Willingness to pay for mobile apps

The App economy is showing huge growth and attracts interest of worldwide technology companies who seek new business opportunities. The App economy already copes with huge saturation of Apps. However, there are some issues. People churn through Apps fairly frequently, the cost of acquiring users through advertising continues to rise with double digits year-on-year, and new companies who seek to introduce a new Apps, force the startups to better tune their spending, based on data about how people are discovering and choose their Apps. On the customer’s side it is uncertainty about product value that is a pervasive feature of many markets. This applies to paid Apps as well, since the customer is not able assess the products real value until they have purchased the product. This research aims to investigate the Willingness To Pay(WTP) for paid Mobile Applications (Paid Apps). Especially the importance of antecedents on likelihood to purchase paid Apps is subject to this study. Features that are displayed at the moment of purchase reduce the customer’s difficulty associated with choosing between options. These features were taken as attributes in a Choice Based Conjoint Analysis to distract the relative importance of these attributes and to measure WTP. Also interaction with involvement, payment method and demographics were subject of analysis. The results show that all displayed attributes have direct effect on the customer’s WTP. Price has been the most important factor in the customers decisions followed by customer rating(worth €0.62 per level upgrade), editors choice(worth €0.32), top developers hallmark (worth €0.15) and best seller rank (€0.16 per level upgrade). Furthermore customer driven features, such as customer rating and best seller rank, have a much stronger effect on WTP than platform driven features, like top developers hallmark and editors choice. Concerning interactions, Females(19%) and Credit Card users(16.7%) are in general willing to pay less for Apps. Application Developers and Platform Providers could respond to this knowledge with, for instance, price promotions. For Click-and-buy users the effects of the attributes customer rating(worth €0.13 less per level upgrade) and editors choice(worth €0.21 less) are less important for making their decisions, but they are less affected by price. Application Developers can only use this information if they are in possession of the customers payment information. These results are of interest for the Platform Providers since they know which customer is using which payment method. In this way they are able to personalize the store environment to the preference structure of the customer, potentially leading to higher revenues. Another finding is that the older people are, the less they attribute utility to the top developer hallmark. This could be explained by the fact older people are less familiar with Application Stores. Moderate supports was found for the effect of involvement. Involved customers are in general willing to pay more for apps and the more a person is involved the more he attributes utility to best seller rank. With these results Application Developers and Platform Providers can allocate resources based on evidence. Managing the features in the Application Store and targeted offerings are examples of activities that can be implemented using the results of this research. Considering the different types of Apps, it turned out that respondents were most likely to pay for Productivity/Utility Apps (mean= 3.27) followed by Sports/Health Apps(mean= 3.02) and Informative Apps (mean= 3.01). Games were stated as the type of App that they were least likely to pay for (Mean= 2.52).