Putting the Horses Before the Cart: Harnessing the Power

of Partner Brands

Pianpian Kong, Paul B. Ellickson, Mitchell J. Lovett⇤

JOB MARKET PAPER

Preliminary and in Progress

Current Version: 7/11/2016

⇤Pianpian Kong is a doctoral student, Paul Ellickson is Professor of Economics and Marketing, and Mitchell Lovett is Associate Professor of Marketing, all at Simon Business School, University of Rochester. We thank Ron Goettler, Avery Haviv, Yufeng Huang, Guy Arie, and participants at the Simon Marketing group seminar for their comments and suggestions. All errors are our own. Please direct any correspondence to [email protected].

1 Abstract

In many tied good industries, variety of complementary products drives adoption of the platform. In the brewer-coffee industry, as the number of K-Cup coffee brand variety increased from 10 to more than 150 from 2010 to 2015, the installed base of Keurig K-Cup brewers expanded from 11% to 31%. The K-Cup coffee consumables grew from $0.3 billion annual sales to over $3.6 billion, comprising 41% of total dollar sales for the U.S. coffee market. A key aspect of Keurig’s brand growth strategy is aggressively pursuing partnerships and licensing arrangements with well-known brands in the mature ground coffee segment. In this paper, we quantify the importance of brand variety in driving the success of the overall Keurig system.

In so doing, we shed light on the benefits of partnering for high brand equity versus a large number of brand options. We propose and estimate a demand system for the brewer and coffee that endogenizes the consumer’s self-selection into brewer adoption based on their heterogeous tastes for both products. We then perform counterfactuals that demonstrate the role of Keurig’s partnerships with national brands in hastening the growth of the category. We find that K-Cup consumables have a strong influence on K-Cup system adoption. On average, Keurig-owned brands have the largest influence on brewer adoption (5.2%), but partner brands are a close second (4.9%) and by 2015, partner brands are 50% more influential on growth than Keurig- owned brands. Hence, our results both quantify the benefits of brand variety to growth and shed light on the most beneficial types of partnerships. 1 Introduction

In the late 2000s, the previously mature U.S. market for brew-at-home coffee was transformed by the creation of the single-serve coffee pod. Between 2010 and 2015, coffee products employing

Keurig’s newly developed ‘K-Cup’ technology grew from $0.3 billion in yearly sales to over $3.6 billion, with the latter figure comprising 41% of total dollar sales for the U.S. market. Compared to traditional brewing methods, single-serve products represented a mess-free alternative that precisely controls the time and temperature of the brewing process, thereby delivering a standardized, high- quality cup of coffee. Within the single-serve brewer market, Keurig’s positioning strategy was geared around the technological superiority of its brewing system and the wide variety of flavors and brands it offered in this proprietary system. A key aspect of its growth strategy involved aggressively pursuing partnerships and licensing arrangements with brands that already enjoyed strong positions in the ground and whole bean coffee segments.

Keurig’s rapid rise to dominance leveraged the unique role of its proprietary platform, the tied goods aspect of its coffee pods and the indirect network effects arising from its broad inclusion of partner brands and licensees in the K-Cup coffee consumables. Our goal in this paper is to quantify the importance of this brand variety in driving the success of the overall Keurig system.

Through this quantification, we aim to shed light on the nature of brand relationships that are most beneficial, and the magnitude of the benefit from working with national brands that are able to leverage their brand equity in the mature coffee segments into the new K-Cup segment. To do this, we propose and estimate a discrete choice model of K-Cup brewer and coffee demand that endogenizes consumer’s self-selection into brewer adoption based on their heterogeneous tastes for both products and accounts for the availability of partner and licensee brands as they join the

Keurig system. Using these estimates, we then perform counterfactuals that demonstrate the role of Keurig’s partnerships in hastening the growth of the category.

To do so, we adapt the platform and tied goods ‘network’ frameworks developed by Lee (2013) and Derdenger (2014) for the video game and console industry to the coffee-brewer context. While our setting shares some of the aspects of the video game and console market, there are some

1 notable distinctions. First, consumers naturally “multi-home” so that adopting the Keurig system does not preclude consumers from continuing to use traditional drip brewers, it simply expands their choice set of available coffee options. In this sense, the coffee category shares aspects of the

‘razor and blades’ market analyzed by Hartmann and Nair (2010). However, unlike in the razors and blades case, the variety of coffee consumables grows dramatically over time and is important to the consumer’s motivation to purchase a brewer. Second, unlike video games, coffee consumables are purchased repeatedly and consumers select among multiple competing varieties. These features make dynamic considerations less important and choice among consumable brands more central.1

An important aspect of the platform and tied goods frameworks is treating the benefit of adopt- ing the Keurig platform as the change in inclusive value arising from gaining access to the expanded set of products it offers. In contrast to the durable video game (software) context, we do not al- low consumers to forecast future changes to coffee product availability.2 We account for consumer heterogeneity in both brewer and coffee demand by using an aggregate discrete choice random coef-

ficients “BLP”-type framework (Berry 1994; Berry, Levinsohn, and Pakes 1995; See Nevo 2000 for user guide) that allows for flexible substitution patterns. In addition, incorporating heterogeneity allows consumers with high values for coffee and the brewer (i.e., lower price sensitivity) to adopt the system earlier than corresponding low type consumers. In sum, our modeling strategy of using the inclusive value to bridge the demand for the brewer and coffee, and incorporating heterogeneity allows us to endogenize the evolution of the platform’s installed base and capture the time-varying composition of platform users.

The multi-homing aspect of our setting requires a novel modeling approach and estimation strategy. Unlike platform and tied goods studies in the video game and console setting, we do not observe sales of consumables broken out by owners (or non-owners) of the K-Cup brewer. To address this, we construct each consumer’s platform ownership likelihood at each point in time, and estimate

1Although coffee products are clearly not durable in the sense of video games, there is some scope for stockpiling. We ignore stockpiling in the coffee choices for two reasons–K-Cups are relatively large to store and we find no evidence that sales diminish following a price promotion, suggesting intertemporal shifts in demand are limited. 2This appears to be consistent with the consumable purchase behavior in the category. To the extent that expectation formation of consumables would matter to consumer’s brewer decision, the entry of a major brand like Starbucks should see acceleration in brewer sales prior to the entry. In contrast, when Starbucks K-Cups became available brewer sales accelerated only after the entry.

2 the demand for consumables jointly for both platforms, rather than for each platform separately as in other platform and tied good studies. As a result, our estimation strategy uses a modified version of Derdenger’s (2014) nested fixed point routine that iteratively updates the parameters from the hardware and software choice problems until the implied distribution of brewer ownership status converges across both markets. Our modification focuses on addressing the multi-homing aspect.

We estimate the demand system using K-Cup brewer and consumable coffee sales data for 15

US cities from 2010-2015, which covers the period of major brand variety expansion for the K-Cup system triggered by the expiration of an important patent. The number of K-Cup coffee brands increased from 10 to more than 150 during our sample period, which includes the entry of many well-known national coffee brands (e.g., Starbucks, Folgers, Maxwell House, Peet’s). The average installed base of the K-Cup brewer expanded from 11% to 31%.3 Our data provide rich regional variation in demand for both the brewer and coffee consumables.

Our demand estimates indicate that the inclusive value plays a significant role in influencing brewer demand, suggesting that quantifying the indirect network effects for the coffee consumables is important. We also find significant price heterogeneity in the coffee consumable market, but little in the brewer market. Consumers who are less price sensitive to coffee are more likely to adopt

K-Cup brewers earlier, so the average price sensitivity of K-Cup brewer owners increases over time.

We conduct counterfactual experiments based on the structural estimates to shed light on the role of brand variety in the evolution of the Keurig system. In particular, we simulate the K-Cup brewer adoption had there been fewer K-Cup coffee brands available with the following conclusions:

First, K-Cup consumables have a strong influence on K-Cup system adoption. If consumers did not respond to the “software” market, the K-Cup system’s installed base would have been on average

20% lower by the end of 2015 than the actual level it achieved. Second, Keurig-owned K-Cup brands on average have the largest total contribution to the system adoption and competitor brands have the least contribution. In particular, installed base at end of 2015 would have dropped by 5.2% if

Keurig owned brand were not available, 1.5% if competitor brands were not available, and 4.9% if partner brands were not available. The magnitude of the contribution is proportional to the

3Based on data from the top 30 IRI cities.

3 share of each relationship type in K-Cup segment. Third, the total contribution of Keurig’s partner brands increases over time, matching Keurig’s own brands by 2012 when Starbucks enters, and overshadowing Keurig’s own brands by 2015. By then they had almost 50% greater influence on the installed base than Keurig’s own brands.

These counterfactual analyses both provide an estimate of the magnitude of the coffee consum- ables influence on the Keurig system growth, and provide insight about the nature of relationship types are most beneficial. In particular, partnerships with a small number of national brands that have strong brand equity to leverage into the K-Cup segment (ala the partner brands) provide much more system growth than even a large number of smaller brands (ala the competitor or licensed brands). As the system grows, partner brands become more important to growth than owned brands. This finding also indicates that not only would measuring indirect network effects through simple counts of tied good size miss the main influences on platform growth, but also the influence of the software brands can vary as those brands obtain deeper penetration.

We note that these counterfactual estimates are conservative because we only drop brands after

2010 (when our data begins) and because we do not allow for entry of competing brewing systems.

Our positive results for partnering generating system growth suggests that Keurig’s relationships with other brands aren’t purely to foreclose the entry of competing systems. Future research could allow for entry of competing brewing systems in order to calibrate the relative size of the incentive to partner for Keurig system growth versus keeping potential entrants out. For Keurig, this incentive to foreclose has important implications because the software patent expires prior to the hardware patent. Further, knowing which brands are most influential for locking in consumers and growing the system is also helpful. Future research could illustrate the incremental value to the Keurig system of partnering with particular brands that have greater brand equity in the mature ground and whole bean segments that can be leveraged into the K-Cup segment.

Although this paper relates to the broader literature on estimating indirect network effects and consumer demand in markets with platforms and tied goods, it is most closely connected to the aforementioned papers by Lee and Derdenger. However, it also relates to earlier papers on networks and platforms, including those by Gandal, Kende, and Rob (2000), Nair, Chintagunta, and Dubé

4 (2004), Clements and Ohashi (2005), Corts and Lederman (2009), Dubé, Hitsch, and Chintagunta

(2010), and Karaca-Mandic (2011). The papers by Lee and Derdenger were the first to take the heterogeneous quality of the networked products directly into account (the earlier papers followed the Katz and Shapiro 1985 approach of capturing the network benefits exclusively through its size).

In our setting, the quality and positioning of the partner and licensee brands is an important aspect of platform adoption.

The rest of the paper is organized as follows. In the next section we describe the US at- home coffee industry and in particular the single-serve coffee market. In section 3 we describe the data and summary statistics. In section 4 we present a static demand model of brewer and coffee.

Section 5 discusses the estimation strategy and identification. Estimation results and counterfactual experiments are presented in section 6 and 7. Finally section 8 concludes.

2 Industry

2.1 The U.S. At-Home Coffee Market

In the United States, the most common coffee brewer for at-home brewing is the classic auto-drip type. With a standard auto-drip machine, users load coffee grounds into a filter basket and make a pot of coffee in each brewing cycle. These brewers typically range in price from $20 about $40, but have limited ability to chage the brewing amount, lead coffee to be wasted, and are a relative hassle to clean. In contrast, a single-serve type brewer, such as a Keurig K-Cup machine, uses a pre-packaged proprietary portion pack (pod) containing ground coffee and a filter, and brews a single cup of coffee each cycle (more recent models can also brew multiple cups). Thus, the single- serve type brewer provides convenience, while allowing users to brew a wider variety of flavors and brands (due to their single-serve nature). Single-cup brewers are now the fastest growing class of brewer, and the second most popular brewing system overall.4 Fueled by the brewer adoption, the related single-cup coffee products have become the second largest segment (trailing only ground coffee) in the brew-at-home category, with $3.6 billion revenue. This corresponds to 41% of total

4http://www.statista.com/topics/2219/single-serve-coffee-market/

5 coffee sales in the 2015 US market.

The single-serve brewing innovation was first commercialized by Keurig. Founded in Massachu- setts in 1992, Keurig first entered the office coffee service market in 1998 with a highly successful, commercial grade single-cup brewer. In 2003, it introduced the K-Cup brewer for the at-home market.5 Several single-serve brewing systems were launched around the same time, including the

Melitta One:One brewer by Salton, Senseo by Sara Lee, and Home Cafe by P&G. However, por- tion packs developed for a given system were not compatible with the others, creating a platform adoption problem. Keurig positioned its systems as the superior option, primarily due to its higher quality machines and greater variety of flavors and brands.6 By 2010 the K-Cup system dominated the field, and effectively became the proprietary standard of the single-serve market. This domin- ance is reflected in Keurig’s share of coffee sales. In 2010, Keurig accounted for more than 87% of single-serve sales, on a base of $201 million. By 2015, K-Cup sales reached $1.9 billion, while the competing systems as a whole never achieved more than $30 million in sales. By 2015, the revenue share of other systems was smaller than 1%. Because of this dominance, we will treat the Keurig

K-Cup brewing system as a monopoly in the single-serve market, focusing on the adoption of the

K-Cup brewers alone.

2.2 Keurig K-Cup Brewing System Expansion

Keurig strove for further platform growth by increasing number of brands available for the K-

Cup system. In 2006, GMCR, a coffee roaster company, acquired Keurig. At the same time, they brought in-house a number of existing brands who had licensed from Keurig the patents to supply coffee K-cups to grocery stores. The integrated company also worked to develop an active licensing and partnership program to bring existing national brands into the Keurig system (e.g.,

Starbucks and Eight O’Clock). In partner arrangements, the partner brands were able to leverage the manufacturing capabilities of the combined company, thereby eliminating the upfront capital

5Source: Kellogg case “Keurig: From David to Goliath - The Challenge of Gaining and Maintaining Marketplace Leadership” and Wikipedia. 6See Kellogg case “Keurig: From David to Goliath” for detailed discussion on the performance of competing brewers during 2003 to 2008.

6 or leasing costs needed to operate their own production lines. In return, they paid Keurig for manufacturing and occur additional licensing fees. Partners still brought the branded product to market independently. In licensing agreements, Keurig manufactures and takes the K-Cups to market in which case Keurig brings the market itself and pays a licensee fee per cup.

The main patent on K-Cup portion pack expired in September 2012. Before the patent ex- piration, Keurig acquired eight brands (e.g., Tully’s and Donut Shop), licensed six brands (e.g.,

Newman’s Own and Eight O’Clock), and added Starbucks and Folgers as partner into the K-Cup system. A few roasters developed compatible products to be used in the K-Cup brewers including brands manufactured by the Rogers Family Company, but the set of competing brands achieved very limited success prior to the patent expiration. After patent expiration, many more competing brands entered the system since entry was no longer legally restricted.

After patent expiration, Keurig continued to engage with national brands to encourage them to partner rather than compete. Keurig was able to continue to attract major brands including switching from unlicensed/competitor to partner (Peet’s, Maxwell House). Mainly because of com- peting brand entry, the post-patent expiration triggered a rapid increase in the number of brand choices in the K-Cup system compared to prior to expiration, though none were as well-known as the existing national brands.

3Data

Brewer and coffee sales data were obtained from the IRI InfoScan scanner data base. Sales data are available at the city, weekly, and UPC-coded product level for 65 IRI cities from 2010 to 2015. IRI collects average price and cups sold (for K-Cups, 1 portion pack is equivalent to 1 cup; for ground and whole bean coffee, 0.4 oz is equivalent to 1 cup), distribution (average product availability weighted by store all commodity volume in that city), number of items (UPC-coded products) sold per store, and percent of items on feature and display. We aggregate the data by city, month, and product choice, which is defined below. The average prices are defined as monthly dollar sales divided by monthly cup sales. Distribution, items sold per store, and the merchandising variables

7 are the averages for each month.

Installed base, coffee segment penetration rate, and individual demographics were obtained from the IRI panelist database. Data are available at the aggregate panelist, city, and year level from

2012 to 2015. We use the annual K-Cup segment penetration rate (percent of panelists that ever bought a K-Cup product in a given city and year) as a measure of the K-Cup brewer’s installed base. These yearly installed base measures from the panelist data are used to scale the monthly level of brewer sales observed in the point of sale data. This scaling is important because we do not observe the universe of brewer sales (for example, Amazon, Costco, and Bed Bath & Beyond are not in our data). Details of the scaling procedure are given in Appendix A.

For our analysis, we use the IRI city data, which is similar to the data approach taken in Nevo

(2000b). For our purposes, we wish to include the cities with the largest populations in order to ensure sufficient sample size to obtain an accurate estimate fo the installed base. In addition, IRI doesn’t track merchandising variables for all cities and we drop any cities for which this data is missing. As a result, we select the top 15 IRI cities for our analysis. 7

Product choices for coffee are defined as brand-segment combinations. Coffee segments include ground, whole bean, and K-Cups. We use the term K-Cup to refer to both K-Cups and K-Cup brewer compatible single-serve products. Note that Starbucks ground and Starbucks K-Cup are treated as different options. Ground and whole bean products are relevant to our study because they affect the marginal benefit of adopting a K-Cup brewer. This will be explained in detail in the model section below. We exclude instant and ready-to-drink coffee from the analysis because

1) they are relatively small segments and 2) previous research suggests their substitution with K- cups is quite limited (see Ellickson, Kong, and Lovett 2016). We include 10 ground coffee, 3 whole bean coffee, and 50 K-cup brands, which are selected based on median market share among the 15 cities in 2015, for each segment respectively. We include more K-Cup brands since the focus of this paper is on measuring the impact of K-Cup brand variety on K-Cup brewer adoption, and, in doing so, we need to capture as much of the benefits of this variety as possible. The included brands constitute about 67% of total coffee revenue, while the included K-Cup brands capture 98% of total

7We also examined the top 30 IRI cities as compared to the top 15 in order to evaluate whether this selection is likely to lead to bias. We found no meaningful differences in demographic make-up. Details available upon request.

8 K-Cups revenue. Observations with lower than 5% distribution are also excluded because these cases include oddities like new product test markets, initial product roll-outs with reduced pricing

(but low volume), and clearance of discontinued products, none of which are a good characterization of typical demand. In total, these omitted cases comprise about 0.24% of the total inside coffee revenue.

The market size for coffee is defined as 4*days in month*number of coffee buying households in the market. Note that we are assuming an average coffee buying household consumes a maximum of

4 cups of coffee per day. The number of coffee buying households in a market is the projected total households in that city (linearly interpolated to the monthly level based on annual data) times the penetration rate of the coffee category (averaging over 2012 to 2015) that is obtained from the IRI panelist data. The average penetration rate for coffee varies between 58% (San Francisco/Oakland,

CA) to 80% (Orlando, FL) for the sample.

Brewer choice options are defined as manufacturer-product lines. Product line information was collected online.8 We include the top 12 K-Cup brewers in the analysis, which includes six Keurig

K-Cup brewers, three Keurig 2.0 brewers, and three Keurig co-branded brewers (produced by the licensee manufacturer Mr. Coffee). All K-Cup brewers use the same brewing technology and operate similarly. Rather than brewing mechanism, product lines are differentiated more by features such as water reservoir size, cup size and water temperature options, LCD display, programmable on/off switch, and the brewer’s appearance. New models in the same product line typically have updated designs and new or improved features. The 12 brewers capture more than 99% of the total K-Cup brewer units sold. As above, observations with lower than 5% distribution were excluded. These cases are about 6.5% of the total brewer units sold.

General descriptive statistics are provided in figure 1 and table 1. A vertical line indicating the expiration date of K-Cup pod patent is shown in all figures. From the top left panel of figure 1, the differences in average prices across the segments is celar. The average price per K-Cup pod is about $0.60, which is more than six-fold the price of ground coffee per cup (around $0.10), and three times that of whole coffee beans (around $0.20). Despite a slight increase from the beginning

8See, for instance, http://coffeejustright.com/compare-keurig-models/ for a detailed discussion on the K-Cup brewer product line.

9 of the data period to the end of 2011, prices for ground and whole bean are relatively stable over time, while price of K-Cup peaked at more than $0.60 in mid-2012, and graduately went back to

$0.60. The top right panel shows that the price of K-Cup brewers varies from $50 to $175 across product lines. The Mr Coffee co-branded K-Cup brewers is a low end option, with prices at or below $75. The Keurig Platinum is the high end option, with average price around $175. The

Special Edition, and 2.0 series are relatively high end brewers. The middle left panel shows that the K-Cup segment sales have grown significantly over the years. The Keurig partner brands have the largest volume sales among all relationship types at the end of 2015, followed by Keurig’s own brands. Relating the sales to the middle-right panel of figure 1 and table 1, one can see that the

first partner brand, Folgers, entered in late 2010 and that it and Starbucks dominate the partner growth. In contrast, the competitor type has the largest number of brand options, but relatively low total sales. This stark difference in concentration of sales suggests that simply using a count of brands cannot capture the rich heterogeneous role that these brands play in shaping Keurig system growth. The middle and bottom left graphs also indicate that there exists significant seasonality in demand for brewer but not as much in K-Cup consumables. Finally, the bottom right panel demonstrates wide regional variation in both the initial level of the K-Cup brewer installed base and the rate at which it increases.

4 Model

In this section, we develop a static structural demand model for pruchasing the K-Cup system, as well as demand for coffee consumable products themselves (both K-Cups and conventional whole bean and ground coffee). Our overall modelling approach is to adapt the classic hardware-software demand framework to a static choice setting.9 To fix terminology, the K-Cup brewer is the “hard- ware,” and the K-Cup coffee products are the “software” required to utilize the hardware. These terms will be used interchangeably hereafter.

9We will explain our choice of a static framework shortly. For the standard dynamic approach, see the applications to the razor/razor blade by Hartmann and Nair 2010 and to the console/video game settings by Lee 2013 and Derdenger 2014.

10 Figure 1: Brewer and Coffee Statistics

Product Price Brewer Price

0.6

150 Keurig 2.0 K200 Keurig 2.0 K300 Keurig 2.0 K400 Keurig Elite Keurig Mini Plus 0.4 GROUND COFFEE Keurig Other 1 K−CUP Keurig Other 2 WHOLE BEANS Price 100 Keurig Platinum price per cup Keurig Special Edition MR COFFEE − KG1 MR COFFEE − KG2 MR COFFEE − KG5

0.2

50

Jan 2010 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015 Jan 2010 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015 time time

Product Sales Number of K−Cup products 8e+07 16

6e+07 12

COMPETITOR COMPETITOR COMPETITOR/PARTNER COMPETITOR/PARTNER 4e+07 LICENSED LICENSED OWNED 8 OWNED Cupsales PARTNER PARTNER

2e+07 4

0e+00

Jan 2010 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015 Jan 2010 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015 time

Installed Base

30 Baltimore, MD/Washington, DC Brewer Sales Chicago, IL Columbus, OH Dallas/Ft Worth, TX 200 Denver, CO Houston, TX Los Angeles, CA 20 Miami/Ft Lauderdale, FL 150 Minneapolis/St Paul, MN

Percentage New York, NY Orlando, FL Phoenix/Tucson, AZ 100 San Francisco/Oakland, CA St Louis, MO Unit sales (in 1000) Tampa/St Petersburg, FL 10 50

0 Jan 2010 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015 Jan 10 Dec 10 Dec 11 Dec 12 Dec 13 Dec 14 Dec 15 time

11 Table 1: Top 3 Selling K-Cup Brands by Group Group Brand Entry date (m/y) Ave. Share Competitor PRIVATE LABEL* 6/12 5.5% DON FRANCISCOS FAMILY RESERVE 1/13 1.5% BARNIES COFFEE KITCHEN 10/13 1.3% Competitor/Partner PEETS 5/13 3.7% MCCAFE 12/14 3.1% GEVALIA 12/12 2.9% Licensed NEWMANS OWN ORGANICS 1/10 8.2% CARIBOU COFFEE 1/10 6.9% EIGHT O CLOCK 8/12 4.1% Owned GREEN MOUNTAIN 1/10 21.0% TULLYS COFFEE 1/10 6.3% DONUT SHOP 3/10 6.0% Partner STARBUCKS 11/11 17.6% FOLGERS 10/10 12.4% DUNKIN DONUTS 5/15 5.6% Note: Share is average share in K-Cup segment revenue in city month *There are private labels in competitor group as well as partner group

Even though the K-Cup brewer is a durable good, it differs from the video game industry for at least two important reasons. First, while game consoles have declining prices over time and new generations are released every few years, in contrast, brewer prices vary little apart from seasonal changes, and true innovation was limited. Second, buying the K-cup brewer does not “lock-in” consumers to that system. Given the wide distribution and low prices of standard drip brewers, we assume everyone has access to such a brewer. This implies the K-cup brewer simply increses the number of available coffee options for those who choose to adopt. For these reasons, consumers are less likely to expect (and wait for) a price decline or a better brewing technology. As a result, we make the simplifying assumption that consumers believe future prices, varieties and qualities will be the same as today, and maintain this assumption each month.

Note that, similar to the video game literature, coffee quality and availability influences brewer demand through the value of the K-cup coffee options. We formulate this influence as the expected maximum coffee utility, as captured through an inclusive value term. Note that the inclusive value term allows for heterogeneous tastes for coffee that impact hardware adoption. Because consumers leave the hardware market after purchase of a brewer, the composition of consumers in the hardware

12 market changes over time. To account for this endogeneous market evolution, we jointly estimate the demand system for both coffee and brewers. Capturing the endogenous market evolution is central to correctly identifying the size of the impact of K-cup coffee brands on brewer adoption.

On the brewer side, consumers must decide whether to adopt a K-Cup brewer (vs. choosing no K-cup brewer). In each month, if they do not already own a Keurig machine, we assume that consumers considering buying and may purchase one of the available K-Cup brewers, h H . They 2 t exit the brewer market after purchase. Based on the assumption that all consumers are endowed with a drip brewer, non K-Cup brewer owners may purchase any non K-Cup products j J NK 2 t NK at any time, where Jt is the set of available ground and whole bean products at time t; K-Cup brewer owners may purchase any coffee product j J , where J is the extended set of available 2 t t ground, whole bean, and K-Cup coffee at t.

4.1 Demand for Brewers (Hardware)

First consider the hardware decision. In every month t,consumeri decides whether or not to purchase a K-cup brewer h H . Since consumers will only purchase the hardware once and then 2 t exit the hardware market forever (we do not consider replacement purchases), the consumer takes into account the lifetime utility obtained from adopting the K-Cup brewer h at time t:

u˜hw = ↵K + ↵hw log(phw)+Xhwhw + ⇠hw + ✏hw,h H , iht it i ht ht ht iht 2 t

K where it is the expected present-discounted value (PDV) of choosing the best option among both K the K-Cup (KCt)anddripbrew(NKCt)productsinperpetuity(it will be defined more formally hw hw in section 4.2), pht is the price of brewer h at market t (city-month), ↵i is a heterogeneous hardware price coefficient that is normally distributed with mean ↵¯hw and standard deviation

p,hw (↵hw =¯↵hw + p,hwvhw,vhw N(0, 1)), Xhw are observed characteristics of brewer h at i ⇠ ht time t, which include distribution, display, feature, as well as brewer, month-in-year, and city fixed

hw hw effects. ⇠ht is unobserved brewer characteristics, and ✏iht is an idiosyncratic type 1 extreme value (T 1EV )shock.

13 The utility of no purchase, which implies continued use of the standard auto-drip brewer, is given by

st NK hw u˜it = ↵ it + ✏i0t ,

NK hw where it is analogously the expected life time inclusive value of non K-Cup products and ✏i0t is T 1EV shock. Note that, by constrast, in a dynamic adoption model consumers would also account for the option value of delaying their purchase.

st Normalizing u˜it gives

uhw = ↵ + ↵hwphw + Xhwhw + ⇠hw + ✏hw,h H iht it i ht ht ht iht 2 t and

st hw uit = ✏i0t , where K NK. This captures i’s marginal benefit of adding K-Cup products to her software it ⌘ it it choice set.

Given the T 1EV error assumption, consumer i’s conditional probability of choosing h at t, conditional on not having adopted any hardware at ⌧

hw hw exp(Viht ) Piht = hw l H exp(Vilt )+1 2 t P where V hw = uhw ✏hw. The total conditional probability of adopting the K-Cup system, namely, iht iht iht purchasing any K-Cup brewer at t,isthen

exp(V hw) hw hw h Ht iht P = P = 2 (1) it iht exp(V hw)+1 h H Ph Ht iht X2 t 2 P To construct the unconditional choice probabilities, we specify a process for how the consumer’s

NK hardware ownership status evolves for a given city. Denote ⇡it as i’s likelihood of remaining in the hardware market at end of t. This is equivalent to the probability of not adopting any hardware

14 in ⌧ t. It is then easy to see that ⇡NK evolves according to  it

t NK NK hw NK hw ⇡it = ⇡it 1 1 Pit = ⇡i0 1 Pi⌧ , (2) ⇤ ⇤ ⌧=1 Y

NK where ⇡i0 is the initial likelihood of being a non K-Cup owner. K The likelihood of being a K-Cup owner, denoted as ⇡it (equivalently, the probability of adopting any K-Cup brewer at current period t or any period before t), is simply

⇡K =1 ⇡NK. (3) it it

Finally the aggregate unconditional probability of adopting a K-Cup brewer h at t is

˜hw NK hw hw Pht = ⇡it 1 Piht dP (v ), ⇤ Z which will be matched to the observed shares in the data.

4.2 Demand for Coffee (Software)

Now consider the “software” market where individuals are choosing what coffee products to consume.

Consumer i’s utility of buying coffee product j J at t is given by 2 t

sw sw sw sw sw sw sw uijt = ↵i pjt + Xjt + ⇠jt + ✏ijt,

sw sw where pjt is price of j at t; ↵i is a heterogeneous software price coefficient normally distributed with mean ↵¯sw and standard deviation p,sw (↵sw =¯↵sw + p,swvsw,vsw N(0, 1)), Xsw are i i i ⇠ jt observed characteristics of software j at t, which include distribution, average available items per store, display, feature, product fixed effects (K-Cups are given product-year fixed effects to capture changing perceived quality for these new products), month-in-year seasonality dummies, and city-

sw sw year location-time dummies. ⇠jt again captures unobserved product characteristics and ✏ijt is a T 1EV error term.

15 At every t, when choosing which software (coffee) to buy (or not to buy at all), the coffee options that consumer i faces depends on their brewer ownership status. NK-type owners can only choose ground or whole bean coffee, since they don’t have access to a K-cup brewer, while K-type owners can choose between K-Cups, ground coffee, or whole beans. The conditional probability for i of choosing j at t conditional on being a K type at t is given by

sw K,sw exp(Vijt ) Pijt = sw ,j Jt, (4) 1+ k J exp(Vikt ) 2 2 t P where V sw = usw ✏sw. ijt ijt ijt Similarly, the conditional probability of choosing j at t conditioned on being an NK-type at t is given by

sw exp(Vijt ) NK sw ,j Jt 1+ NK exp(Vikt ) NK,sw k Jt 2 Pijt = 8 2 . > P <>0,otherwise > According to Bayes rule, the aggregate:> unconditional choice probability of j at t is given by

K K,sw NK NK,sw sw NK ⇡it Pijt + ⇡it Pijt dP (v ),j Jt P˜sw = 8 2 jt ⇣ ⌘ >R K K,sw sw < ⇡it Pijt dP (v ),otherwise, >R :> which will be matched to the observed data analogue in estimation.

4.3 Coffee (Software) Inclusive Value

We define I , the software inclusive value associated with brewer ownership I NK,K ,as it 2{ } the expected present-discounted maximum utility i could obtain from the complementary coffee consumables, which depends on the individual’s current brewer ownership status, as well as what

I coffee products are available at time t.BasedontheT 1EV error assumption, it is the present-

16 discounted logit inclusive value given by

1 ln exp(V sw)+1 ,I= K 1 j Jt ijt I = 8 ⇤ 2 (5) it ⇣ ⌘ > 1 P sw < 1 ln j J NK exp(Vijt )+1 ,I= NK, ⇤ 2 t > ⇣P ⌘ :> where is the discount factor. This definition means that consumers construct the present- discounted software inclusive value based on the current conditions of software availability, price and other characteristics. This reflects our assumption that consumers are forward looking but do not expect product quality, availability, and prices to change in the future. This assumption implies that consumers make the brewer decision based on knowledge of the currently available consumables and their prices. Note that, in practice, this implies that we should not expect to see brewer sales slow down or accelerate ahead of new consumable introductions.10

The marginal benefit of adding K-Cup products to her coffee product choice set it is given by

1 K NK = ln exp(V sw)+1 ln exp(V sw)+1 . (6) it ⌘ it it 1 2 0 ijt 1 0 ijt 13 j Jt j J NK X2 2Xt 4 @ A @ A5 5 Estimation and Identification

To estimate the static demand model for hardware and software simultaneously, we nest within a

fixed point routine two random coefficient aggregate demand models, i.e., the BLP model for each

K market. The two markets are linked through the inclusive value (it)andtheinstalledbase(⇡it ). The nested fixed point routine iteratively updates the parameters from each choice problem until the implied distribution of consumers’ brewer ownership likelihood is consistent across both markets.

The (non-linear) model parameters are recovered by optimizing a GMM objective function for which

hw the moment conditions are the interactions between the unobserved mean product qualities (⇠t sw and ⇠t )andinstruments.Algorithmssimilarinspirithavebeenappliedinotherplatformstudies (See also Lee 2013; Derdenger 2014; and Derdenger and Kumar 2013 for applications in dynamic settings). We set the discount factor =0.975 following the literature on dynamic models in video

10As noted earlier, we do not find evidence of such rate changes in the data.

17 games (Nair 2007). For the rest of this section, we first describe the main components nested in the

fixed point routine, followed by instruments and identification. We discuss estimation procedure last.

5.1 Mean Brewer Utility

Denote the mean utility for brewer h at t as

hw ↵¯hwphw + Xhwhw + ⇠hw, ht ⌘ ht ht ht

hw which then allows brewer utility uiht to be written as

hw p,hw hw hw hw hw uiht = ↵ it + vi pht + ht + ✏iht.

For a given set of non-linear parameters ✓ ↵,p,hw,p,sw ,coffeeinclusivevalue , 1 ⌘ { it}i,t NK hw and composition of potential hardware consumers ⇡it i,t 0,themeanbrewerutilityht is hw obtained by solving the BLP contraction mapping, using 50 standard normal draws for vi from a Halton sequence:11

hw(n) hw(n 1) hw = +ln(s ) ln P˜ , (7) ht ht ht ht ⇣ ⌘ ˜hw where Pht is the predicted share of h given by equation 1 and sht is the observed brewer share defined as: unit salesht sht =IBt , ⇤ l H unit saleslt 2 t P where IBt IBt IBt 1 is the change of installed base from t 1 to t. The tolerance for the ⌘ hw 12 sup-norm is set to 10 .

5.2 Mean Software Utility and Inclusive Value

Define the mean utility for complementary product j at t as

11The same set of random draws of individual tastes are used for all city months.

18 sw sw sw sw sw hw jt =¯↵ pjt + Xjt + ⇠jt

For a given set of nonlinear parameters ✓1,consumercompositioninpotentialhardwaremarket

NK K sw ⇡it i,t 1,andcompositionoftheplatformusers ⇡it i,t 1,themeanproductutilityjt is sw obtained through the BLP contraction mapping using 50 standard normal draws for vi from a Halton sequence:12

sw(n) sw(n 1) sw = +ln(s ) ln P˜ , (8) jt jt jt jt ⇣ ⌘ ˜sw where Pjt is the predicted share of j given by equation 4 and sjt is the observed coffee product share defined as: cup salesjt sjt = . market sizet

We set the tolerance for sw to be the same as for hw.Usingtheseimpliedsw values, the inclusive value it is then computed based on equations 5 and 6.

5.3 Initial Conditions and Evolution of Consumer Brewer Ownership Status

Given ✓ , ⇡NK, and hw ,consumeri’s likelihood of either remaining in (⇡NK)orbeing 1 i0 { it}i,t ht h,t it K out of the market (⇡it )atendof t,evolveaccordingtoequations2and3,respectively.Weassume that ⇡NK =1 IB for all i, where IB is the fitted initial installed base of K-Cup brewer equal to i0 0 0 the extrapolation parameter ↵ˆ for each city (See Appendix A). This simplifying assumption implies that early adopters do not adopt because of unobservables and that the distribution of unobservables for early adopters and non-adopters is the same. Although this simplifying assumption is common in the platform adoption literature, it can lead to two potential issues. First, the fraction of high type consumers owning the K-Cup system should be systematically under-reported. Second, unlike in most theoretical predictions for adoption models, the distribution of price sensitivity for adopters

12Similar to the hardware market, we use the same set of individuals for all software markets (city month). Note that we draw v independently for the hardware market and software market. In future work, we plan to allow these price sensitivities to be correlated.

19 can first decrease in sensitivity before increasing. This can potentially lead to undesirable biases in the price sensitivity parameters. As a result, in future work we plan to explore alternative assumptions/approaches.

5.4 Moment Conditions, Instruments, and Identification

We construct moment conditions using the interactions between the unobserved mean product

hw sw qualities (⇠t and ⇠t )andinstruments:

E(Zhw ⇠hw)=0 (9) ht ⇤ ht

E(Zsw ⇠sw)=0 (10) jt ⇤ jt

hw sw where Zht ,Zjt are vectors of instruments for the brewer and coffee markets respectively. The sw parameters to be estimated are all the nonlinear parameters ✓1 since the linear parameters and

hw 1 will be given as a function of ✓1. The GMM estimator is ✓ˆ1 = arg min g(✓1)0 (Z0Z) g(✓1), where g(✓1) is a vector of stacked moments given by9 and 10 and Z is a block-diagonal matrix consisting of Zsw and Zhw.

hw For the hardware market, we use Xht as its own instrument, treating price as conditionally exogenous given the set of included covariates.13 For identifying the heterogeneity parameter p,hw, we construct a set of instruments proposed by Gandhi and Houde (2015), which measure how isolated a product is in characteristics space. These instruments include 1) the number of other brewer choices in each of the brewer-price-difference bins14 and 2) the sum of price differences. For identifying the nonlinear parameter ↵,weusetheinclusivevalueaveragingoverallindividuals,

1 i.e., t = ns i it where ns is the number of simulated individuals. sw Similarly,P for the software market, we use Xjt as its own instrument, treating price as con- ditionally exogenous given covariates.15 For identifying the heterogeneity parameter p,sw,we

13The main concern for endogenous brewer price is distribution, promotion and seasonality, which are included in the model. 14We construct the GH instruments using 6 grid points for the brewer-price-difference bins 15Note that we’ve controlled for distribution, promotion, number of items, and seasonality. In addition, we use year*brand dummy for the K-Cup products to control for changes in new product quality. Our belief is that these

20 also use Gandhi and Houde instruments which include 1) the number of products in each of the product-price-difference bins16,2)thenumberofproductsfromsamesegmentineachoftheseprice- difference bins, and 3) the sum of the price differences in the product’s own segment. In addition, we use average segment prices interacting with segment dummies as instruments for p,sw.Forthe three segments in our setting, this yields 9 instrumental variables.

5.5 Estimation procedure

1. Given nonlinear parameters ✓ ,use⇡˜NK =1 IB as the initial guess of ownership likelihood 1 it t ˜sw ˜hw ˜ to obtain initial guess of jt , ht , it, by the following steps

(a) Solve for ˜sw assuming p,sw=0 (i.e., homogenous consumers) via contraction mapping ln sjt ˜sw in 8 (use s0t as initial guess for )

⇣ ⌘ sw (b) Compute homogeneous it =t given ˜ using equation 6

˜hw p,hw sht (c) Solve for given t and =0via contraction mapping in 7(use ln 1 IB as t initial guess for ˜hw) ⇣ ⌘

˜ ˜sw (d) Obtain heterogeneous it given ✓1 and jt

˜sw ˜hw ˜ K 2. Using jt , ht , it as the initial input, iterate the following steps until tolerance on it is 10 reached (set to 10 for sup-norm)

hw NK (a) Solve for ht via contraction mapping given it and ⇡it

NK (b) Update ⇡it using 2

sw NK (c) Recover jt via c.m. given ⇡it

NK (d) Update it and ⇡it

hw 3. Recover the unobserved mean component ⇠ht in hardware utility through

1 hw = X 0 ZWZ0 X X 0 ZWZ0 hw ⇣ ⌘ covariates control for the main threats to causal inference on product price. 16We construct the GH instruments using 10 grid points for the product price bins

21 ⇠hw = hw Xhwhw ht ht ht 1 where X = Xhw, Z = Zhw,andW = Zhw0 Zhw is the weighting matrix ⇣ ⌘ sw 4. Similarly, recover the unobserved mean component ⇠jt in software utility using

1 sw = X 0 ZWZ0 X X 0 ZWZ0 sw ⇣ ⌘

⇠hw = sw Xswsw jt jt jt 1 where X = Xsw, Z = Zsw,andW = Zsw0 Zsw ⇣ ⌘ 5. Compute GMM objective function given by

0 1 1 hw0 hw 1 hw0 hw 1 hw0 hw nhw ⇠ Z nhw Z Z 0 nhw ⇠ Z f = 2 3 2 3 2 3 1 sw0 sw 1 sw0 sw 1 sw0 sw nsw ⇠ Z 0 nsw Z Z nsw ⇠ Z 6 7 6 7 6 7 4 5 4 5 4 5 6. Search over ✓1 repeating the above steps until the GMM objective function is minimized.

6 Results

The demand system parameter estimates and standard erros are presented in table 2.17 We begin with our main parameter of interest, the coefficient on the inclusive value, ↵.Ourestimateof↵ is positive (0.07) and significant. This implies that consumers respond to the coffee options available to them when deciding whether to buy a K-Cup brewer. The magnitude, however, is difficult to interpret directly due to the complex, non-linear model, and for this reason, our counterfactuals

(see section 7) focus on elaborating the role of consumable options in shaping the evolution of the

K-Cup brewer sales.

17Due to the complexity of the analytical solution and computational burden of the bootstrap method, the reported NK standard errors are computed without accounting for the uncertainty in consumer brewer ownership likelihood ⇡it and inclusive value it. Hence standard errors reported here are a lower bound of the actual standard errors. Standard errors based on analytical solution or bootstrap method will be provided in the updated version.

22 Table 2: Estimation Results

Nonlinear parameters ↵ 0.073*** Hardware parameters ↵¯hw -1.906*** (0.005) (0.109) p,hw 0.028 distribution 0.062*** (0.400) (0.002) p,sw 6.134*** display 0.137* (0.160) (0.082) feature 0.048 (0.054) Software parameters ↵¯sw -13.442*** (0.303) distribution 0.035*** (0.000) Number of HW observations 5,031 nitermsperstore 0.114*** (0.001) Number of SW observations 36,468 display 0.526*** (0.023) feature 0.398*** (0.026) Note: Brewer, coffee brand-segment, month-in-year, and city fixed effects for both hardware and software demand are not reported; ***Significant at the 1 percent level **Significant at the 5 percent level *Significant at the 10 percent level

23 The mean price coefficient for brewer demand is negative and significant with a value of -1.91 but heterogeneity in price sensitivity p,hw is not statistically significant. Because the brewer prices are in log space, aggregate consumer responses to changes in high prices are smaller than those cor- responding to lower prices. This pattern of response is similar to that predicted by heterogeneity in a linear price model. Considering the elasticity, under homogeneous price sensitivity this price elasticity would be close to the estimate, so price elasticities are slightly smaller in absolute mag- nitude than -1.9. This suggests that consumers are price elastic regarding brewer prices. Turning to the merchandizing parameters, the parameters for distribution and display are significant with the expected sign (positive) and have reasonable magnitudes, 0.06 and 0.14, respectively. Feature ads are not significant and small in magnitude. This result suggests that consumers respond to display but we do not find evidence of response to feature ads.

Turning to the coffee market where prices enter linearly, the mean price coefficient is negative

(-13.4) and significant and the unobserved heterogeneity p,sw is large (6.1) and meaningful. These estimates indicate that the coffee market contains many price sensitive individuals, but also some very price insensitive individuals.18 Further, it suggests that, because of the vertical differentiation in segment prices (shown in figure 1), consumers are more likely to switch brands within the same segment versus between segments.

For the coffee market, the coefficients for the merchandizing variables again have the expected positive signs and are significant. Distribution (0.03), display, (0.53) and feature (0.40) have the same relative ordering as we found in the brewer market. The number of items under the brand is also positive and relatively large in magnitude (0.11), suggesting that having greater selection for the brand increases the brand’s attractiveness. One might also interpret this result as indicating that shelf-space (for which number of items is a rough approximation) is an important determinant of demand for coffee consumables. 18The distribution of the price sensitivity parameter, i.e. N ↵¯sw, (p,sw)2 , implies a negligible estimated weight on positive values.

24 7 Counterfactual Results

Our counterfactuals focus on evaluating the impact of K-Cup coffee brand variety on the adoption of the brewer system. These analyses both calibrate the importance of indirect network effects and shed light on Keurig’s incentives for developing partner and licensing arrangements with existing national brands. We consider two additional dimensions of the incentive to partner here. First, if these relationships do not increase the adoption of the brewer system, it then raises greater concerns that the primary role of such relationships might be to foreclose potential alternative system entrants. Second, by examining how different partnering arrangements (owned, licensee, and so-called partner brands) shape the sales of Keurig’s owned brands, we can begin to evaluate the benefit of these alternative arrangements, versus maintaining strict ownership of any branded products. In this sense, we can shed light on Keurig’s incentives to build its own consumable brand versus its incentive to build the total system (hardware and software). These two incentives related questions are particularly relevant to this setting because the expiration of the K-cup patent for the consumables, which represents an end of the legal restriction on entry by competing, unlicensed coffee manufacturers, occurred well before the expiration of the hardware. This staggered timing of patent expirations suggests interesting implications from partnering.

Our counterfactuals focus on adjusting the set of options available to consumers. These simu- lated changes to the available options change the inclusive value, which then affects the adoption of the Keurig brewer system. Our analysis proceeds by examining how much the installed based changes under various counterfactual scenarios regarding these coffee options. In particular, we examine three sets of counterfactuals. First, we consider a completely fictional case with no con- sumables. Although unrealistic this scenario can illustrate the maximum impact of the consumables market on brewer adoption. Second, we consider scenarios where particular relationship types do not exist (i.e., no owned brands, licensed brands, partner brands, or competitor brands). This helps to illustrate how beneficial each of these types was for the evolution of the system and can shed light on the trade-offs Keurig was making between building its own brand versus broadening its appeal through more brand variety. Third, we consider the scenarios in which the various relationship

25 types become unavailable starting at different points in time. This analysis helps to isolate the entry timing of the brands under the various types of relationships (e.g., later entry of partner vs. owned brands) from the overall attractiveness of those brands to consumers.

We now discuss how we implement our current counterfactual analyses. In our analyses we hold at the actual (observed or estimated) levels the price, merchandising, and the unobserved characteristics of the brewers and coffee consumables. In this sense, the counterfactuals are a partial equilibrium analysis because prices aren’t reoptimized, i.e., firms do not respond (in price) to the reduction in the number of competitors. For fully dynamic firms in this hardware-software setting, it is difficult to predict how prices will adjust in response to fewer K-cup compatible brands in the consumables market. However, a simple prediction is possible for static price-setting

firms: as the number of competitors falls, under fairly general conditions, the prices of remaining consumable brands should rise. Because demand curves are downward sloping, this implies that the counterfactual brewer sales should be lower than what we predict in our counterfactuals. Hence, relative to equilibrium pricing with static Nash behavior, our counterfactual estimates of the lost adoptions are biased downward (i.e., adoption doesn’t fall enough). On the other hand, in the more complex setting with the hardware-software joint optimization, the loss of a brand within the

Keurig system reduces the incentive of consumers to adopt the brewers and, hence, generates an incentive on the part of Keurig to “make-up” for the lost brand (and adoptions) by reducing its own brand price. Whether this dynamic hardware-software incentive to reduce price is stronger than the incentive to exploit the additional monopoly power and raise price is unclear ex ante. In future work, we aim to incorporate reoptimized prices.19 However, our current counterfactuals provide a

first-order approximation to the relevant effects.

We also make two further assumptions that are likely to make our counterfactual estimates of lost installed base conservative. First, we assume that initial installed base for the beginning of

2010 does not change. Since this initial installed base is a function of the coffee consumables prior to 2010, we are implicitly assuming the K-Cup brands we drop still existed prior to 2010. If we

19This requires backing out marginal costs for the coffee market and then calculating the new optimal price equilibrium. Various supply-side assumptions can be made here to capture the relationships between Keurig and the licensee and partner brands including linear price agreements, non-linear contracts, or a bargaining model.

26 were to drop these brands from the pre-2010 period, the initial installed base and all subsequent levels would decrease accordingly. Second, in the current counterfactuals, we do not allow for entry of competing brewing systems. Such competing systems might have been enticed to enter if major national brands like Starbucks and Eight O’clock were not already a part of Keurig. Rival entry would clearly decrease the installed base for Keurig, though the exact amount is unknown.

7.1 Counterfactual 1

In this first counterfactual, we drop all options from the coffee market, so that consumers are assumed to not respond to coffee products when making their brewer purchase decisions (in practice, we set ↵ the parameter on the inclusive value to zero). Table 3 summarizes the impact on the

K-Cup system installed base across the 15 IRI cities. We find a meaningful decrease in installed base: by the end of 2015, average installed base would decrease by 3.9 percentage points to 7.7, which is 15% to 30% of the actual installed base. Figure 2 contrasts the actual and counterfactual installed base percentages for the two cities with the largest–30% (St Louis, MO)–and smallest–15%

(New York, NY)–relative lost in installed base. This counterfactual result shows that the K-Cup coffee consumables has an important influence on K-Cup brewer adoption, though not an outsized one.

Table 3: Counterfactual 1 variable ave min max act IB(%) 29.3 19.7 35.8 ctf IB(%) 23.4 15.8 29.9 ctf act -5.9 -7.7 -3.9 ctfact act 20% 15% 30% Note: Statistics are computed using the outcomes at the end of 2015

7.2 Counterfactual 2

Next, to quantify the contribution of own, licensed, partner, and competitor K-Cup brands to system adoption, we simulate how the installed base would have evolved had the brands with each of these relationships with Keurig not actually existed. Table 4 reports the percentage change in

27 Figure 2: Counterfactual 1

St Louis, MO New York, NY 35 35 Actual Actual Counterfactual Counterfactual 30 30 25 25 20 20 Installed base Installed base 15 15 10 10 5 5

Jan2010 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015 Jan2010 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015

time time actual installed base if the brands corresponding to each of the relationship types were eliminated and their overall share in the K-Cup segment. We find that, on average, Keurig’s own K-Cup brands contribute the most to platform adoption and competitor brands the least. Partner brands, which include major national brands like Starbucks are a close second in contribution to the Keurig system expansion. Interestingly, partner brands also have the biggest variation across cities, suggesting that the partner brands’ strength in the overall coffee market may play a key role here (coffee brands exhibit strong regional tastes). Figure 3 plots lost installed base upon elimination of partner brands against in-K-Cup segment volume share of partner brands for 15 cities (based on total cup sales from 2010 to 2015). The positive correlation between the two provides evidence supporting our conjecture. Licensed brands play a smaller role in shaping the evolution, but that role is still larger than competing brands. To make more sense of the counterfactual results, we compare the magnitude of the influence on installed base to the share of K-Cup sales (as reported in the last column of table 4). We find that one is roughly proportional to the other, which is consistent with our expectation.

Figure 4 presents the counterfactual installed base evolution for the same two cities used to illustrate the counterfactual 1. This figure reveals that the impact of partner brands on installed base (purple dash-dotted line) is quite similar to that of owned brands in St. Louis, but is much

28 Table 4: Counterfactual 2 Removed cohort ave min max share Competitor 1.5 0.9 2.7 9.0 Competitor to partner 1.3 0.6 2.5 9.0 License 2.1 1.3 3.6 15.2 Own 5.2 4.3 6.6 35.3 Partner 4.9 2.9 7.6 31.4 Notes: Numbers in the first three columns are in percentage points of lost installed base ctf act ( act % )comparedbyendof2015;Lastcolumnisthecity-averageshare(intotalcupsales from 2010-2015) of the indicated relationship type in K-Cup segment

Figure 3: Counterfactual 2: Share of Partner Brand and Impact on Installed Base

●

7

●

● 6

● ●

● 5 ● Lost installed base ● ● ● ●

4 ● ●

●

3 ●

24 28 32 36 Share of partner brands (%)

29 weaker in New York City. Such a difference is likely due to the higher partner brand share in St.

Louis.

Figure 4: Counterfactual 2

St Louis, MO New York, NY share of partner brands 33.5% share of partner brands 24.3%

30 30

Actual Actual COMPETITOR COMPETITOR 20 COMPETITOR/PARTNER 20 COMPETITOR/PARTNER LICENSED LICENSED OWNED OWNED

Installed base PARTNER Installed base PARTNER

10 10

Jan 2010 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015 Jan 2010 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015

7.3 Counterfactual 3

Although the partner brands represent a significant component of the installed base growth, because partner brands enter later than owned brands (see figure 1), it is possible that this late entry is artificially diminishing their importance. To control for the impact of entry timing on a brand’s contribution to platform adoption, we adjust the previous counterfactual simulations by changing when the brands are dropped. Specifically, instead of dropping the brands from 2010 onward, we shift the drop date up by one year until 2015. Thus, we present the counterfactual average lost installed base for the years 2010 (which was previously reported in counterfactual 2), 2011, 2012,

2013, 2014, and 2015. The pattern of loss is consistent with the late entry concern. The relative effect of partner brands versus owned brands on installed base increases over time. By 2012 (the

30 year Starbucks enters), the influence on the average is equal, but by 2015, partner brands are almost

50% more influential than owned brands.

Table 5: Counterfactual 3 2010 2011 2012 2013 2014 2015 Own 5.2 5.0 4.5 3.6 2.5 1.1 Partner 4.9 4.8 4.5 3.8 2.9 1.6 ctf act Notes: The first two rows are percent decrease in installed based ( act %)asoftheendof 2015 were the brands of the indicated relationship type not available beginning in January of the indicated year. Average percentage decrease across cities is reported.

7.4 Summary

Our counterfactual experiments uncover several important features of the software-hardware mar- ket. First, K-Cup consumables have a strong influence on K-Cup system adoption. If consumers did not respond to the “software” market, the K-Cup system’s installed base would have been on average 20% lower by the end of 2015 than the actual level it achieved. Second, Keurig-owned

K-Cup brands on average have the largest total contribution (aggregating from 2010 to 2015) to the system adoption and competitor brands have the least contribution. In particular, installed base at end of 2015 would have dropped by 5.2% if Keurig owned brand were not available, 1.5% if competitor brands were not available, and 4.9% if partner brands were not available. Finally, the total contribution of Keurig’s partner brands increases over time, matching Keurig’s own brands by 2012 when Starbucks enters, and overshadowing Keurig’s own brands by 2015. By then they had almost 50% greater influence on the installed base than Keurig’s own brands.

8 Conclusion

In this paper, we propose a static demand system for brewer and coffee. We estimate the demand system using K-Cup brewer and consumable coffee sales data. Our data covers the period of major

K-Cup brand variety expansion through partnering and licensing prior to the expiration of an important patent and competitive entry after the expiration. The demand estimates document the

31 value of indirect network effects in the Keurig K-Cup brewer coffee system. Using the structural estimates, we simulate counterfactuals that quantify the importance of brand variety in driving the success of the overall Keurig system and demonstrate the role of Keurig’s partnerships with national brands in hastening the growth of the category. We find that K-Cup consumables have a strong influence on K-Cup system adoption, contributing to 20% growth of the system. Keurig-own brands have the largest total contribution (5.2%) to the brewer adoption, but partner brands are a close second (4.9%). Further, over time partner brands outstrip the influence on adoption of own brands with 50% more influence by 2015.

These results already present deep insight about the importance of partnerships and the partner selection strategy. We find that a small number of national brands that have strong brand equity to leverage into the K-Cup segment (ala the partner brands) provide much more system growth than even a large number of smaller brands (ala the competitor or licensed brands). Further, unlike the intuition that competition will erode the importance of these larger national brands, as the system grows, partners become relatively more important to growth. In our setting, not only would measuring indirect network effects through simple counts of network size be inaccurate, it would miss the main influences on platform growth.

Because we find that partnering generates system growth, this implies that Keurig’s relationships with other brands aren’t purely to foreclose the entry of competing systems. Future research could allow for entry of competing brewing systems in order to calibrate the relative size of the incentive to partner for Keurig system growth versus keeping potential entrants out. For Keurig, this incentive to foreclose has important implications because the software patent expires prior to the hardware patent. The staggered timing of competition into the two markets leads to interesting strategic incentives to tie up large players versus open the system to small players. Further, we could provide insight into which brands are most influential for locking in consumers and growing the system.

Future research could illustrate the incremental value to the Keurig system of partnering with particular brands that have greater brand equity in the mature ground and whole bean segments that can be leveraged into the K-Cup segment.

32 References

[1] Berry, S. T. (1994). Estimating discrete-choice models of product differentiation. The RAND

Journal of Economics, 242-262.

[2] Berry, S., Levinsohn, J., & Pakes, A. (1995). Automobile prices in market equilibrium. Econo-

metrica: Journal of the Econometric Society, 841-890.

[3] Clements, M. T., & Ohashi, H. (2005). Indirect network effects and the product cycle: video

games in the US, 1994–2002. The Journal of Industrial Economics, 53(4), 515-542.

[4] Corts, K. S., & Lederman, M. (2009). Software exclusivity and the scope of indirect network

effects in the US home video game market. international Journal of industrial Organization,

27(2), 121-136.

[5] Derdenger, T. (2014). Technological tying and the intensity of price competition: An empirical

analysis of the video game industry. Quantitative Marketing and Economics, 12(2), 127-165.

[6] Derdenger, T., & Kumar, V. (2013). The dynamic effects of bundling as a product strategy.

Marketing Science, 32(6), 827-859.

[7] Dubé, J. P. H., Hitsch, G. J., & Chintagunta, P. K. (2010). Tipping and concentration in

markets with indirect network effects. Marketing Science, 29(2), 216-249.

[8] Ellickson, P. B., Kong, P., Lovett, M. (2016) Private Labels and Retailer Protability: Bilateral

Bargaining in the Grocery Channel. Working paper.

[9] Gandal, N., Kende, M., & Rob, R. (2000). The dynamics of technological adoption in hard-

ware/software systems: The case of compact disc players. The RAND Journal of Economics,

43-61.

[10] Gandhi, A. and JF. Houde (2015). Measuring substitution patterns in differentiated products

industries - the missing instruments. Working paper.

33 [11] Hartmann, W. R., & Nair, H. S. (2010). Retail competition and the dynamics of demand for

tied goods. Marketing Science, 29(2), 366-386.

[12] Karaca-Mandic, P. (2011). Role of complementarities in technology adoption: The case of DVD

players. Quantitative Marketing and Economics, 9(2), 179-210.

[13] Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. The

American Economic Review, 75(3), 424-440.

[14] Knittel, C.R. and K. Metaxoglou (2008). Estimation of random coefficient demand models:

Challenges, difficulties and warnings. Technical report, National Bureau of Economic Research.

[15] Lee, R. S. (2013). Vertical integration and exclusivity in platform and two-sided markets. The

American Economic Review, 103(7), 2960-3000.

[16] Nair, H. (2007). Intertemporal price discrimination with forward-looking consumers: Applica-

tion to the US market for console video-games. Quantitative Marketing and Economics, 5(3),

239-292.

[17] Nair, H., Chintagunta, P., & Dubé, J. P. (2004). Empirical analysis of indirect network effects

in the market for personal digital assistants. Quantitative Marketing and Economics, 2(1),

23-58.

[18] Nevo, A. (2000a). A Practitioner’s Guide to Estimation of Random-Coefficients Logit Models

of Demand. Journal of Economics & Management Strategy, 9(4), 513-548.

[19] Nevo, A. (2000b). Mergers with differentiated products: The case of the ready-to-eat cereal

industry. The RAND Journal of Economics, 395-421.

[20] Tenn, S., & Yun, J. M. (2008). Biases in demand analysis due to variation in retail distribution.

International Journal of Industrial Organization, 26(4), 984-997.

34 Appendix A

The IRI brewer sales data is collected from all supermarkets, mass merchandise retailers, drug and pharmersutical stores, BJ’s and Sam’s. The IRI brewer sales accounts for approximately 40% of total brewer sales and does not cover online, Costco, and specialty retailers like Bed Bath and

Beyond. Hence we extrapolate the IRI K-Cup brewer sales for all retail channel K-Cup brewer sales based on yearly installed base and then interpolate yearly installed base to monthly using extrapolated brewer sales. In particular, we run the following regression for each city j seperately:

cumsalejt IBjt = ↵j + j ,t= 2012,...,2015 ⇤ householdjt

where cumsalejt is the cummulative K-Cup brewer sales since the beginning of data period. The extrapolation parameter ↵j is an estimate of the initial installed base (end of 2009) for city j and j reflects the ratio of total brewer sales in city j and IRI brewer sales in city j.Asexpected,↵j > 0 and j > 1 for all j. The average of j is around 2, implying that on average IRI K-Cup brewer sales capture 50% of total K-Cup brewer sales. Finally, the interpolated installed base is given by

cumsalejt IBˆjt =ˆ↵j + ˆj ,t= Jan2010,Feb2010,...,Dec2015 ⇤ householdjt

35