Amid 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 experience 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. The results reveal that firms with superior data analytics capabilities purchase more data at high data prices, whereas those with weaker capabilities tend to buy more at low data prices. Counter intuitively, 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 data prices, while those with weaker capabilities can achieve profit superiority at low data prices. Under specific market conditions, a “winner-takes-all” outcome emerges, where firms with stronger analytics capabilities dominate the entire market.