Research On The Marketing Strategy Improvement Of Digital Content Platform

As the strategy of Network Power and Digital China continues to advance,the digital content industry,as an important carrier of future cultural output and an important means of cultural inheritance,has gradually become the focus of various government policies.Statistics show that China’s digital content industry is currently in a critical upward stage of rapid expansion.However,compared with other developed countries,there is still some space for growth.Therefore,how to promote the further development of China’s digital content industry has become a hot spot for a wide range of research concerns.Past studies have made many discussions on macro policy recommendations,whereas the development of the digital content platforms,which constitute the basic elements of the digital content industry,is also extremely important.Therefore,this study explores how to improve the exsiting marketing strategies of digital content platforms at the micro-level,and thus promote the flourishing development of the digital content industry.This paper examines the improvement of markting strategies for digital content platforms from three perspectives.First,how to obtain high-quality new customers is the first step that this study focuses on.Digital content platforms currently mostly use incentivized social referrals in the hope of gaining more new customers.While past studies have focused on its impact on the number of new customers acquired,this study wants to explore its impact on its quality.Secondly,how to build an effective recommendation system to increase customers’ stickiness to the platform is another key issue of interest in this study.Digital content platforms currently use both algorithmic and human recommendation systems,but how these two types of recommendation tools work and how to optimize their ranking to best improve their effectiveness is the question this study wants to explore and solve.Finally,how to optimize the payment business model is the most important part for platforms.The digital content industry mostly adopts a freemium business model for marketing,i.e.,providing the first part of content for free for customers to understand,and then charging for the subsequent content.However,this model often has a game problem,that is,too little free content will lead to premature abandonment by customers before they have time to fully understand or become interested in the content,while too much free content will have an impact on the platform’s revenue.Therefore,how to reasonably determine the amount of free content to achieve the balance between maximizing the interest of consumers and maximizing the profit of the platform is the problem that this study wants to solve.Facing the above three problems,this paper cooperates with a leading digital content platform in China and uses field experiments,empirical research and algorithmic optimization to obtain the following research results.Study 1 shows that while incentivized social referrals can increase old customers’purchases,it attracts adverse new customers who have lower quality compared to new customers who sign up for other channels.Further,such dysfunctional effects are exacerbated for monetary and apathetic gift incentives used in social campaigns,yet empathetic incentives can reverse this result.By exploring the underlying mechanisms,this study finds that monetary incentives and apathetic gift incentives cause customers to be extrinsically motivated,which in turn decreases engagement with the platform,while empathetic gifts cause customers to be intrinsically motivated and can increase their engagement with the platform.Finally,causal random forest-based machine learning algorithms provide managers with campaign design solutions based on customers heterogeneity characteristics.Overall,the study reveals the dark side arising from incentivized social sharing when applied to digital content products,providing an early warning for platforms;it also provides actionable guidelines for platforms to acquire high-quality new customers.Study 2 show that both algorithmic and human are able to increase consumer purchases,but algorithmic recommendations function as deep sales by increasing purchases of historical consumption types(customer preference exploitation),while human recommendations function as breadth sales by increasing purchases of new types(customer preference exploration).And the experiments found that the hybrid sequence of manual followed by algorithmic recommendation(HA)was optimal.HA was 12%more effective than the non-hybrid sequence and 18%more effective than the reverse hybrid sequence AH.While both development and exploration are important,the initial exploration leads to more effective subsequent development under the optimal layer sequence.Overall,this study provides managers of digital content platforms with a new system of recommendation mix strategies,i.e.,not only can algorithmic and human recommendations be used as independent marketing tools,but also can optimize the order in which they are used to achieve complementarity of different advertising strategies,providing an important guarantee for improving customers long-term stickiness to the platform.Study 3 show that the number of free chapters has a significant impact on customers’ final purchase decision,and the optimal charge points identified by the experiment can increase the platform’s revenue by 50%.Moreover,by combining the position of the charge point with the emotional fluctuation of the book content and using SnowNLP’s text mining technology to mine the climax point of the book content emotion,this study finds that setting the charge point after the second climax point can maximize the customer’s willingness to pay.In addition,by exploring the underlying mechanism,this study found that customers’ emotions change in synchrony with the fluctuation of content emotions,and this consistency reaches the highest after the second climax point,therefore,based on the self-expansion theory,customers have a strong willingness to pay in order to maintain this synchronization.Finally,this study uses multinomial logistic regression to predict an optimal pay point setting strategy.Overall,this study provides a new content-based pricing strategy for digital content platform managers,which can play a substantial role in increasing customer payment rates and increasing platform revenue.Based on the characteristics of digital content products,this study enriches and improves the research on the marketing strategies of referral,recommending and pricing for digital content platforms from both theoretical and practical levels.On the one hand,it reveals the impact of incentive social referral on the quality of new customers,deepens the understanding of algorithmic and manual recommendations and dynamic sequential recommendations,broadens the scope of research on freemium models and related theories,and provides a theoretical basis for ensuring good and healthy platform operations;on the other hand,it also provides platform managers with practical suggestions for acquiring stable and high-quality new customers,establishing a reasonable recommendation system On the other hand,it also provides platform managers with practical suggestions for acquiring stable and high-quality new customers,establishing a reasonable recommendation system,and pricing strategies,which help to improve the revenue and competitiveness of the platform,and thus provides new insights into the development of the digital content industry from the micro-level.