Analysis Of Knowledge Paid Membership System Based On Fogg Behavior Model

The knowledge payment industry faces the dilemma of weak user value and reduced commercial value.The weak user value is mainly manifested in two aspects.One is that the knowledge payment industry is still in the stage of user growth,but the overall user growth rate has slowed down.It is difficult for the platform to quickly obtain more new users' first purchase value.The other is the knowledge payment platform,having a lack of refined operations for existing users,and the value of existing users cannot be fully tapped.The weak user value has further led to distrust of the knowledge payment industry in the capital market,and the commercial value of the knowledge payment platform has decreased.In this context,membership has become the focus of operation in the knowledge payment industry.As the most common method of user refined management,it is the key to enhancing user value and the platform's commercial value.By sorting out the development status of paid-knowledge members,analyzing user reviews of paid-knowledge apps in App Store,and combining user interviews,the author found that paid-knowledge members have the following problems: the quality of content is uneven,which cannot meet the learning needs of members;the charging method is unclear,the purchase experience is poor,and the perceived cost of the members is increased;the promised rights are not realized,and the member's payment rate and repurchase rate are low.In order to solve the above problems,the author needs to fully analyze the learning needs of member users,what costs users will pay attention to in the process of purchasing members,and the scenarios in which users will carry out member payment behaviors.The author intends to use the Fogg behavior model to carry out the analysis.On the basis of verifying the applicability of the Fogg behavior model to the analysis of paid knowledge members,the author uses user interviews to build specific indicators for the analysis of paid knowledge members on the basis of the model:(1)Based on the motivation elements of Fogg's behavior model,construct the learning demand index of member users.(2)Based on the ability elements of Fogg's behavior model,build cost indicators that users will notice in the process of purchasing.(3)Based on the trigger elements,explore the scenarios under which users will conduct member payment behaviors.After deriving specific indicators,the author made following assumptions:(1)The satisfaction of motivation elements(satisfaction of learning needs)will affect the payment behavior of members.(2)The improvement of ability elements(reduction of perceived cost)will affect the payment behavior of members.(3)Trigger elements(trigger scenarios)will affect the payment behavior of members to varying degrees.Subsequently,the author carried out a quantitative analysis through questionnaire surveys to verify these hypotheses.After analyzing the questionnaire data,the author found that the above assumptions are true.Subsequently,the author analyzed the typical cases of knowledgepaying members from three aspects of motivation,ability and triggering factors,and analyzed whether they met these factors,and on this basis,proposed corresponding strategies to meet the learning needs of members and reduce members' Perceive cost,increase the payment rate and repurchase rate of knowledge-paying members.By comparing the membership systems of four typical knowledge payment platforms,the author found that: for motivation factors,there are various forms of check-in methods on each platform to help members persist in learning,and members can interact with platforms and content producers,IP value and rights distinction can attract members to pay most.Regarding the ability elements,before payment,each platform mainly reduces the perceived cost by enhancing the perceived value of members.When paying,the platform mainly optimizes the information architecture to enhance the member's operating experience.After payment,the platform also needs to use more systematic methods to enhance the member's learning ability.Regarding the trigger elements,most of the various platforms have been adapted to the usage scenarios of the members to enhance the members' autonomous triggering on the platform,and the interpersonal triggers between members are increased by setting auxiliary functions.However,a more systematic approach is needed to realize deep learning for members.Interactions between members should further take advantage of the platform's UGC advantages.Some interaction designs still need to be optimized,and triggering methods need to be more precise to further promote member payment behavior.Based on the results of the comparative analysis of the membership system of typical knowledge payment platforms,the author proposes the optimization path of the knowledge-payment member system: extend member content categories,strengthen member learning functions,create member interactive communities,and meet member learning needs;clarify member rights,carry out precise member marketing,and reduce member operating costs;grasp multiple role positioning,explore technological innovation paths,and play a variety of aspects positive effects.