-Preference significantly reduces labeling costs and mitigates model-induced biases. Experimental results indicate that models trained with Self-Preference generated data demonstrate a strong positive correlation with target business metrics, highlighting the method’s effectiveness in facilitating efficient, goal-oriented alignment of LLMs.