Over the past decade, “freemium”—a combination of “free” and “premium”—has become the dominant business model among Saas start-ups and smart phone app developers. Users get basic features at no cost, and can access richer functionality for a subscription fee. If you’ve networked on Linkedin, shared files through Dropbox, watched TV shows through Hulu, or searched for a mate on Match, you’ve experienced the model firsthand. It works for B2B companies as well—examples include Box, Splunk, and Yammer.
However, nowadays, many services or software products are no longer free to end-users, and users have to pay for the cost of service. In this case, how do you promote user acceptance for paid services? How do you reduce the uncertainty for users to make an acceptance decision for paid services? How can users gain knowledge about the system before making the decision to pay for the service? To address these questions, the free trial marketing strategy, in which a service is provided free of charge, either with limited functionalities or with full function for a limited time, seems applicable. Once a user accepts a free trial and gains some experience, the subsequent decision is whether to pay the fee for continued use of the service. Typically, individual acceptance for paid services involves two stages of decisions: (1) the acceptance decision for using the service free with limited time or functionalities to gain experience; (2) the purchasing decision for continuing to use the service for a longer time or for full functionalities.
The first stage, the acceptance decision to enroll in the free trial, could be explained by the existing technology adoption model, while the second stage, the purchasing decision after the free trial with the intention to continue using the system, has seldom been investigated. Since the second stage involves both continued usage and free trial marketing strategy, can analytics help explore the purchase decision-making process after the free trial? While the trial promotion practice could lead to strong marketing performance, should the perceived fee should taken into consideration in user adoption and continued usage decision-making process when the target service is not free to users? Can analytical insights help identify both pre-usage and post-usage beliefs? Does usage experience have a great impact on post-adoption beliefs, and if yes, what is the influence it exerts?
For the uninitiated, these questions translate into, “How do we own and monetize the relationship with customers and come up with creative pricing and packaging to grow our businesses?”