Explaining Prices Paid for Television Ad Time: The Purchasing Profile Model
W. Wayne Fu School of Communication and Information Nanyang Technological University 31 Nanyang Link Singapore 637718 Phone: +65 6790 4107 Fax: +65 6792 4329
[email protected] Hairong Li Department of Advertising Michigan State University 409 Communication Arts & Sciences Building East Lansing, MI 48824-1212
[email protected] Steven S. Wildman Quello Center for Telecommunication Management and Law & Department of Telecommunication, Information Studies and Media Michigan State University 409 Communication Arts & Sciences Building East Lansing, MI 48824-1212
[email protected]
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Abstract Building on a recent theoretical model of the pricing of television ad time by Wildman (2003), this study introduces the purchasing profile, a measure of the composition of the set of products purchased by the members of a program’s audience, as a variable critical to constructing empirical models that can produce statistically satisfactory explanations for observed variation in prices paid for commercial time on television networks. Using data on prices paid for network commercial time in the US in 1997, we show that regression models incorporating two variables related to purchasing profiles, a measure of the profitability of ad-generated sales for different types of advertisers, and a proxy for the effectiveness of television ads for promoting different type of products, do a substantially better job of explaining observed variation in ad time prices than do more traditional models based on measures of the demographic composition and size of program audiences.
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Explaining Prices Paid for Television Ad Time: The Purchasing Profile Model Advertising plays a major role in the financing of the vast majority of television networks, the principal exceptions being premium pay services such as HBO and pay-per-view services, including video on demand. For the over-the-air broadcast networks, it is the sole source of revenue. According to Poltrack and Stipp (2003), advertisers spend more than $50 billion annually on television ads in the United States alone. Given the financial stakes involved, it is not surprising that the measurement of television audiences constitutes a substantial business in its own right, or that reports on prices paid for ad time are featured stories in the industry trade press. Measured in per viewer terms, the prices advertisers pay for commercial time vary considerably among programs, both within and across individual channels.1 Efforts by academics and other researchers to empirically model the pricing of television ad time have focused primarily on variation among programs in the size and the demographic composition of their audiences as measured by the commercial audience measurement services, principally A.C. Nielsen.2 The arguments for using demographic composition to explain price variation are fairly intuitive. Demographic measures are employed by advertisers to predict the likelihood that the members of a program’s audience will be prospective purchasers of their products and more competitive markets are expected to generate lower prices.3 It is sometimes also argued that advertisers are willing to pay more per viewer for ad time in programs with larger audiences because per viewer transaction costs are reduced when eyeballs are purchased in larger lots, and 1
Webster and Phalen (1997) and Napoli (2003) review and extend the substantial literature that explores this relationship. 2 When the prices examined are for local spot time, demographic variables are typically combined with variables reflecting variations in the structure and condition of local markets, such as the number of over-the-air broadcast stations, retail sales per capita, and rates of home ownership. See, e.g., Fournier and Martin (198??). 3 Wildman (1999) argues that viewer demand-related links between the market for advertising and the market for viewers leaves the prediction of the effect of market structure on the price of ad time indeterminate.
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because some viewers are likely to be duplicated in the audiences of programs with smaller audiences that collectively equal the audience for a more popular program. When one examines the practices of the media buyers who plan and execute advertisers’ media strategies, it is immediately apparent that they rely on a varied set of sources for information about programs’ audiences that extends considerably beyond the reports on audience size and demographic composition produced by audience measurement services. Clearly advertisers believe that with the information on audiences from these alternative sources they can do a better job assessing the merits of ad buys in different programs than would be possible if they relied on the commercial measures of audience size and demographics alone. Napoli’s (2003) review of the substantial literature generated by audience researchers and media planners suggests that the demand for additional information about audiences arises because the demographic attributes reported for a typical television program’s audience are rather crude predictors of the types of products purchased by its viewers. Because demographically similar viewers may, and apparently do, consume very different sets of products, information from other sources may enable an advertiser to improve considerably the accuracy with which it can predict the number of potential customer’s in a program’s audience. (Assael & Poltrack, 1996; D’Amico, 1999; Schroeder, 1998; Surowiecki, 2002) Simmons Market Research Bureau (SMRB) annually conducts a survey of over 20,000 U.S. households, asking them what products they buy and what media they consume. According to Assael and Poltrack ( 1991, 1993, 1996, 1999), the data generated by the SMRB surveys provides an advertiser with a much better picture of the comparative benefits of purchasing time in different programs than do the audience size and demographic data reported by Nielsen. All this suggests that we might benefit from constructing empirical models of ad pricing that incorporate more of the information used by
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buyers of ad time to compare programs’ audiences and other factors that might influence the amounts they are willing to pay for commercial time. Even if such models do not improve on the statistical fit of traditional demographics-based models, they might provide additional insight into the role of buyer-side considerations in the determination of prices for television ad time. The heavy reliance on Nielsen audience measures to study the pricing of television ad time in the U.S. undoubtedly reflects in part the high visibility of the Nielsen ratings. The audiences for national television networks are measured by Nielsen on a nearly continuous basis, and the results are provided to clients electronically on a daily basis and are reported weekly in the trade press catering to both buyers and sellers of television ad time. By contrast, the SMRB survey is conducted annually4 and its reports are used primarily by media planners working on behalf of advertisers. However, the most important factor limiting the use of findings from consumer surveys in ad time pricing models may be that it is not obvious how data on the correlation of purchases of specific products with the viewing of individual programs might be aggregated in an empirical model. Simmons reports data that can be used to assess the correlation of consumer choices among products and television programs for over 8,000 brands in 460 product categories. Recently, Wildman (2003) published a 2-product model that constructs the demand for a program’s ad time from the demands of individual advertisers. The model, which we will call the purchasing profile model, or PPM, predicts that the price charged for ad time in a television program will increase with: (1) the range of products consumed by viewers in the program’s audience, (2) the degree to which the program’s viewers resemble each other in their choices among advertised products, (3) the profits sellers realize from marginal sales of products 4
The instrument for the annual survey is presented twice during a year with a six month interval to two separate subsamples of consumers. Semi-annual reports based on the subsamples are also available for purchase.
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purchased by the program’s audience, and (4) the effectiveness of television ads in converting potential customers into actual customers for the types of products purchased by members of the audience. Whether advertisers do or do not work with more complete information about what viewers actually purchase than can be devined from measures of audience demographics, the PPM suggests that variation among viewers in their product choices impacts prices paid for television ad time in ways that cannot be captured with empirical models relying on demographic information as a proxy for the value of programs’ viewers to advertisers. Using SMRB data advertisers would have used to evaluate broadcast network programs in advance of the 1997 Fall television season, we were able to construct measures, although at very high level of aggregation, of the range of products purchased by programs’ viewers and the similarity of a program’s viewers’ choices among products. Data from other sources allowed us to construct an estimate for the average marginal profitability of products purchased by a program’s viewers and a proxy for the effectiveness of television advertising in inducing sales of these products. These variables are employed in a regression model used to explain prices reported for upfront market5 sales of time in prime time network programs for the Fall 1997 television season. As is reported below, even though by necessity the constructed measures rely on highly aggregated data, the results provide strong empirical support for the PPM and show that empirical models that incorporate more of the data advertisers use to evaluate television programs along with measures of other factors influencing their demands can shed new light on how the demands of individual advertisers and factors influencing the ways in which they are aggregated are reflected in prices paid for TV ad time.
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The upfront market is a period in early to mid summer during which networks and advertisers negotiate prices for packages of ad time in programs that will be broadcast starting in the forthcoming fall television season.
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This paper is organized as follows. The next section describes the purchasing profile model (PPM) and four testable hypotheses derived from the model that are examined in this paper. The empirical model and data set are described in the section after that. Results are presented and discussed in the fourth section and the findings are further discussed in the fifth section. The Purchasing Profile Model The PPM constructs the demand for a program’s ad time from the demands of individual advertisers, where an advertiser is the seller of a product that might beneficially be promoted through commercials placed in television programs. The focus is on the price paid for commercial time in a single television program. We assume each advertiser distinguishes among viewers according to whether they are or are not potential buyers of its product. In addition, each advertiser possesses information about the program’s audience that it uses to predict the number of viewers in the audience that are potential customers for its product. We describe an advertiser’s calculation of how much it would be willing to pay for a unit of commercial time in the program as if it knows with certainty how many potential customers the audience contains. However, the basic logic of the model carries through if advertisers purchase time in programs based on less than perfect predictions of the numbers of potential customers in program audiences. From an advertiser’s perspective, a program’s viewers are either potential customers or they are not. The PPM assumes that the value of a program’s audience to an advertiser is proportional to the fraction of the audience comprised of people who might buy its product. For product i, let fi designate this fraction. How much the advertiser selling i (advertiser i) is willing to pay for each potential customer in the program’s audience is a function of the contribution
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exposure to its commercial in the program would make toward turning a potential customer into an actual customer and the profit it realizes from sales to a representative customer. Let ∆i be the increase in the likelihood that a potential i customer becomes an actual i customer after seeing a commercial for i in the program, given purchases of ad time or space advertiser i has made or expects to make from other media companies selling access to audiences, and define mi and ti, respectively, as the contribution the marginal unit of i sold makes to the advertiser’s profits and the number of units of i purchased by a representative customer. miti is the value of the marginal customer to advertiser i. Let wi be the maximum advertiser i would be willing to pay for a unit of commercial time in the program. Given fi, mi, ti, and ∆i, for n the number of viewers in the program’s audience we have: wi= nfi∆i miti.
(1)
The amount i is willing to pay per viewer in the program’s audience is: wi/n= fi∆i miti.
(1′)
If we rank order advertisers according to willingness-to-pay such that w1 w2 w3, etc., and assume unit demands for commercial time by advertisers, this rank ordering describes the inverse demand function for the program’s commercial time.6 Suppose advertisers are price takers and the program sells a units of ad time. Then the price paid for each unit of time sold by the program will be at least wa. If the program sells all units of ad time at a common price, that price will be wa. Increasing the homogeneity of the program’s viewers as measured by the sets of products they purchase will result in an audience whose purchases are concentrated in an increasingly small subset of the set of all products that might be advertised on television. As a consequence, the fractions of audience comprised of potential buyers for products advertised on 6
Allowing for individual advertisers to have positive valuations for more than one unit of the program’s ad time does not alter this description of the inverse demand function for the program’s ad time because an advertiser’s valuation of a second or subsequent unit of ad time can be treated as the demand of an entirely separate advertiser.
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the program (the fi) will increase. Unless the products consumed by viewers of programs with fairly consumption homogeneous audiences have systematically lower marginal customer values or are systematically less well suited to promotion on television than products purchased by people who watch programs with more consumption heterogeneous audiences, we should expect to see the price of ad time increasing with the consumption homogeneity of programs’ audiences because advertisers purchasing time in programs with more consumption-homogeneous audiences will be willing to pay more as the fractions of viewers the perceive as potential customers grow. This is an obvious consequence of increasing fi in the expression for wi in equation (1) for all i
a.7
Equally apparent from equation (1) is that increasing the profits the program’s advertisers earn from sales to their customers (miti) will also increase the price the program can charge for its ad time, as will increasing ∆i, the impact a television commercial has on the likelihood a viewer will purchase the product it promotes. We would also expect the price charged for a program’s ad time to increase if the number of products purchased by a representative audience ember increased because more advertisers would have a positive demand for the program’s ad time. Thus, the PPM generates four hypotheses that are testable with data available to the authors: that per viewer price paid for a program’s television ad time will increase (1) with the consumption homogeneity of the program’s audience, (2) with the per customer profits the sellers of products purchased by the program’s viewers would make on additional sales generated by advertising, (3) with the effectiveness of television ads for promoting the products purchased by members of
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This conclusion also depends on a being smaller than the number of products purchased by a representative viewer. This seems to be a reasonable assumption. At most an hour-long program will have 17-18 minutes of commercial time, which permits a maximum of 36 30 second commercial units—a number far smaller than the number of products purchased by most consumers.
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the program’s audience, and (4) with the number of products purchased by individual members of the program’s audience Data and Variables Variables Specific to PPM-based Models We test the four hypotheses presented above and compare the ability of regression models based on the PPM to explain variation in per rating point prices for ad time to the performance of a traditional demographics-based model for the 46 prime time series (out of 82 total) aired by the four major U.S. broadcast networks (ABC, CBS, NBC, FOX) during the Fall 1997 television season that were renewed from the previous season.8 The trade publication, Advertising Age, published a list of “average unit rates” paid for 30 second spots in prime time programs during the 1997 upfront market.9 (Advertising Age, 1997a) Broadcasting and Cable magazine publishes the weekly estimates of prime time network programs’ ratings (percent of all viewers watching television who watch each program) produced by Nielsen Media Research, the dominant audience measurement service in the U.S. For each program, we computed the simple average of the weekly Nielsen estimates reported by Broadcasting and Cable for the Fall 1997 season. We then divided the 30 second rates published by Advertising Age denominated in $1,000 by the corresponding season average Nielsen ratings to create UnitRate, an estimate of the average price per rating point paid by advertisers for each program in the sample. UnitRate is the dependent variable in all the regression models described below. The range of variation in
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The 46 prime-time programs are 20/20, 3rd Rock, 48 Hours, Beverly Hill 90210, Boy Meets World, Caroline in the City, Chicago Hope, Cops, Cosby Bill, Cybill, Dateline NBC-Friday, Dateline NBC-Tuesday, Diagnosis Murder, Dr. Quinn Medicine Woman, Drew Carey, Early Edition, ER, Ellen, Family Matters, Fox Tuesday Night Movie, Frasier, Friends, Home Improvement, Homicide: Life on the Street, JAG, Law & Order, Living Single, Mad About You, Melrose Place, Millennium, Murphy Brown, Naked Truth, The Nanny, Nash Bridges, News Radio, NYPD Blue, Party of Five, The Pretender, Prime Time Live, Profiler, Promised Land, Sabrina, Seinfeld, Spin City, Suddenly Susan, and Walker, Texas Ranger. 9 Prior to each Fall television season, advertisers negotiate with networks for packages of commercial availabilities. These negotiations, which take place during June and July, are referred to as the “up front market.”
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UnitRate is quite large, with the highest value more than five times the lowest, as shown in Figure 1. Figure 1 Distribution of Unitrate Among 46 Programs
Frequency
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4
2
0 5
10
15
20
25
30
UnitRate in $1,000/rating point
Simons Market Research Bureau annually surveys over 20,000 consumers about their viewing habits and the products they purchase, recording survey responses for over 8,000 brands in 460 product categories. Survey participants are asked what television programs they watch and the brands and types of products they purchase. To construct measures of the range and distribution of products purchased by viewers in the audiences for prime time programs, we utilized data from the 1997 Simmons National Consumer Survey database (Simmons Market Research Bureau, 1997), which was based on consumer interviews from 1996 and 1997, all conducted prior to the Fall 1997 television season. (The fact that audience purchasing habits can be assessed only after a program has been aired and attracted an audience to be studied is the
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reason we limited our sample of programs to the 46 programs in the networks’ prime time schedules that were renewed from the previous television season.) For survey participants indicating they watched a given program, it is possible using software tools provided by Simmons to calculate the percentages who also state they purchase various brands and products from the various Simmons product categories. Advertising Age and its affiliated online service, AdAge.com, annually report U.S. advertising expenditures broken down by media type for 33 aggregated product categories, where a product category is a set of (for the most part) closely related products. AdAge.com (2004b) reports that television advertising for products in its 33 product categories accounted for 98.38 percent of network television advertising in 1997. We matched Simmons survey results on products purchased and programs viewed to corresponding AdAge.com product categories to construct a measure of the fraction of a program’s audience purchasing products from the corresponding AdAge.com product categories for each of the programs in the sample, which we used as a proxy for the fraction of a program’s audience that might be potential purchasers of products in each category. By their respective descriptions, 21 of the AdAge.com categories appeared to have nearly direct matches in the Simmons product categories. For these AdAge.com categories and for each program, we used the fraction of Simmons survey respondents who reported they watched the program and purchased at least one product from the corresponding Simmons category as our measure of the fraction of the program’s audience purchasing products from the AdAge.com category. For the remaining 12 AdAge.com categories, it was necessary to combine two or more Simmons categories to produce a close match for the AdAge.com categories. In these cases, for each program we used the simple average over the Simmons categories aggregating to an Ad Age category as our measure of the fraction of the program’s viewers purchasing products from the
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AdAge.com category. We will refer to these figures as the categories’ audience penetrations for the programs. The AdAge.com product categories and the number of Simmons categories combined to match each of them are listed in Table 1.
Explaining Prices for TV Ad Time Table 1 AdAge.com and Simmons Product Categories Top AdAge.com product categories
Number of Simmons categories matched to the AdAge.com category
Automotive, auto accessories, equipment & supplies Retail, department and discount stores Movies and media Toiletries & cosmetics Medicines & proprietary remedies Food & food products Financial Restaurants & fast food Airline & cruise travel, hotels & resorts Telecommunications Computers & software Direct response companies Insurance & real estate Apparel Beverages Confectionery & snacks Beer, wine, & liquor Audio & video equipment & supplies Games, toy, & hobby craft; Household soaps, cleansers & polishes Household paper, plastic& foil products Building materials, equipment & fixtures Sporting goods Household appliances equipment Schools, camps, seminars Manufacturing, equipment, freight Pets, pet foods & supplies Gasoline & oil Office machines, furniture & supplies Household furnishings & accessories Eye glasses, medical equipment & supplies Fitness & diet programs and spas
1 1 1 15 14 22 1 2 3 1 1 1 13 1 6 6 4 1 1 1 3 1 1 1 1 1 2 1 1 1 2 1
14
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For each program, its audience penetration figures for the 33 product categories were used to create two variables reflecting different aspects of the distribution of products purchased by members of its audience. The first, the Purchasing Profile Index, or PPI, is simply the sum of percentages of viewers reporting purchases from each category for a given program. That is for program j, product categories indexed by k=1, … , 33, and αj,k the percentage of viewers in program j’s audience reporting purchases from product category k, PPI j =
33 k =1
α j, k . For example,
consider a hypothetical program for which Simmons records positive penetration rates for only 3 product categories and suppose they are 30%, 40% and 50%. Then the PPI for this program would be 0.3+0.4+0.5 = 1.20. We label the second variable describing the distribution of audience purchases the PPI-HHI, because, with the exception of not multiplying the decimal measures of shares by 100, it is calculated with a formula identical to that the U.S. antitrust agencies use to calculate a product maket’s HHI (Herfindahl-Hirschman Index). The PPI-HHI for program j is calculated as: PPI − HHI j =
33
k=1
2
( ). α jk
PPI j
The HHI is used by antitrust authorities to assess the degree of concentration in economic markets and it plays an analogous role here. For antitrust analysis, a market’s HHI is calculated as the sum of the squared market shares of all firms serving the market. Because shares are squared, a market’s HHI increases as the distribution of sales in the market becomes concentrated in the hands of fewer sellers. For a program’s PPI-HHI, each product category’s share is the percent of the program’s viewers who purchase at least one product in the category divided by the sum of the corresponding percentages for all product categories. A program’s PPI-HHI increases as consumption choices by the program’s viewers become concentrated in
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fewer product categories, which means that the program’s viewers are becoming more similar in their purchase choices. Note that the value of the PPI-HHI is determined by the values of the product categories’ shares of audience relative to each other, rather than by their absolute values, while the value of the PPI is determined by the absolute values of the category shares. The relationship between a program’s PPI and its PPI-HHI is illustrated in Figure 1 for two products , 1 and 2, with f1 and f2 the fractions of the program’s audience purchasing products 1 and 2 respectively. The dashed rays through the origin are PPI-HHI isoquants because along a ray the relative shares of viewers purchasing the two products are always the same. The straight solid lines with 45 degree negative slopes are PPI isoquants because the sum of f1 and f2 is not changed by movement along one of these lines. As long as the products’ shares are both less than one (a condition satisfied for all 33 of our product categories), it is always possible to shift to a higher or lower PPI isoquant along any PPI-HHI isoquant and vice versa. We may thus treat the PPI and PPI-HHI as orthogonal for our regressions.
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Figure 2 Relationship Between PPI and PPI-HHI for Two Products
1
f2 PPI-HHI = ( f1+f1f2 ) 2 + ( f1+f2f2 ) 2
PPI=P1+P2 0 0
f1
1
By including both PPI-HHI and PPI as independent variables in a regression equation, we can interpret the sign of the coefficient for PPI as a test of the PPM hypothesis that the price per viewer paid by advertisers purchasing time in a program will increase as members of the program’s audience increase the number of products they purchase. Treating PPI as a measure of the overall level of purchases by a program’s audience, we can interpret the sign of the coefficient for PPI-HHI as a test of the hypothesis that the per viewer price for ad time will increase with the consumption homogeneity of a program’s viewers. According to the PPM, the signs of both coefficients should be positive.
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AdAge.com reports national advertising expenditures by year for the 33 product categories described above (AdAge.com, 2004b) and advertising to sales and margin to sales ratios for 4-digit SIC (Standard Industrial Classification) industries (AdAge.com, 2004a), where margin is calculated as an industry’s net sales minus the cost of goods sold. For all but eight of the 33 AdAge.com product categories,10 products within the category match up directly with those included in a corresponding 4-digit SIC industry (which often has the identical name). For these categories, the 4-digit SIC industry margin to sales ratio was applied directly to the category. The eight remaining AdAge.com product categories include products produced by two or more 4-digit SIC industries. For each of these product categories, we calculated the margin to sales ratio for the category as a weighted average of the margin to sales ratios of the corresponding 4-digit SIC industries using their relative industry sales as weights.11 To produce estimates of category profits, we divided a category’s margin to sales ratio by its advertising to sales ratio and multiplied by category ad expenditures. Taking the Simmons sample as representative of the U.S. population, we used category penetrations for the Simmons sample as estimates of category penetrations for the U.S. population as a whole. Multiplying these estimates of category penetration by the U.S. population for 199712 produces an estimate of the number of people in the U.S. who purchased products from each of the 33 AdAge.com categories in 1997.
10
Automotive, auto accessories, equipment & supplies; retail, department and discount stores; movies and media; financial; airline & cruise travel, hotels & resorts; telecommunications; computers & software; insurance & real estate. 11
The receipts or revenues of product industries for 1997 by SIC codes are posted on the Census Department’s website: http://www.census.gov/epcd/ec97sic/E97SUS.HTM. 12 The 1997 Population Profile of the United States (US Census Bureau, 1997) reports the US population to be 266,490,000.
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For each category, we divided estimated category profits by the estimated number of people in the U.S. purchasing from the category to produce an estimate of per customer profits. For representative product i, this is ti in equations (1) and (1′). For any given program and product category i, the percent of the Simmons’ sample purchasing at least one product from category i is an estimate for fi, so the product of category profits per customer and the percent of the program’s viewers purchasing from the category is an estimate of fiti in equations (1) and (1′), which is the potential per viewer profits from the sale of products in the category, the realization of which might be influenced by advertising. We created the variable SalePrft by adding potential per viewer profits for all 33 product categories. SalePrft is intended to reflect the potential value of the program’s audience to all advertisers. We also created AveSalePrft, which is SalePrft divided by the program’s PPI, to reflect the value to a representative advertiser of sales induced by advertising on the program. Paralleling the construction of AveSalePrft, we created AveTVAdtoSales by first creating for each product category a weighted average of the television advertising to sales ratios for the component 4-digit SIC industries (again using relative industry sales as weights). Television advertising to sales figures were calculated by multiplying category advertising to sales ratios by the fraction of all category advertising expenditures for television reported by AdAge.com for each category. (AdAge.com, 2004b) For each program a weighted average of the category TV advertising to sales ratios was created using category audience penetrations as weights. AveTVAdtoSales is this weighted average. Assuming television advertising to sales ratios are higher for products for which television makes a larger contribution to generating new sales, we use AveTVAdtoSales as a proxy for the effectiveness of television
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advertising in promoting sales of the products in a product category. This is the final variable employed in the empirical model corresponding to the PPM. Variables Specific to Demographics-based models Nielsen Media Research (1996, 1997) reports estimates for various measures of demographic composition for program audiences. For the demographics-based pricing model, we included the natural logs of Nielsen’s estimated percentages of audience that are females 1849 , males 18-49, women with four or more years of college education, and African American females. (lnPctFemale18-49, lnPctMale18-49, lnPctWomen-college, lnPctWomen-black) The male counterparts for the last two variables were dropped from the regressions because they were highly collinear with the measures for women. It is generally accepted that advertisers value most viewers in the 18-49 age groups, and perhaps women more than men. In addition, education and minority status are also believed to influence buying behavior and thereby advertisers’ valuations of audiences. We also included two measures of viewer income, percentages of audience earning more than $60,000 annually and less than $30,000 annually. (Pct>$60K and Pct$60 and Pct
