Essays on Applied Microeconomics of Freemium Pricing Strategies in Mobile App Market
Open AccessThe motivation of the dissertation is to help venture capital investors discoverwhich apps will be most successful from an early stage. Since most apps are not making positive profits in the early stages of mobile app development, it is even more important for venture capitalists to look into the strategic aspects of the product.The two crucial decisions an entrepreneur makes at an early stage are product-market fit and pricing strategy. In the competitive market of mobile apps, developing a product that is different from its peers is beneficial to capture consumers’ attention and attract traffic early on. That decision is usually interlinked with the pricing decisions. As freemium pricing becomes the prevalent choice nowadays, entrepreneurs would couple the suitable pricing choices with their product-market fit to optimize sales or profits.This dissertation comprises a comprehensive literature review, a data section, andthree essays covering mobile app product market fit and freemium pricing topics.In the literature review chapter, I have diligently reviewed the optimal distinctive-ness theory to argue that being niche is beneficial; the second-degree price discrimination theory argues that freemium is suitable for mobile apps. Ultimately, I reviewed the two- sided market and positive network externalities to link the product-market fit and pricing strategies. For certain types of apps, being niche is more beneficial, and they are more likely to include in-app purchases or ads. For other apps, being niche would lead to a narrower consumer base, and thus, charging positive price upfront would be a better strategy. The chapter also covers important theoretical models such as Borenstein’s circular loca- to model and Shaffer and Zhang’s generalized Hotelling’s model and relevant literature in economics, marketing, and organizational studies closely related to my topic.The data section introduces the niche index, created by applying naturallanguage processing to app description data. It quantitatively measures how similar or dif- ferent an app is compared to its peers. It quantitatively measures how similar or different an app is compared to its peers. The data section also includes explanations on data collection, cleaning, missing value imputation, descriptive statistics, and variable definitions.In the three analytical essays, I explore the predictive power of the niche indexon app price, app installs, and whether an app includes ads or in-app purchases in the full sample, market-leader, and market-follower sub-samples, respectively. To assess whether some control variables are inadequate, I used forward step selection, alkaline information criteria, and Bayesian information criteria to pick the best regression models with adequate control variables according to each outcome variable. In addition, I also assessed the post- covid time dummies and their interaction with the niche index and their prediction on the outcome pricing variables. I found no additional impact of the niche index on the post- covid pricing variables as compared to pre-covid impacts.In the first essay, I found that niche has positive impacts on installs, and the prob-ability to include in-app purchases and ads while negatively impacting price.I found that the effect differs for successful apps and less successful apps. I measure the success here using cumulative installs and the firms that developed the apps. Based on third-party rankings and the shape of the distribution of installs, I set two thresholds and divided apps into market-leading and market-follower apps. The market-leading sub- the sample consists of apps with high cumulative installs or developed by prestigious firms, and the market-follower sub-sample consists of the rest. I observed that the predictions for the market-leading and market-follower apps are quite different. Thus, I decided to devote essay two exclusively to market-follower apps and three to market-leading apps.The second essay focuses on the market-follower sub-sample. I found the impactof the niche index is similar to the impacts in the full sample. In terms of different app categories, I found that the niche impact is especially large in gaming apps due to their relatively low heterogeneity nature.The third essay focuses on the market-leading sub-sample. I found that the im-pact of being niche is smaller in the market-leading apps because of their relatively higher heterogeneity. In addition, being niche also impact utility apps differently from hedonic apps or apps with two-sided market characteristics. For the special gaming category, being niche has some effect but is smaller than in the market follower sub-sample.My research provides novel empirical evidence of digital products for variousstrands of theoretical research, including the optimal distinctiveness theory, product differentiation, price discrimination in two or multi-sided markets, and consumer psychology.
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