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混合收益模式下在線內容免費數(shù)量與廣告強度的組合優(yōu)化

系統(tǒng)管理學報 頁數(shù): 9 2019-09-27 17:31
摘要: 在線內容的混合收益模式是指運營商從免費內容中獲取廣告收益,并從高服務品質的付費內容中獲取訂閱收益的運營模式。在該模式下,運營商的一個主要決策問題是如何確定在線內容免費數(shù)量與廣告強度的組合,從而獲取最大收益?;诿赓M體驗前后消費者品質感知的差異建立了從免費體驗到付費升級的隨機行為模型,在此基礎上構建免費數(shù)量與廣告強度的組合優(yōu)化模型。結合數(shù)值實驗,分別分析了消費者對內容網站品質"高估"與"低估"的情境下,免費數(shù)量與廣告強度組合的優(yōu)化策略。研究發(fā)現(xiàn):①在"低估"情境下,運營商需采取"高免費數(shù)量+低廣告強度"的組合策略來吸引客戶,但隨著客戶群趨于穩(wěn)定且消費者對網站品質感知不確定性水平大幅降低時,運營商可適當通過加大廣告強度來進一步提高收益;②在"高估"情境下,如果消費者品質感知的不確定水平較低,則運營商可采取"低免費數(shù)量+高廣告強度"的組合策略來提高收益;反之,運營商則要采取"高免費數(shù)量+低廣告強度"的組合策略。
The mixed-revenue mode of online content refers to the fact that providers obtain advertising revenue from free online content and subscription revenue from paid content with high service quality,in which,one of the main decision-making problems for providers is the determination of the optimal combination of free size and advertising intensity to maximize profits.Based on the difference of consumers' perception of quality before and after free-experience,this paper constructed a stochastic behavior model of‘free to pay'.Besides,it developed an optimization model for the combination of free size and advertising intensity.Moreover,through numerical experiments,it analyzed the optimization combination strategies under two different contexts,where consumers might overvalue or undervalue the quality of the online content websites.The results show that in the context of undervaluation,providers should adopt the strategy of "greater free size and smaller advertising intensity"to attract customers.However,as the consumer base tends to stabilize and perceived uncertainty decrease,providers should increase advertising intensity to improve revenue.In addition,in the context of overvaluation,if consumers have a low perceived uncertainty,providers should adopt the strategy of"smaller free size and greater advertising intensity",otherwise,providers should adopt the strategy of"larger free size and lower advertising intensity"because it is the best choice where consumers have a high perceived uncertainty.

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