Amid the wave of the digital economy, data, as a critical factor of production, is profoundly reshaping enterprise value creation patterns and competitive landscapes through its dual value: significantly enhancing service quality and boosting advertising precision. The rapid emergence of the data brokerage market provides firms with a strategic lever to harness this dual value. By integrating supplementary datasets from data brokers, firms can optimize service experiences and achieve precise ad targeting. However, this widespread data utilization simultaneously intensifies market competition. To investigate firms’ optimal data acquisition and service pricing decisions, this study constructs a game-theoretic model involving two competing firms who provide subscription-based service products. The results reveal that firms with superior data analytics capabilities purchase more data under high data price, whereas those with weaker capabilities tend to buy more under low data price. Counterintuitively, when both the unit advertising revenue and data price are low or high, firms with weaker analytics capabilities tend to set higher service prices. Furthermore, firms with stronger analytics capabilities earn higher profits at high or low data price, while those with weaker capabilities can achieve profit superiority in the intermediate price range. Under specific market conditions, a “winner-takes-all” outcome emerge, where firms with stronger analytics capabilities dominate the entire market.