Tangible Hedonic Portfolio Products: A Joint Segmentation Model of Music CD Sales by Wendy W. Moe and Peter S. Fader The Wharton School University of Pennsylvania Marketing Department 1400 Steinberg Hall-Dietrich Hall Philadelphia, PA 19104-6371 telephone: (215) 898-7603 fax: (215) 898-2534 e-mail: [email protected] July 1999 Wendy W. Moe is a doctoral candidate in Marketing and Peter S. Fader is an Associate Professor of Marketing. The authors thank Capitol Records for the data used in this research and are grateful for the comments of numerous colleagues at Wharton including Eric Bradlow, Dave Schmittlein, and Christophe Van den Bulte. Tangible Hedonic Portfolio Products: A Joint Segmentation Model of Music CD Sales Abstract This paper presents a framework with which to better differentiate between a wide array of hedonic products. Specifically, we define and discuss characteristics of hedonic portfolio products and offer a joint segmentation model appropriate for this class of products. We construct a flexible duration-time model that can be used to capture the dynamic purchase patterns across a broad portfolio of different albums. We apply the model to music CD sales for 20 different albums. The model simultaneously models multiple albums and can accommodate a very large degree of product heterogeneity through product clusters and model covariates. We allow explanatory variables to have a differential impact on both components of the model, i.e., accelerating purchase rates within segment, and/or changing the proportions drawn by each cluster. To illustrate the role of covariates in our model, we discuss the different effects of radio airplay and holiday buying on sales for our sample in the music industry. Recently, there has been increasing interest in the marketing of hedonic products, for example, box office sales for new movies (e.g., Krider and Weinberg 1998, Sawhney and Eliashberg 1996). However, there is a wide array of hedonic products besides movies that are still understudied. Some of these products, such as the live arts, music, and books, share many characteristics with movies. But there are also distinguishing differences across these products that make it necessary to better differentiate the category of hedonic products and develop new modeling techniques to better suit them. Hedonic products have been defined as products whose consumption is primarily characterized by an affective experience (Dhar and Wertenbroch 1997). These products are driven by the experience that the product provides rather than the utilitarian benefit offered by its bundle of attributes. As such, many hedonic products are not directly comparable nor substitutable. Utilitarian product features can be mimicked by other manufacturers while the more abstract attributes provided by a hedonic product cannot easily be replicated. Consequently, many hedonic product categories contain a diverse array of products with few direct substitutes. Many hedonic products are purchased to be part of a portfolio. For example, books are added to a library, music CDs are added to a collection, and movies contribute to a repertoire of experiences. We will call these types of items hedonic portfolio products. To assemble this portfolio of products, repeat purchasing in the product category is common. In fact, in many categories such as music and movies, the category purchase frequency is similar to many consumer packaged goods. However, repeat purchasing of an individual item in this product category is relatively rare. In this way, hedonic portfolio products are similar to many durables in that the useful life of a CD or book is very long and replacement is typically unnecessary. 1 However, there are still vast difference among hedonic portfolio products. Music CDs, books, and toys are very different from live concerts, movies, and theatre. Hedonic portfolio products can be either tangible or intangible. The purchase of a tangible hedonic portfolio product, such as a CD, gives the consumer something to take home and experience at her convenience, possibly repeatedly. The purchase of an intangible hedonic product, such as a movie, allows the consumer to experience the product only once and at the convenience of the distributor. But once a movie goes from theatrical release to being available for retail purchase as a video cassette, it becomes a tangible portfolio product and begins to share more of the characteristics associated with CDs. In this paper, we focus on modeling the sales of tangible hedonic portfolio products, specifically music CDs. Since each product is typically purchased only once, we will extend some well-established trial sales models to explain CD sales and the associated impact of various sales drivers. Since these products are evaluated based on the experience they provide rather than specific product attributes, product sampling is very important in influencing purchase. Product sampling in this industry occurs systematically through songs being played on the radio. Additionally, there may also be seasonal influences, especially around the time of the December holiday period. But a natural question concerning these factors (i.e., radio airplay and seasonal influences) is how do they influence sales? Does radio airplay increase awareness and therefore increases the potential market size or does it simply speed up sales that would otherwise have occurred at a later time? The same question can be asked of seasonal effects. This is of great interest to the music industry. The current industry belief is that getting the right album released for Christmas is key and will provide sales that are otherwise unattainable: 2 “Despite the planning that labels put into their holiday marketing campaigns, who gets the coveted Christmas number one single slot is a lot like winning the lottery and comes as a windfall to the company lucky enough to distribute it.” -- Music Week 1996 We will introduce covariates into our model in a way that allows us to differentiate whether each factor is increasing the rate of purchase of the product or expanding the potential market size. Lastly, faced with a high degree of heterogeneity in this category, we offer a method to structure this market as a combination of both consumer segments and product clusters. Since CDs are hedonic products with abstract attributes, traditional product clustering techniques that are based on differences across attributes are less useful. We offer a model that allows the clustering of products based on factors such as market size, rate of purchase, response to radio airplay, and seasonal effects. Data Our model will use store-level sales data obtained from SoundScan, Inc., a market research firm that plays a very similar role in the music industry to that of Information Resources and A.C. Nielsen for consumer packaged goods. SoundScan's computer system records and tabulates over-the-counter music sales soon after they are rung up at the cash register from over 14,000 retail stores in the United States. This sample represents more than 85% of all over-the- counter record sales in the US. The information recorded includes the name of the album sold, the number of units sold, the week of the sale, and the market in which the sale was made. Our data set contains weekly sales volume for a sample of 20 different music albums provided by Capitol Records. These albums are drawn primarily from the “adult contemporary” and “alternative music” genres. The set of albums used for this analysis represent a convenience sample chosen by managers at Capitol Records in 1996 and, therefore, might not be fully 3 representative of the complete portfolio of Capitol albums available at that (or any other) time. For this reason we do not wish to use our model’s parameter estimates to draw any firm, substantive conclusions about the music industry in general, but rather we view our dataset as a detailed case study of how such a model might be applied in other managerial contexts. While this is an excellent and highly reliable data source, it suffers from two possible drawbacks (similar to IRI and Nielsen). One weakness involves the fact that SoundScan primarily contracts with major retail chains, so data from many small record stores are not collected, and this is often where newly introduced artists first begin to sell. Furthermore, SoundScan only records over-the-counter music sales. Sales through other distribution channels, such as record clubs and the internet, are not captured within their system. Two categories of explanatory variables are included in our analysis and dataset, radio airplay and seasonal (holiday) indicators. Although radio airplay is not a controllable factor like most marketing mix variables, record companies do promote to radio stations in hopes of obtaining extra exposure. This added radio support is widely believed to enhance sales and has been empirically related to observed sales patterns via formal time series analyses (Montgomery and Moe 1999). Radio airplay data comes from Broadcast Data System (BDS), a firm that identifies and tracks the airplay of songs by electronically monitoring over 560 radio station signals across the United States. Their computer system can recognize virtually every different song by matching a unique pattern present in each song’s digitized broadcast signal. BDS records the name of the song played, the exact time and date of the broadcast, the radio station that played the song, and the estimated size of the listening audience in GRPs. Therefore, each week of observation for a 4 given album contains not only national sales volume for that week but also national airplay exposure in terms of GRPs. Figure A presents the data for a fairly typical album in our dataset, Soup, by Blind Melon. The graph shows the weekly sales for the album and illustrates the role of the covariates. Sales tend to move with airplay levels, suggesting some relationship between the two.