Growth and popularity in markets for free digital products

Gil Appel
Marshall School of Business
University of Southern California

Barak Libai
Arison School of Business
Interdisciplinary Center (IDC), Herzliya

Eitan Muller
Stern School of Business
New York University
Arison School of Business
Interdisciplinary Center (IDC), Herzliya

June 2016

The authors would like to thank Gal Elidan, Zvi Gilula, Jacob Goldenberg,Hema Yoganarasimhan, Scott Neslin and Oded Netzerfor their advice and helpful comments during the research process.

Growth and popularity in markets for free digital products

Abstract

Free digital products (FDPs) dominate online markets, yet our knowledge and theories about their growth are based mainly on conventional goods. We demonstrate how FDPs’growth dynamics differ from those observed for conventional new products, using a large-scale dataset that documents the growth of close to 60,000FDPs, and supported by an additional growth analysis of thousands of mobile apps. We find that FDPs display three distinct patterns of growth: bell-shaped pattern (“Diffuse”); exponential-type decline (“Slide”); and a combination of the two (“Slide and Diffuse”). We further show a robust relationship between FDP popularity and growth pattern ubiquity, providing the first evidence of a correlationbetween products’ popularity and growth patterns. We further show how FDP-related growth phenomena help to explain the patterns that emerge, and elucidate the need to adapt our knowledge on new product growth and its modeling to the fast-moving world of free digital products.

Keywords: diffusion of innovations; free products; mobile applications; product life cycle; social influence; software

1. Introduction

An intriguing development in the consumer market landscape is the substantial increase in the number of digital products available for free (Anderson 2009). Free digital products (FDP) have been available for a while for computer software products supplied via online platforms, joined recently by similar FDPs for smartphones and web applications. Some of this availability stems from the “freemium” business model, under which a certain percentage of adopters will eventually upgrade to a less restricted version or purchase in-app byproducts (Kumar 2014). Yet the increase in FDPs also follows other developments such as the rise of open-source software collaboration projects, where many users join forces to produce software products that will be free except for technical support (Mallapragada et al. 2012). Recent reports highlight the ubiquity of the phenomenon: More than 90% of recently downloaded smartphone applications were free, with this percentage expected to continue rising in the foreseeable future (Olson 2013; AppBrain 2016). In established markets (e.g., task management tools and anti-virus programs), a fierce battle is being waged among “freemium” and “premium” business models (Dunn 2011; Woods 2013).

The question we put forth is whether the new product growth generalizations and insights developed over the years in markets for conventional products apply also to FDPs. In particular, we focuson the essential shape of growth. Previous research has maintained that growth in digital environments in general (Rangaswamy and Gupta 2000) and FDP in particular (Jiang and Sarkar 2010; Lee and Tan 2013) follows the commonly observed S-shaped diffusion patterns – with bell-shaped non-cumulative growth – and can thus be analyzed using traditional diffusion models. However, when we began to examine the growth patterns of tens of thousands of FDPs (to be described later), the picture that emerged was different:While a bell-shaped patternimplies growing demand early on, we find that in the FDPsexamined, the correlation ofmonth-to-monthgrowth in the first year was positive for only about 40% of the products. What further stood out was thatthe extent of the phenomenon was highlycorrelated to the level of product popularity: The percentage of positive correlations monotonically decreased with the popularity level, down to 26% positively correlated patterns for the less popular bottom 10% of popularity. This is not the conventional pattern of growth we read about in the new product textbooks.

What can drive this phenomenon?Note that conventional products are associated with significant R&D costs, as well as costs of manufacturing, marketing, and maintaining market presence. Therefore, firms will invest in screening and testing of the products before market launch, and will be motivated, internally or due to channel pressure, to take a product off the shelf if it seems to fail. The case of FDP differs, in particular due to the low barriers to development and introduction of many digital products into the market, which may be reflected in two ways: First, the cost of adapting the product and offering it to small, specific niches is low, which leads to a “long tail” of supply (Brynjolfsson et al. 2010).If past research emphasized the ability of digital channels to enable a long tail of physical goods (Brynjolfsson et al. 2011), then the fact that the goods themselves are digital enables even better tailoring to small niches. Second, given the lack of barriers, there is an increased presence of small and less experienced suppliers with low resources to invest in marketing, so that we can expect to find many products whose low popularity stems from inability to reach larger audiences even if targeted otherwise. Indeed, it is reported that a large share of FDPs are considered failures, and eventually do not even cover development costs (Foresman 2012; Rubin 2013).

Overall, whether the niche market was intended at the outset or not, consumers should face a large share of low-popularity products when considering supply in the FDP market, at least in the absolute number of offerings[1]. Empirical data suggest that this is the case for markets such as free PC software (Zhou and Duan 2012) and mobile applications (Zhong and Michahelles 2012). In early 2016, for example, among the 1.87 million free android apps available, more than 60% had fewer than 1,000 downloads, and only about 1% were downloaded more than 1 million times (AppBrain 2016).

This phenomenon raises an interesting question regarding the prevalent growth pattern of FDPs. Our knowledge on the diffusion of new products has been largely shaped in markets such as durables, pharmaceuticals, and services looking typically at highly popular cases of growth(Peres et al. 2010). In fact, one of the essential concerns with the understanding of innovation diffusion is that nearly all knowledge comes from successful innovations (Greve 2011; Rogers 2003). This lack of evidence on the growth pattern for what may be the majority of the FDP market is an issue of significant managerial and theoretical importance. The shape of the growth curve is considered “the most important and most widely reported finding about new product diffusion” (Chandrasekaran and Tellis 2007). Studying growth patterns is a fundamental stepping stone to the understanding of markets for new products: It is used to understand the driving forces of new products’ success; as a base for modeling and optimizing firm behavior in the context of new product introductions; for decisions of termination or further support for new products; and for segmentation by adoption times (Golder and Tellis 1997; Peres et al. 2010).

Here we study the full spectrum of growth patterns in FDPs, providing comprehensive evidence for a fundamental difference between the growth of highly studied superstars, and the growth of the less popular majority. The ability to track information in the case ofFDP markets provides an opportunity to conduct a large-scale analysis in a way seldom available to past new product growth researchers, and to overcome theproblem of a left truncation bias to lack of data on the product’s early days (Jiang et al. 2006). We use data on the monthly level of downloadsfrom launch-day of a large number of software products in multiple categories, with downloads per product ranging from a few hundred to millions, making this one of the largest new product diffusion studies to date. Our main data source is the SourceForge database, which enables us to study the growth of almost 60,000 free software products. We are able to complement this analysis by also looking at data on the growth of close to 7,000 mobile apps, which shows consistent results. The main insights can be summarized as follows:

  • Three pattern archetypes dominate the growth of FDPs in our datasets: a bell-shaped curve (largely left skewed) that we label diffuse, an exponential-like decline starting at launch labeledslide, and a combination of the first two – slide & diffuse. Diffuse patterns represent about half of the cases in our database.
  • Thedynamics that lead a product to the “underdog” part of the long tail differ from the pattern that leads a product to become a superstar, as the ubiquity of the three archetypes is strongly related to the popularity of the products. Bell shapes are dominant in popular products, yet become a minority in small niche products.The fact that the very popular products are almost exclusively bell shaped may help to explain how previous research, which has been based on popular products, missed this relationship.
  • Two phenomenathat characterize FDP markets help explain the shape of growth:The first is the inception effect, representingdisproportional early-onset external effects, which explains the slide phenomenon in the presence of social influence. The second is the recency effect, which implies that in free digital markets, recent adoptions (and not only cumulative adoptions as traditionally used in diffusion models) help explain the dynamic effect of social influence on growth.
  • Recency is in particular important in helping to differentiate between popular and less popular products. The association of recency and growth is more than double among the top popular 10% compared to the bottom 10% in popularity. We further find evidence that recency level in a category is associated with the shape of the popularity curve, so that higher average recency level in the category is associated with higher inequality, captured by the Gini coefficient.

These findings are significantto our understanding of FDP growth, and to attempts to model and optimize growth in such markets. In a broader theoretical sense, these findings imply that generalizations that developed along the product life cycle, its turning points, and its drivers (Golder and Tellis 2004) may need re-examining in the rapidly growing, dynamic world of free digital products.

2. Background

2.1. Related literature

Our study relates to a number of research avenues:

Markets for free digital products:Research on FDPs has examined issues such as optimal initial spread of freeware as part of profit maximization in the longer run (Cheng and Liu 2012; Niculescu and Wu 2014), free-riding and competitive dynamics (Haruvy and Prasad 2005), and the impact of the creation process on success (Grewal et al. 2006). Other research has focused on the effect on demand of bestseller ranking and consumer ranking (Carare 2012; Lee and Tan 2013), as well as other factors such as price discounts on in-app purchases (Ghose and Han 2014). We add to this growing literature by providing the first large-scale analysis of the growth patterns of FDPs, which is significant in particular given the assumption that FDPs grow and should be modeled in a manner similar to other products typically described by the Bass diffusion model (Jiang and Sarkar 2010; Yogev 2012; Lee and Tan 2013).

The long tail. From another angle,this work is also related to efforts to understand the nature and significance of supply and demand inequality in electronic commerce, often considered in the context ofa “long tail”. Previous literature in that area has focused on the factors that affect the pattern of sales, and in particular whether it leads to higher shares of sales among low-selling niche products, or alternativelyamong high selling “superstars”. Looking at both supply-side factors, such as broader product variety and distribution channel dynamics and lower stocking costs, and demand-side factors, such as reduced search costs (Elberse and Oberholzer-Gee 2007; Brynjolfsson et al. 2009, 2011; Hinz et al. 2011; Kumar et al. 2014), considerable attention has been given to the inter-customer effect in the form of recommendations and reviews in the creation of the long tail; yet also providing more “thrust” to superstars (Fleder and Hosanagar 2009; Oestreicher-Singer and Sundararajan 2012; Hervas-Drane 2015; Zhu and Zhang 2010).

We add to this literature an exploration of the dynamics at the individual product level along the curve. If previous approacheshave generally accepted the existence of “underdog products” and “superstar products”, we ask how a product gets to become one or the other.

Patterns of innovation growth: In a more general sense, our effort is related to the ongoing efforts to study the pattern of new product growth, which spans numerous disciplines (Rogers 2003). The fact that the adoption rate of successful innovations follows a bell-shaped or logistic-type curve, and a cumulative S-shaped curve, is considered one of the fundamental discoveries of social science, and was largely attributed to the dynamic role of social influence among customers in various forms (Young 2009; Peres et al. 2010). While there is evidence of some exceptions to theS-shaped curve with acumulative r-shaped (non-cumulative exponential decline) pattern for entertainment goods such as movies and for supermarket goods (Gatingon and Robertson 1985; Sawhney and Eliashberg 1996),the perception across disciplines is that “the S-curves are everywhere” (Bejan and Lorente 2012). Indeed, these patterns form the bases of diffusion-of-innovations theory and forecasting new product growth using consistent growth shapes, such as the Bass model, Gompertz, or logistic curves (Meade and Islam 2006).

We add to this literature in two ways. First, we highlight FDPs as an additional, yet separate category that is not necessarily dominated by S-shaped curves, and show how growth characteristics of FDPs can explain the various shapes.In a more general sense, we provide initial evidence for the relationship between product popularity and the shape of growth, an unexplored issue in a research stream that has focused on highly popular products.

2.2. Modeling FDP growth

Since our aim is to examine growth along the FDP popularity curve, we will need to model the growth of an individual free digital product. Two fundamental effects that lead to the commonly observed S-shaped curve are considered when modeling the growth of new products (Mahajan et al. 1990): The internal influencecaptures the impact of previous adopters via word of mouth, imitation, and network externalities, typically considered a function of the number of cumulative adopters to date. The external influence captures influences outside of the group of previous adopters, such as advertising and mass media. We argue that an adaptation is needed in both types of influence is to capture the growth in FDP markets as follows:

Internal influence and recency effect. While diffusion modelers have largely used the number of cumulative adopters as a sole indicator of internal influence, some recent work points to a possible need to separate the effect of recent adopters from that of cumulative number of adopters, attributed to the difference in intensity of word of mouth in the two groups (Hill et al. 2006; Iyengar et al. 2011). It has been suggested, for example, that recent adopters may be more contagious than consumers who adopted less recently,as the former are more enthusedand/or credible (Risselada et al. 2014).

We contend that in particular the growth in FDPs should allow this distinction. First, it is often reported that for many FDP users, usage and engagement center on the timeright after adoption (Danova 2015). Second, it is well accepted that adopters of FDPs (and other digital goods) rely heavily on popularity ranking information as appears in social media, app stores, and download sites (Carare 2012; Garg and Telang 2013; Ghose and Han 2014; Lee and Raghu 2014). Yet, as is clearly observable, rankings do not necessarily reflect cumulative downloads, but rather reflect past period popularity (Neitz 2015). This means that the recent number of downloads, and not only cumulative ones, may play a pivotal role in FDP download decision making. In fact, popularity rankings may also affect users who do not consider this information explicitly, but rely on search. For example, it is reported that search results of engines belonging to Google and Apple also largely depend on recent popularity ranking when displaying results (Walz 2015).

External influence and the Inception effect.External influence istraditionally a parameter that captures the marketing mix in the industry, in particular that of advertising (Mahajan et al. 1990). In the absence of large-scale advertising support for many FDPs, and given the dominance of social media, much of the external influence comes from social media articles and experts’ recommendations and ratings. However, attention to new products may be short lived: Given the large number of launched products, the attention given to a new product centers on the beginning of its life cycle.In fact, even when considering firms that do invest in advertising to promote FDPs, there is a strong motivation to focus on the early period of growth. It is argued that FDP producers have a short window of time in which to generate the groundswell that can lead to attention by sources such as the charts in the app stores, and thus they must act early on (Rice 2013; Kimura 2014). Consequently, those FDP developers who invest in marketing may often do so in “burst campaigns” that are meant to get them on consumers’ radar early in the game (ADA 2014; Klein 2014). Overall, we can expect that for FDPs, external influence will be particularly strong early on in the new product’s life, a phenomenon that we label the inception effect. This effect can be reflected in decay in the external influence parameter’s value over time.