High average-utility itemset mining is a significant research direction in data mining. The traditional average-utility (AU) measure employs itemset length as the normalization benchmark, which mitigates the bias toward long itemsets; however, it does not adequately account for the discrepancies between unit profit and actual sales volume. This paper introduces two novel measures: the weighted average utility based on external utility (E_WAU) and that based on internal utility (I_WAU), and examines the theoretical relationships among AU, E_WAU, and I_WAU. Subsequently, we establish a three-dimensional evaluation framework, namely, “category average–profit conversion–unit profitability”. Based on this framework, we develop classification decision matrices for pairwise comparisons of AU, E_WAU, and I_WAU. Within these matrices, products are grouped according to combinations of any two measures, yielding distinct product classifications that support customized marketing strategies. This approach further underscores the practical value of E_WAU and I_WAU in facilitating real-world decision-making. Finally, we conduct empirical validation and analysis on three real-world datasets. Experimental results demonstrate that, compared with using AU alone, the combined use of E_WAU and I_WAU is more effective in identifying product categories and devising appropriate sales strategies.