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Strategic Competition in the Multichannel Video Programming Market: An Intra-Industry Strategic Group Analysis



Submitted to the Media Management and Sales Division, AEJMC


March 2000





By



Sylvia M. Chan-Olmsted, Ph.D.
Department of Telecommunication
University of Florida
Gainesville, FL 32611
Phone: 352-392-0954 (office)
352-379-3908 (home)
Fax: 352-846-2899
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And

Jack C. C. Li
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Mass Communication Ph.D. Program
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Strategic Competition in the Multichannel Video Programming Market: An Intra-Industry Strategic Group Study of Video Programming Networks


The number of subscribers to both cable TV and non-cable MVPD (Multichannel Video Programming Distributor) services has grown to reach over 80 million U.S. households in 1999 (FCC, 1999). As the landscape of the video programming industry continues to change rapidly with the development of alternative MVPD services and the arrival of digital television, the strategic importance of Òcontent,Ó the ÒproductÓ for distribution, is amplified. Some MVPDs from the telephone sector, BellSouth and Ameritech, have filed complaints with the FCC, claiming that they have difficulties in gaining programming access to non-vertically integrated cable networks and that the incumbent cable operators receive steep discounts for popular programming networks, thus putting them at a competitive disadvantage (FCC, 1999). Such bargaining power of suppliers over market participants is one of the main forces that determine the intensity of competition and profitability in an industry (Porter, 1985). Wolzien (2000) also argued that in a balancing game of distribution and content, the growth of more distribution capacity gives weight to the power of content. As the role of the multichannel video programmers[1] such as CNN and ESPN becomes more significant in a growing number of markets with competing MVPD services, an assessment of the strategic patterns employed by these video programmers is critical to our understanding of the emerging MVPD industry. 
The multichannel video programmers may be characterized by their provision of a heterogeneous media product and the use of diverse business strategies. These media firms present an economic mechanism different from that of the traditional broadcasting industry. When conducting analyses of such media firms, analysts should consider thoroughly the firms' heterogeneous market characteristics, which often determine the parameter of competition and consequently influence media firms' market conduct as well as performance. 
Economic discussions of the broadcast television industry have in the past assumed that all programming distributors in the market use the same funding mechanism and deliver products to a fairly homogeneous group of buyers.  Such a presumption does not apply to the programmers in the MVPD market.  While CNN charges its MVPDs a fairly high license fee to carry its signals, many new start-up multichannel video programmers actually provide incentives to cable systems to induce carriages. While USA Network carries commercials, Independent Film Channel does not offer any local or national avails. Furthermore, multichannel video programmers often offer differentiated programming products, and the buyers may deliver these products via different distribution platforms. In essence, the traditional emphasis on "industry" as a unit of analysis is no longer appropriate for analysis of these heterogeneous video programmers. 
This paper attempts to apply a multichannel strategic group competition theory to assess the strategic patterns of the multichannel video programmers and the relationship between group membership and performance. A strategic groups analysis is the exercise of identifying the key distinguishing elements of a strategy in an industry and then examining groups of firms with similar strategies to provide concise and useful insights into the competitive environment and competitive behavior (Olusoga, Mokwa, & Noble, 1995). Accordingly, section I of this paper reviews the proposed multichannel media strategic group competition model; section II examines the state of competition in the multichannel video programming market; section III describes the research design, including research questions, method, and the key strategic grouping variables; section IV presents the results of the statistical grouping, the cluster profiling, and the relationship between group membership and performance; and finally, section V provides a conclusion and the discussion of implications. 

The Multichannel Strategic Group Competition Model 
     
Because of the capacity of "multiple" communication channels, firms in the MVPD industry operate under a more complex business system than the traditional broadcast industry and thus are capable of more diverse strategic competition. The observation that strategic diversity within an industry has a significant bearing on market behavior is central to the theory of strategic groups and grounds this study.  In essence, the MVPD industry is inherently heterogeneous as the MVPD programmers may be supported by advertisers and/or subscribers, have the capacity to offer general appeal as well as specialized programs, and may utilize different MVPD delivery technologies.  
Porter defined a strategic group as a cluster of firms that follow similar strategies in terms of the key decision variables (Cool, 1985; Porter, 1985).  Firms within a strategic group resemble one another closely, recognize their mutual dependence, and thus coordinate their behavior effectively. Furthermore, when one considers inter-group market dynamics, the existence of different strategic groups affects the overall level of rivalry in the industry.  When firms are associated with different strategic groups, they have different preferences about pricing, R&D, advertising, optimal output, and other market conduct.  As a consequence, operational differences complicate the process of cooperation (either explicit or implicit) between groups (McGee, 1985).  Hence, it is more likely for groups with similar strategic approaches to cooperate than it is for groups that use diverse strategies.  Moreover, cooperation is easier and more likely to happen within groups than between groups.  Also, environmental changes do not have equal impact on different strategic groups due to their different strategic postures, assets, and skills. 
As for the performance differences among strategic group members, many strategic group scholars have argued that there are group specific entry barriers that provide protection to group members (Mascarenhas & Aaker, 1989; Olusoga, Mokwa, & Noble, 1995). Such structural forces impede firms from freely changing their competitive positions and explain intra-industry profit differentials in a cross-section of industries (Caves & Ghemawat, 1992).  Incorporating the strategic groups concept, Chan-Olmsted (1997) proposed that the multichannel video programming market would, by its heterogeneous nature, exhibit monopolistically competitive market behaviors at the industry level. In other words, these firms would attempt to build differential advantages (e.g., programming differentiation) and "selectively" interact with certain competitors' strategic actions. To rationalize such selective competitive behavior and at the same time provide a more systematic framework for studying firm strategy, Chan-Olmsted (1997) suggested moving away from simply using "industry" as a unit of analysis in the study of MVPD programmersÕ market strategy. Instead, these firms should be examined, in addition to an industry level overview, in a group setting with expectations of oligopolistic market behaviors, namely, recognition of mutual dependency (i.e., taking each other's changes into consideration and realizing that any change in conduct causes strategic responses from their competitors).  The proposed model also anticipated that the relative size of the MVPD firms within a group influences the degree of mutual dependence among the firms. The more equal the relative size between the programmers, the more attention is paid to the interdependence.  If a multichannel strategic group is comprised of a few programmers that are unequal in size, the smaller firms' strategic actions have less of an impact and thus are taken less seriously by larger firms.
As for inter-group competition, the multichannel video programming industry is hypothesized to comprise various strategic groups, thus the overall competition pattern of the industry would be characterized by the nature and interaction of these strategic groups.  There are factors expected to influence the competition intensity and pattern between different strategic groups in the market.  The proposed theory suggested that the strategic distance between groups and the number and size distribution of groups would likely affect the intensity of competition in the industry. For example, if MVPD programmers in various groups choose to engage in diverse strategic activities such as very different programming approaches, overall industry competitiveness should become less intensive since these groups are "geographically" separate in the marketplace.  By selecting different niches, these strategic groups avoid any unnecessary competitive behavior and prosper together by sharing the environment.  On the other hand, if the groups are close to each other with no definite and clear boundaries (e.g., similar programming), encroachment occurs, and in order to survive, fierce competition may result as groups defend their strategic territories. On the other hand, if the size of strategic groups is distributed unevenly, the competition may be more active because large size strategic group configurations tend to be unstable as less successful MVPD programmers in the group try to become more competitive by altering their market strategies to obtain differential advantages.  This may, in turn, gradually lead them into a different strategic group.  In addition, as the number of strategic groups increases, the probability of strategic territory encroachment also increases.  Thus, the inter-group competition may be intensified.
As discussed earlier, the economic notion of mobility barriers explains the structural and behavioral forces that keep potential entrants from easily entering a certain strategic group and eroding the group profits. Thus, Chan-Olmsted (1997) argued that the multichannel programmers that choose successful approaches in the key strategic dimensions and can execute such strategies in an operational sense are not easily imitated by other program distributors because of the insulation created by their mobility barriers.  Strategic groups that possess high mobility barriers are expected not only to be relatively more insulated from competition but also to have superior bargaining power with the exhibitor as well as the producer--and thus enjoy even higher financial returns.

The Multichannel Video Programming Market

As one of the explanations for the formation of strategic groups provided by Porter (1985), the historical development of an industry 's nature of demand and product characteristics bestows differential advantages/disadvantages on firms. Since detailed knowledge and understanding of the industry context is a necessary condition for the specification of dominant strategic variables, an overview of the MVPD programming market is provided next.  
Cable TV continues to be the dominating distribution system for multichannel video programming, serving 82 percent of MVPD subscribers in 1999 with its increasing delivery capacity (FCC, 2000). Approximately 85 percent of cable systems nationwide are now offering more than 30 channels; 22.4 percent have a capacity exceeding 54 channels (Cable Advertising Bureau, 2000). Cable also continues to attract audiences from broadcast networks. Advertising-supported cable networks enjoyed a share of 34.1 percent of all TV households during primetime in 1999, an increase of 59 percent from 1994, while all commercial broadcast TV accounted for a 55.6 percent share, a decrease of 22 percent for the same period (Cable Advertising Bureau, 2000). 
Thus far, the most significant alternative MVPD to cable TV is DBS (Direct Broadcast Satellite) services, which represent 12.5 percent of all MVPD subscribers, an increase of 39 percent since 1998. Though not as dominant as its cable counterpart, DBS was reported to have higher levels of consumer satisfaction and lower average monthly programming prices over cable (FCC, 1999). With the passage of the "Satellite Home Viewer Act" in 1999, which granted DBS providers retransmission rights of network and network affiliate signals into local markets, DBS has become a more viable MVPD competitor. Nevertheless, DBS service providers continue to comment that access to vertically integrated cable programming networks is still a barrier against their remaining a serious MVPD competitor.
  To ensure their critical role in this media market, the multichannel video programmers continue to invest heavily in the development of their products, pouring over $5.5 billion in programming in 1999, a 13 percent increase from 1998 and nearly a 240 percent increase since the start of the decade (Cable Advertising Bureau, 2000).  New niche multichannel video networks from Baby TV to Wedding Channel emerge constantly. In fact, as the multichannel space becomes more populated, most new programmers shy away from the broad-appeal programming lineups. Instead, product differentiation and audience segmentation become the strategic norm of the market. The number of satellite-delivered programming networks has increased from 245 in 1998 to 278 in 1999[2] (FCC, 1999). Nevertheless, the numbers of subscribers reached by these programmers vary tremendously. Whereas the market veteran, Discovery Channel, is delivered to over 77 millions subscribers, a relatively new startup, Oasis TV, is available to less than 2 millions households. While audiences are able to enjoy a greater number of programming choices, the proliferation of programming supplies signals a more competitive marketplace for the multichannel video programmers.  At the same time, the increasing availability of non-cable MVPD systems elevated the importance of strategic planning in the marketing of these programming products.
       Approximately 18 percent of the MVPD programmers are so-called premium channels, which generate their revenues from subscription fees without advertiser support. The revenue growth for these premium channels has been modest, increasing only 2 percent to reach $5.2 billion. On the other hand, the MVPD programmers that traditionally rely on carriage fees as well as advertising revenues (e.g., basic cable networks) managed to grow 6 percent, attaining over $23 billion in subscriber revenues in 1999. In addition, the advertiser-supported programmers have seen a 14 percent increase in advertising revenues from 1998 to 1999, reaching $7.5 billion in national ad revenues. The increasing reliance on advertising revenues for these programmers amplifies the importance of effective programming strategies in attracting audiences and the gate-keeping role of the delivery systems in controlling the programmersÕ access to audiences.
        Amidst all the recent waves of mergers and acquisitions in the media industries, the level of concentration in the national market for the buyers of multichannel video programming has actually decreased according to a FCC report (FCC, 1999). The overall Herhindahl-Hirschman Index for cable system operators declined from 1013 in 1996 to 923 in 1999. The combined market shares of the top four MSOs remained relatively stable at about 53-54 percent, while the top ten figures increased slightly from 71 percent to 75 percent during the same period. In essence, the buyerÕs market power, as one of the factors that affect competition in an industry (Porter, 1985), has remained steady in the multichannel video programming market.
                  	
Research Design

        The following research questions guide this study:
1. What different strategic groups are there in the multichannel video programming market?
2.      What is the relationship between strategic group membership and performance in the multichannel video programming market? 
3.      What strategic positions deliver superior market performance in this market?

To address these questions, the authors first identify the key strategic variables to be used for strategic group formation. Houthoofd and Heene (1997) have suggested that the inclusion of industry-specific strategic variables is essential in the application of strategic groups. Though the adoption of prior research variables from different industries may sometimes be appropriate, the identification of strategic variables has to be rooted in literatures concerning the current MVPD industry, namely the cable television industry, as over 80 percent of all subscribers to MVPD services received their programming from a local franchised cable operator (Paul Kagan Associates, 2000).  Most prior research on the cable industry has focused on the economic and regulatory issues involving cable system operators (Chan-Olmsted, 1996; Crandall & Furchtgott-Roth, 1996; Hazlett & Spitzer, 1997; Johnson, 1994). While some studies have addressed explicitly competition in overbuild markets (Barrett, 1995; Tseng, 1999), some have examined cable audience viewing behaviors (e.g., Heeter & Greenberg, 1988). To the extent that programming suppliers are addressed, research focus has been on the video programmersÕ vertical relationship with cable systems (Chipty & Snyder, 1999; Waterman & Weiss, 1997). 
 Most strategic groups studies have subscribed to the notion that strategic group formation should be based on scope and resource commitments variables,[3] the strategic dimensions that are key to gaining and maintaining competitive advantage in target product-market segments (Olusoga, Mokwa, & Noble, 1995; Fiegenbaum & Thomas, 1995). The authors proposed that the core scope and resource variables most likely to create competitive advantages in the MVPD programming market are: (1) size, (2) vertical integration, (3) horizontal integration, (4) history, (5) operating efficiency, (6) product differentiation, (7) programming development, (8) pricing, (9) advertising product availability, and (10) degree of reliance on multiple revenue streams. A complete list of the strategic/performance variables and their operational definitions are in Table 1.

Size of the Multichannel Video Programmer
As noted by Cool and Schendel (1987), the size of the firm influences the ability to allocate different amounts of resources to different functional areas. Firm size is one of the most frequently used variables for identifying strategic groups in various industries (Figenbaum & Thomas, 1995; Cool & Dierickx, 1993; Olusoga, Mokwa, & Noble, 1995; Veliyath & Ferris, 1997). A resource-based variable, the potential market size is  particularly critical in the media market because media products have the nature of public goods. That is, the product has extremely low marginal costs once the first copy is obtained. Therefore, any subsequent new viewer added to the existing customer base will increase profits. The size of a MVPD programmer, as measured by the number of its subscribers, has significant implications on its subscriber revenues, advertising revenue potential (if any), power to negotiate with system operators, and ability to invest in original programming as well as new technology. 

Vertical Integration with System Operators
        MVPD system operators and program distributors have a symbiotic relationship as the delivery systems need an ample supply of programming to attract and retain subscribers and the video programmers need access to subscribers in order to compete. The economic necessities have led cable operators and networks to integrate vertically (Owen & Wildman, 1992). In an earlier study, Klein (1989) found that vertically integrated MSOs were more likely to carry a cable network in which they had an ownership interest. Nevertheless, these MSOs were also more likely to carry networks in which they had no financial interest than non-vertically integrated MSOs. In another empirical study, Waterman and Weiss (1997) suggested that cable networks can benefit from their vertically integrated relationship with cable systems, especially major MSOs such as Time Warner and Cox. It was concluded that although the relationship between a cable system and a cable network can be based on simple contractual agreement, an ownership relation will likely facilitate the contracting process. Furthermore, a cable operator with financial interest in a programming supplier has higher incentives to provide promotional materials for that cable network, such as assigning the network a more favorable channel position and providing additional program promotions. Competing video programmers have voiced concerns that an MSO-programmer consolidation sometimes may work anti-competitively to the disadvantage of unaffiliated programmers. That is, the cable operator can foreclose the access of competing programming networks to the cable subscribers it controls. Nevertheless, Crandell (1990) found no statistically significant difference between vertically integrated MSOs and non-vertically integrated MSOs in their network carriage decisions. As most studies have revealed a certain degree of competitive advantages derived from the integration with MVPD buyers in the market, the vertical integration strategic dimension is included in the grouping process. As a resource-based variable, the degree of vertical integration is operationalized by the percentage of subscriber reach of the MSOs that are under the same corporate ownership as the video programmer. To accurately reflect the partial ownership status of certain MSOs, each MSOÕs reach is adjusted according to its ownership percentage.

Horizontal Integration with Other Video Programmers
The ownership of multiple MVPD programming services is becoming more common as many established multichannel programmers branch into sub-niche network territory. For example, BET started a BET on Jazz network, while Discovery Channel introduced sister networks such as Discovery People and Animal Planet. The benefits of being affiliated with various MVPD programmers are evident. In addition to cost-cutting possibilities from the sharing of resources and talent, the affiliated networks might be able to offer advertisers innovative, targeted advertising packages and to preempt competition by saturating a programming niche. Common ownership can also enhance the bargaining power of the networks against advertisers as well as system operators. Also a resource-based variable, the degree of horizontal integration is measured by the number of other TV programming networks, including cable and broadcast TV networks, that are owned by the same parent company/companies of the video programmer under examination.

History and Operating Efficiency
  Multichannel programmers such as CNN, Nickeloden, and MTV that have a long history of operation are often regarded as the foundation networks on cable systems (Eastman, 1997). Such a first-comer status may contribute to higher license fees and advertising revenues for the more established networks, creating a competitive advantage over new startups. The history variable is operationalized by the total number of months in operation. Veliyath & Ferris, in their study of mobility barriers (1997), found "efficiency" to be an important strategic factor that often affects a strategic group's financial performance. To take into account a video programmerÕs ability to efficiently apply its resources in non-programming areas, the authors decided to include in the group formation procedure an operating efficiency variable, measured by the percentage of selling, general, and administrative expenses (total overhead costs excluding expenses that can be directly attributed to programming production and acquisition) over total net revenue of a MVPD programmer. 

Product Differentiation
A scope-oriented variable, product differentiation is a central strategy in the multichannel video programming market. As MVPD continues to develop as a narrowcasting medium, offering specialized niche content otherwise not available via traditional broadcast media, the ability to differentiate its product is critical to the success of a MVPD programmer. Bae (1999) suggested that, though cable networks do not directly compete with each other, program differentiation exists among the competing cable all-news networks as each cable news network offered a distinctive program format and content approach. Many strategic groups scholars such as Veliyath & Ferris (1997) have used differentiation as one of the grouping strategies. To measure product differentiation, the authors use both a programmerÕs content type (e.g., children, news, sports, etc.) and the degree of target appeal (e.g. mass, niche, sub-niche).  While a ÒmassÓ category indicates a more general programming appeal targeted at a wide variety of audience, the niche category signals a somewhat narrowcasting programming approach targeted at a subset of audience. Sub-niche networks are those programming services that focus on providing the most defined type of programming that appeals to an even smaller group of audiences. For example, USA Network will be in the ÒmassÓ category, with ESPN in the ÒnicheÓ and The Golf Channel in the Òsub-nicheÓ categories. 

Programming Development
An important development of the MVPD industry is that the programming market is no longer a key "aftermarket" of movies and off-broadcast network programs; it has become a "foremarket," in which some cable networks produce their own programs and even sell the programs back to broadcast TV (Brooks, 1997). As a result, the ability to develop attractive original programming has become a critical strategy. For example, Discovery Channel, one of the most popular cable channels, is a unit of Discovery Communications, Inc., which is a major producer of original, non-fiction entertainment programming. Programming product development, a scope-based variable, is measured by the percentage of each programming serviceÕs total programming expenses over its total net revenue.

Pricing
Another scope strategic variable, pricing, is also used to identify strategic groups in many studies (Olusoga, Mokwa, & Noble, 1995; Galbraith, Merrill, & Morgan, 1994). In the MVPD programming market, pricing strategies may be applied in determining license fees charged to MVPD systems and/or advertising rates to advertisers. For example, to acquire a size competitive advantage, a programmer may opt for a lower license fee to induce/increase cable carriages. On the other hand, higher license fees directly contribute to a MVPD programmer's revenue potential. In this study, the authors use the carriage license fees charged to cable operators per subscriber per month to measure the pricing strategies of these programmers. Unfortunately, because the CPM (cost per thousand) pricing data were incomplete for many newer programmers in the sample, the ad pricing measure is not used in the grouping exercise. 

Adverting Product Availability and Reliance on Multiple Revenue Streams
    While the offering of local advertising avails provides incentives for MVPD system carriages (Eastman, 1997), national avails influence a programmerÕs advertising revenue potential. To take into account the advertising activities that often lead to strategic advantages for certain programmers, the authors include in the grouping process another scope variable, advertising availability. It is operationalized by the numbers of both local and national commercial spots available for local system operators and national advertisers per hour. Finally, to assess a MVPD programmerÕs reliance on multiple revenue streams and to compare the programmer's reliance on advertising versus licensing revenues, the authors examine the video programmers' ad revenue as a percentage of total net revenue and licensing revenue as a percentage of total net revenue.

Performance Measurement
  Strategic groups scholars have used return on sales (ROS), return on assets (ROA), return on equity (ROE), market shares, and pretax profits to measure firm performance (Cool & Dierickx, 1993: Houthoord & Heene, 1997; Olusoga, Mokwa, & Noble, 1995; Veliyath & Ferris, 1997). Nevertheless, performance measurement is a perennially thorny issue in strategic management (McGee, Thomas, & Pruett, 1995). Many have criticized the narrow terms of profitability as opposed to a broader performance view including both financial and operational measures. Cases and Ghemawat (1992) have argued that in exploring profit differences between groups, pretax income rather than rates of return, as chosen by many strategic management scholars, should be used because according to economic theory, firms try to maximize total economic profits rather than rates of return. McGee, Thomas, and Pruett (1995) again stressed the importance of using multiple measures of performance. Thus, to evaluate the performance of MVPD programmers, the authors propose to use a pretax income measure of Òtotal net revenueÓ (total gross revenue excluding ad commissions) in addition to rate of return measures such as Ònet revenue per subscriberÓ and Òcash flow margin.Ó Revenue per subscriber provides a more accurate performance measure by taking into account the inherent size limit of younger, niche programmers. Cash flow margin is particularly relevant to the cable TV industry as cash flow is used as a primary measure in valuating most cable properties. The measure of cash flow margin here is derived from a programmer's EBITDA (earnings before interest; taxes; depreciation of property, plant, and equipment; and amortization of intangibles) over total net revenue.
	
Method

        Most strategic group studies (Cool & Schendel, 1987; Mascarenhas & Aaker, 1989; Fiegenbaum & Thomas, 1995) have used cluster analysis to statistically group firms. Clusters of firms are developed based on their ÒscoresÓ on a set of important strategic variables representing key competitive resources typically chosen by the researcher on the basis of industry studies, expert opinion and judgment (Fiegenbaum, Sudharshan, & Thomas, 1990). Dillon and Mulani (1989) have pointed out that the major weakness of cluster analysis is that there is no natural or objective grouping in the data. Given any data matrix, it is not difficult for numerical taxonomic methods to derive statistically significant groupings (McGee, Thomas, & Pruett, 1995). Punj and Stewart (1983) also argued that clusters are Òfuzzy constructsÓ with no clear guidelines for determining the boundaries of each segment.  To cope with the ÒartificialÓ nature of cluster analysis, as suggested by Ketchen and Shook (1996), the authors have carefully selected the grouping variables and will interpret the emerged clusters based on prior research and industry observations.
Since a la carte pay programming services (i.e., pay cable channels such as HBO and Pay Per View) compete under a different financing and operating system and with a different set of programmers, the subjects of this study are limited to the multichannel video programmers (networks) that are often offered to subscribers as part of a basic or extended basic programming tier. The data set was derived primarily from the Economics of Basic Cable Networks 2000 databook compiled by Paul Kagan Associates, a leading media consulting firm. Initially, data were collected for all 59 basic cable networks included in the databook.[4] A total of twelve metric measures were used to operationalize the resources and core scope variables discussed earlier (see Table 1). The 59 cable networks were also coded according to their dominant programming types.[5] The categorical variable was not included in the initial analysis because of clusterÕs limitation in combining metric and non-metric data in the grouping procedure. The programming types variable was later used for cluster profiling.[6] To examine the relationship between group membership and financial performance, ANOVA (Analysis of Variance) was performed on the membership variable and the three performance measures discussed earlier. 
In order to obtain the largest and most compatible sample size, the year 1998 was chosen for a cross-sectional study.[7] A total of 45 network programmers were included in the final analysis as the grouping procedure necessarily excludes cases with missing data in any of the measures. Also, because the variables were measured in different units, which might give more weights to variables with large values, the data were standardized prior to the statistical analysis.[8] A two-stage cluster analysis procedure was used. First, the hierarchical cluster analysis with squared Euclidean distance measure was used to identify the most appropriate number of clusters. Based on the rules that small agglomeration coefficients indicate a combination of fairly homogeneous clusters and the joining of two very different clusters would result in a large coefficient or a large change in the coefficient, the authors identified two possible cluster solutions, a three-cluster solution with 26, 4, and 15 firms in each of the clusters (coefficient increased from 23.079 to 37.377) and a seven-cluster solution with 1, 3, 12, 8, 13, 6, and 2 firms in each of the clusters (coefficient increased from 15.719 to 19.913).  After a careful examination of the cluster membership and the membersÕ values in each of the grouping variables, the authors concluded that the seven-cluster solution was most appropriate based on prior industry observations and created the most homogeneous clusters. A non-hierarchical procedure, K-means clustering, was performed next.[9]  To ensure that the segments are mutually discrete, a one-way ANOVA was conducted. 

Results

Strategic Groups in the MVPD Programming Market
Seven strategic groups were identified by the cluster analysis (see Table 2). The authors performed ANOVA for each independent variable to examine the variances of characteristics among groups. All measures, with the exception of programming expenses as a percentage of net revenue, were found to vary significantly from group to group (see Table 3).  Note that standard deviation for cluster one is not available because it consists of only one member (i.e., TV Guide). 

Strategic Group 1: Programming Guide 
Highly differentiated from all others, TV Guide is the only member in this strategic group. It seems that, as the leading provider of programming schedules/information across different media platforms, TV Guide is able to re-purpose its content for multiple distribution systems and invest the lowest percentage of its net revenue in programming among all cable networks. By nature of its content, TV Guide does not compete directly with other networks. In fact, it complements other programmers. Other strategic positions of TV Guide that may generate competitive advantages include the largest number of local avails for local cable systems, lowest license fee per subscriber, and vertical integration with the largest MSO, AT&T (56 percent ownership). TV Guide also has the highest number of sister networks. Being the only member in a strategic group, TV Guide has no within-group competition. Furthermore, it holds the most distant position from all other groups based on the strategic measures (see Table 4). Nevertheless, these strategic advantages did not translate to financial success. Earning $58 million in net revenue, 65 cents net revenue per subscriber, and 29.3 percent cash flow margin, TV Guide exhibited the worst financial performance among all groups.

Strategic Group 2: Commercial-Free Movie Programmers
This is the most content homogeneous strategic group. All three members here offer movie products commercial-free and rely on license fees as the primary revenue source. An examination of the ownership shows some interesting patterns. Both American Movie Classics and Independent Film Channels are co-owned by a major MSO, Cablevision (75%) and NBC (25%) respectively. Accordingly, the two movie channels are strategically differentiated as AMC offers classic, general-appeal Hollywood movies and Independent Film provides newer, narrow-appeal films. Competing against AMC, Turner Classic Movies is also vertically integrated with one of the leading MSOs, Time Warner. While AMC has the first-comer advantage in this group with twice as many subscribers, TCM has access to Time WarnerÕs studio products and film libraries. Though all three networks offer movie products, the oligopolistic interdependent relationship seems to exist in this cluster as TCM and AMC priced their products (AMC: 19 cents; TCM: 15 cents) comparably and spent similar proportions of their revenues in programming development (AMC: 29.1 percent; TCM: 30.9 percent), while the Independent Film Channel is content differentiated and thus is not in direct competition with the other two. Firms in this group generated only modest net revenue ($73 million) and net revenue per subscriber ($1.53), while earning the lowest cash flow margin (29.13 percent).
Strategic Group 3: Young, Differentiated, and Integrated Programmers
This group consists of twelve cable networks with a diverse programming content mix. Eleven of the 12 networks are affiliated with MSOs and all members have many affiliated programming networks (M = 13.58, SD = 4.40) (see Table 2). This group also consists predominantly of younger networks ((M = 66.08, SD = 34.53). It seems that most of the networks emerged in the early 1990s as the second wave of niche programming after the cable industry experienced the success of the original niche networks such as ESPN and MTV. To establish a differentiated position from the existing niche networks, members of this group try to provide an even more defined programming content, spending over 80 percent of their net revenues on programming. Perhaps due to its newness, this group had the second lowest average license fee and a relatively small subscriber base (36 million), while operating in modest efficiency (49.43 percent). Firms in the group generated very modest net revenues ($67 million net revenue and $1.72 net revenue per subscriber) but comparably higher rate of return in cash flow margins (49.65 percent). 

Strategic Group 4: Established, General, and Integrated Programmers
This is the most established strategic group with eight cable networks that were early entrants to the market (many have been in operation for more than 20 years). Seven of the eight networks here are owned by top MSOs such as AT&T, Time Warner, and Cox. This group has the largest number of subscribers when TV Guide is excluded (M = 80.91, SD = 27.24), it is very efficient (M = 22.34, SD = 6.12), and it demands the second highest license fee per subscriber (M = .22, SD = .15). An examination of the program content and audience appeal also reveals some patterns. Half of the group members (i.e., Fox Family, TBS, TNT, and USA Network) offer general-appeal programming. While CNN and Headline News provide news and information, its content is at the general end of the spectrum in this content category. The same holds true for networks like BET and Discovery. To a certain extent, this group may be characterized as the cable-castersÕ broadcast tier. This is the second best performing group in terms of net revenue ($500 millions) and net revenue per subscriber ($6.21). It has modest cash flow margin of 38.18 percent.

Strategic Group 5: BroadcastersÕ Differentiated Cable Programmers
Opposite of group four, group five may be characterized as broadcastersÕ strategic cable tier. This group has the largest number of networks (13). Except for the Weather Channel and HGTV, all other 11 networks are owned by either CBS/Viacom, NBC, or ABC/Disney. Interestingly, none of these networks are affiliated with any MSOs. This group contains the largest cluster of music programmers (5). Similar to their broadcast parent companies, firms in the group rely mostly on their advertising revenues (M = 62.74, SD = 9.25). They also did not command very high license fees (M = .11, SD = .6) and received only about 30 percent of their net revenue from advertising sources.[10] This strategic group seems to deliver narrowcasting, niche programming opportunities for broadcasters. The financial performance of the group was modest with $284 million total net revenue, $4.02 net revenue per subscriber, and 36.38 percent in cash flow margin. 

Strategic Group 6: Small But Trying Programmers
This group consists of six networks with very diverse history, content, size, operating efficiency, pricing, and degree of integration. In general, these video programmers have the smallest number of subscribers (29.2 million), the least affiliations with MSOs (with the exception of Bravo), the lowest level of horizontal affiliations (3.67), and understandably the least efficient networks considering their small subscriber reach. However, they invested in product development heavily, spending over 80 percent of their net revenues on programming. Relying mostly on license fee revenues, firms in this group produced less than $2 net revenue per subscriber and delivered the second lowest total net revenue ($66 million). Nevertheless, these programmers were able to generate the highest rate of return in this market, delivering a cash flow margin of 89.55 percent. 

Strategic Group 7: Established and Content-Valued Programmers
This group is composed of two networks, both of which are owned by ABC/Disney. The two networks do not have the largest subscriber base and are not affiliated with any MSOs; however, because of their well-established credibility (at least 15 years in operation) and highly-valued content in two profitable programming segments (children and sports), they were able to charge the highest license fees among all groups (67 cents) and rely heavily on their license fee revenues (70.45 percent). Armed with the most efficient operation (20.60 percent), firms in this group have exhibited the best overall financial performance among all groups, generating $ 832 million average net revenue, over $13 per subscriber net revenue, and a cash flow margin of 39.15 percent. 
	
Programming Types and Strategic Distance Between Groups
To take the categorical variable, programming types, into consideration, a cross-tab analysis was performed. The resulting Chi-Square test indicated that the seven strategic groups have applied significantly different programming strategies (p <.00). The cross-tab analysis also revealed that group three has the most diverse, evenly distributed programming niches, followed by groups five and six. Only groups four and five have four or more programmers competing in the same programming categories. Even so, these programmers do not really encroach on each otherÕs product territory as they often differentiate further their programming emphasis (e.g., music networks MTV versus TNN).
To examine the strategic position of each group, the Euclidean distance between each pair of clusters was measured. The values indicate the degree of similarity/dissimilarity between the clusters (see Table 4). With all variables taken into consideration, group one, the programming guide, occupies a position most dissimilar to other groups, with distances ranging from 5.58 to 9.32. The commercial-free movie group (No. 2) and the established, content-valued group (No. 7) also have a considerable degree of dissimilarity relative to other groups, with distances ranging from 4.38 to 5.77 and 4.89 to 8.94. Alternatively, groups three through six are closer to one another, all with distance values no higher than 3.66.  Overall, the pairs of group three (young, differentiated, and integrated group) and group five (broadcasterÕs differentiated group), group three (young differentiated, and integrated group) and group six (small but trying group), and group five (broadcastersÕ differentiated group) and group six (small buy trying group) occupy the closest strategic space.
Relationship between Strategic Group Membership and Performance in the Multichannel Video Programming Market

ANOVA was performed to examine the correlation between group membership and performance. The statistical results, Fs (6, 38) = 8.20 and 9.90 (p( .05), indicate that group membership in this market is significantly related to the total net revenue and net revenue per subscriber a video programmer generates. The relationship, however, is not significant between group membership and cash flow margin, F (6, 38) = .79 (p ( .05).
With regard to total net revenue, group seven has the best performance (M = 832.05, SD = 575.66), whereas group one (TV Guide) has the worst (M = 58.30, SD = n.a.). Because net revenue per subscriber is highly correlated with total net revenue, it is not surprising that group seven also delivered the highest per sub value (M = 13.50, SD = 4.06) and that group one has the lowest (M = .65, SD = n.a.).
Although the variable of cash flow margin has not shown significant variance among groups, group six has marginally the highest mean value (M = 89.55, SD = 131.46). By contrast, group one (M = 29.30, SD = n.a.) and group two (M =29.13, SD = 11.27) have the lowest cash flow margins. 


Strategic Positions that Lead to Superior Performance


It seems that a niche content that is highly valued (as suggested by per subscriber license fees) by its subscribers as well as buyers (i.e., cable system operators) is the key to better financial performance for a multichannel video programmer in regards to revenues, both overall and in rate of return. It is also evident that while higher subscriber numbers do not guarantee financial success, a long established history elevates a MVPD programmerÕs performance. More ad avails and reliance on ad revenue do not yield better margin or revenue numbers, neither does a heavy reliance on license fee revenues. Horizontal relationships with other programmers and efficiency seem to somewhat contribute to the performance measures. Nevertheless, contrary to many MVPD system operatorsÕ belief, vertical integration, while possibly enhancing carriage probability and/or other marketing practices, is not essential for superior performance in this market. Spending more money in programming also does not lead to better overall financial outcomes.

Discussion and Conclusions

It is evident that the MVPD programmers, namely the cable networks, in pursuit of monopolistic space, are highly differentiated not only in their programming product but also in many of the strategic dimensions discussed. By occupying different strategic positions, most strategic groups in this market were able to carve out more definite and clear boundaries to avoid territory encroachment (i.e., direct competition).  
A careful review of the group composition reveals some interesting strategic patterns. While the presence of broadcasters may be felt through their niche cable properties in group five, cable-based MVPDs have tried to compete with the broadcasters with their own mass-appeal networks in group four. Possibly due to its longer history in operation and the higher perceived value (license fees), group four has outperformed group five, generating almost twice as much net revenues.
Though delivering the second best overall performance in terms of within-group competition, group four seems to present the highest likelihood of content encroachment between its members. The more general broadcast content model, while generating higher license fees and ad revenues, also translates to continuous pressure from other comparable programmers to deliver more high-profile and/or original programming product. In regards to between-group competition, group three (young, differentiated, & integrated) and five (broadcasters' differentiated brand) seem to be in most direct competition in both overall strategic distance and content appeals. As group three becomes more established and the proportionately high programming investment takes effect, it may be able to demand higher license fees and/or deliver better ad revenues, thus becoming a more competitive group in the market. 
There appears to be a relationship between strategic group membership and financial performance in the multichannel video programming market. It's interesting to see that, contrary to general industry belief, neither size nor vertical integration has played an important role in elevating a MVPD programmer's financial performance. The competitive advantage derived from such strategies apparently was not realized financially. While operating efficiency and affiliation with other programmers somewhat contribute to better performance, first-comer advantage and valued content are the key to financial success. Note that smaller programmers may still outperform bigger programmers in rate of return when they rely mainly on license fee revenues and invest in programming to improve the value of their products.
This study is limited by its inability to include more newer cable networks as well as CPM pricing and audience ratings to evaluate the value of a programming product from the subscribersÕ and advertisersÕ perspectives. Future research in this area may extend the strategic grouping to a multi-year analysis to examine the stability of strategic groups and their financial performance. Also, more studies are needed to investigate the environmental factors that actually influence in-group and between-group competition and to validate the proposed theoretical relationship between size and strategic distance with the degree of competition in this market.





























Table 1: Strategic and Performance Variables and the Operationalization of the Variables

Strategic/Performance Variable
Operationalization of the Variable
Size
1  Number (in million) of Subscribers
Vertical Integration
2     Number (in thousand) of Subscriber Reach of the Multiple System Operators that Are Owned by the Same Parent Company/Companies (Partial Ownership % is Adjusted Accordingly)
Horizontal Integration (i.e., Sister Networks)
3   Number of Other Cable or Broadcast TV Networks that Are Owned by the Same Parent Company/Companies
History
26880Number of Months in Operation from Launching to 1998/End
Operating Efficiency
26881Total SG&A (Selling, General & Administrative) Expenses[11] as a % of Total Net Revenue
Product Differentiation
4   Degree of Targeted Appeal: (mass = 1, niche = 2, sub-niche = 3)
5   Programming Content Type (general/superstation, movies, children, music, sports, news/information, religion, ethnic, educational, specific topic mix, lifestyle/leisure)
Product Development
6    Total Programming Expenses as a % of Total Net Revenue
Pricing
7        License Fee ($) Per Subscriber Per Month 
Advertising Product Availability
26882Number of Local Avails (Commercial Spots) for Local System Operators Per Hour
26883Number of National Avails for National Advertisers Per Hour
Degree of Reliance on Multiple Revenue Streams
26880Reliance on Ad Revenue Ð Ad Revenue as a % of Total Net Revenue
26881Reliance on License Revenue Ð License Revenue as a % of Total Net Revenue
Performance
26882Total Net Revenue ($ million)
26883Total Net Revenue Per Sub ($)
26884Cash Flow Margin









Table 2Ê: Cluster Group Membership and Associated Programming Types

Cluster
Group members (programming types)
Strategic Positions
    Strategy                                Mean                 SD
1: Programming Guide Strategic Group
(1 member)
¥     TV Guide (news/information)
Size 
89.50
n.a.
Vertical Integration
6619.00
n.a.
Horizontal Integration
19.00
n.a.
History
131.00
n.a.
Operation Efficiency
61.70
n.a.
Product Differentiation
2.00
n.a.
Produce Development
9.00
n.a.
Pricing
.02
n.a.
Product Scope (local ads)
18.00
n.a.
Product Scope (Ntl ads)
24.00
n.a.
Reliance on Ad Revenue
60.40
n.a.
Reliance on License Rev.
39.60
n.a.
2: Commercial-Free Movie Strategic Group
(3 members)
¥      American Movie Classics (movie)
¥ Independent Film Channel (movie)
¥  Turner Movie Classics (movie)
Size 
40.30
25.04
Vertical Integration
3887.00
2267.25
Horizontal Integration
7.00
1.00
History
92.33
67.31
Operation Efficiency
42.03
10.73
Product Differentiation
3.00
.00
Produce Development
47.73
30.73
Pricing
.14
.05
Product Scope (local ads)
.00
.00
Product Scope (Ntl ads)
.00
.00
Reliance on Ad Revenue
.00
.00
Reliance on License Rev.
99.67
.58
3: Young, Differentiated,  & Integrated Strategic Group (12 members)
¥        Animal Planet (educational)
¥       Cartoon Network (children)
¥        CNN/fn (news/information)
¥       Comedy Central (Specific Mix)
¥     Court TV(specific mix) 
¥      E! Entertainment (specific Mix)
¥   Food Network (life/leisure)
¥   Golf Channel (sports)
¥     History Channel (educational)
¥    Outdoor Life (life/leisure)
¥       Speedvision (sports)
¥  Travel Channel (life/leisure)
Size 
36.43
16.04
Vertical Integration
4612.67
2595.67
Horizontal Integration
13.58
4.40
History
66.08
34.53
Operation Efficiency
49.42
21.83
Product Differentiation
2.50
.52
Produce Development
80.40
52.04
Pricing
.07
.04
Product Scope (local ads)
5.17
1.03
Product Scope (Ntl ads)
17.08
2.15
Reliance on Ad Revenue
50.97
13.27
Reliance on License Rev.
44.44
16.85
4: Established, General, & Integrated Strategic Group 
(8 members)
¥ BET (ethnic)
¥      CNN + Headline  News (news/info)
¥ Discovery Channel (educational)
¥   Fox Family Channel (general)
¥        The Learning Channel (educational)
¥        TBS (general)
¥        TNT (general)
¥     USA Network (general)
Size 
80.91
27.24
Vertical Integration
4657.00
3002.21
Horizontal Integration
8.63
4.14
History
212.38
48.12
Operation Efficiency
22.34
6.12
Product Differentiation
1.50
.53
Produce Development
39.46
12.21
Pricing
.22
.15
Product Scope (local ads)
3.75
1.67
Product Scope (Ntl ads)
16.75
3.99
Reliance on Ad Revenue
57.48
8.86
Reliance on License Rev.
40.21
7.83
5: BroadcastersÕ Differentiated Branch Strategic Group (13 members)
¥      A&E Network (specific mix)
¥        Box (music)
¥       CNBC (news/info)
¥  Country Music TV (music)
¥        ESPN2 (sports)
¥    Home & Garden TV (life/leisure)
¥   Lifetime (general)
¥        MTV (music)
¥       Nickelodean (children)
¥    Sci-Fi Channel (specific mix)
¥   TNN (music)
¥       VH1 (music)
¥       Weather Channel (news/info)
Size 
62.65
13.70
Vertical Integration
.00
.00
Horizontal Integration
6.23
3.52
History
153.92
59.74
Operation Efficiency
23.78
13.32
Product Differentiation
2.15
.38
Produce Development
42.93
11.82
Pricing
.11
.06
Product Scope (local ads)
4.31
.75
Product Scope (Ntl ads)
17.31
3.90
Reliance on Ad Revenue
62.74
9.25
Reliance on License Rev.
30.12
11.99
6: Small but Trying Strategic Group (6 members)
¥   Bravo (specific mix)
¥  FX (general)
¥      Game Show Network (life/leisure)
¥  Goodlife TV Network (general)
¥        Knowledge TV (educational)
¥        MSNBC (news/info)
Size 
29.20
14.41
Vertical Integration
429.67
1052.46
Horizontal Integration
3.67
4.03
History
90.33
83.67
Operation Efficiency
93.10
68.69
Product Differentiation
2.17
.75
Produce Development
83.28
85.10
Pricing
.11
.10
Product Scope (local ads)
3.83
2.04
Product Scope (Ntl ads)
17.50
2.81
Reliance on Ad Revenue
28.32
14.11
Reliance on License Rev.
57.33
13.69
7: Established & Content-Valued Strategic Group 
(2 members)
¥    Disney Channel (children)
¥       ESPN (sports)
Size 
57.85
25.24
Vertical Integration
.00
.00
Horizontal Integration
8.00
.00
History
209.50
30.41
Operation Efficiency
20.60
7.50
Product Differentiation
2.00
.00
Produce Development
40.25
10.39
Pricing
.67
.03
Product Scope (local ads)
2.00
2.83
Product Scope (Ntl ads)
8.00
11.31
Reliance on Ad Revenue
22.80
32.24
Reliance on License Rev.
70.45
33.45











































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[1] Endnotes:

 The term, multichannel video programmers, is used in place of cable networks since the distribution of these video programming services is no longer limited to the cable television platform.
[2] 
 181 of which were exclusively cable networks in 1999.
[3] 
 Core scope variables are the operational strategies such as market segments targeted, types of products/services offered in the market (differentiation), R&D, pricing, and advertising, while resource variables are more established beneficial strategic positions such as efficiency, size, and years of operation.

[4]  Paul Kagan Associates included in its databook the top 59 most widely available cable networks (by number of subscribers). Disney Channel is also included in the databook because it is sometimes marketed as part of a basic cable network tier. 

[5]  A video programmer will be classified into the ÒgeneralÓ programming type category if it offers a diverse, broad programming content similar to those of broadcast networks (superstations are included here) (e.g., USA Network). If it predominantly offers films of all kinds, it would be in the ÒmoviesÓ category (e.g., Tuner Movie Classics); if it offers programming content that mainly appeals to children, it would be in the ÒchildrenÓ category (e.g., Nickelodeon); if it predominantly offers music video and related music programs, it would be in the ÒmusicÓ category (e.g., MTV); if it offers sports programming of all kinds, it would be in the ÒsportsÓ category (e.g., ESPN); if it offers news of all kinds and information-oriented programming such as weather reports and program schedules, it would be in the Ònews/informationÓ category (e.g., CNN and TV Guide); if it offers religious programs, it would be in the ÒreligionÓ category; if it offers programming that appeals to specific ethnic groups, it would be in the ÒethnicÓ category (e.g., BET); if it offers non-fiction programming such as documentary and other educational content, it would be in the ÒeducationalÓ category (e.g., The Learning Channel); if it offers programming that has a variety of formats but still is still in a particular interest area (e.g., Comedy Central), it would be in the Òspecific mixÓ category; and finally, if it offers programming that focus on lifestyle and leisure activities, it would be in the Òlife/leisureÓ category (e.g., Travel Channel).

[6]  One of the key requirements of cluster analysis is that the variables used in the analysis should be continuous and measured in comparable scales, see Ketchen and Shook, 1996. Hence, categorical variable, programming types could not be included in the initial analysis. However, it will be included in the subsequent cross-tab analysis with the resulting clusters.

[7]  Though 1999 data are available in the dataset for most variables, advertising avails data are missing for most cable networks.  

[8]  Ketchen and Shook (1996) suggested that this process allows variables to contribute equally to the definition of clusters.

[9]  Punj and Stewart (1983) noted that such an approach increases validity of solutions since K-means clustering algorithms are less impacted by presence of outliers in the data.

[10]  This may possibly due to its lack of vertical relationship with MSOs. Members of this group may be in a less favorable position in the negotiation of license fees. In fact, license fee revenues account for only 30% of total revenues in this group.

[11]  This is the sum of all selling, general & administrative expenses. Total overhead costs related to a basic cable network, excluding expenses that can be directly attributed to actual production and programming.