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Explaining Prices Paid for Television Ad Time: The Purchasing Profile Model

Explaining Prices Paid for Television Ad Time: The Purchasing Profile Model. W. Wayne Fu Nanyang Technological University, Singapore Hairong Li & Steven S. Wildman Michigan State University, USA 5th Workshop on Media Economics Bologna, Italy October 19 & 20, 2007.

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Explaining Prices Paid for Television Ad Time: The Purchasing Profile Model

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  1. Explaining Prices Paid for Television Ad Time: The Purchasing Profile Model W. Wayne Fu Nanyang Technological University, Singapore Hairong Li & Steven S. Wildman Michigan State University, USA 5th Workshop on Media Economics Bologna, Italy October 19 & 20, 2007

  2. General setting for the research • Television advertising is a major industry with over $50 bil/yr in sales in U.S. alone • As a consequence, audience measurement and prediction of ad time prices receive lots of attention within the industry • Because commercial media depend substantially on ad revenues and select content to increase the profitability of audiences generated, the pricing of ad time has been a matter of considerable interest to academic researchers focusing on media

  3. Past research has focused on audience demographics • Participants in markets for ad time rely on a variety of sources of information about the audiences generated by television programs. • Advertisers want to know how much programs’ audiences are worth to them as ways to reach potential customers for their products • Program suppliers (networks) want to know how much advertisers might be willing to pay for access to their audiences • Lion’s share of the money spent on audience research services has gone to audience measurement services (Nielsen in U.S.) who measure audiences in terms of size and demographic composition • Demographic measures valued because individual consumers consumption choices are believed to be significantly influenced by their demographic characteristics.

  4. Hedonic models of ad time pricing • The intuitive appeal of the demographics-determines-consumption hypothesis and the prominence of demographic measures in the reports of the most prominent audience measurement services led to development of statistical models of ad time pricing that viewed programs’ audiences as products and their demographic components as product characteristics. • Implicit in these models are the assumptions that: • there is a common market valuation of each of the underlying components so that the price of ad time in a program is the sum of the valuations of each of the program’s audience’s demographic components. • Each advertiser purchasing a unit of a program’s ad time values these demographic components the same.

  5. Limitations of hedonic ad pricing models • Advertisers want to reach potential customers and demographic characteristics often are not accurate predictors of individual consumers’ product choices • Advertisers turn to other sources of information about what viewers of specific programs purchase, so demographics may not be a good proxy for what advertisers know about audiences • Because they are not bottoms-up descriptions of the demand for ad time, hedonic models cannot be used to examine more fundamental factors influencing advertiser demand and market-set prices

  6. Purchasing Profile Model Critical elements of the Model • Advertisers’ profits on advertised products • Program’s whose viewers purchase higher margin products should be able to charge more for ad time • Suitability of advertised products for TV promotion • More effective are TV ads for promoting products purchased by members of a program’s audience, the higher should be the price of ad time • Consumption composition of a program’s audience • Price paid for ad time should increase with the number of products purchased by a representative audience member • Price paid for ad time should increase with the similarity of products purchased by different audience members

  7. The consumption composition of audiences and the demand for ad time • Subject of Wildman (2003) • Starts with observations/assumptions that: • Most viewers buy many products, but • Different viewers buy different products • Advertisers are only willing to pay for access to those viewers who are potential customers for their products • Given these assumptions, it can be shown that the per viewer price that can be charged for a program’s ad time is likely to increase • the larger are the sets of products purchased by individual members of its audience (because more advertisers compete for access to the audience) • the more similar are the sets of products purchased by individual members of its audience

  8. Simple example of effect of purchasing profile similarity on price of ad time • 2 programs: A & B • Each captures 100 viewers • 4 products: 1, 2 , 3, 4 • Advertisers willing to pay $1 for one unit of commercial time for each prospective customer in the audience • Half of A viewers are potential customers for products 1 & 2 and half are potential customers for products 3 & 4 • All B viewers are potential customers for 1 & 2 • Program sells 2 units of ad time • Prices for Ad time? $.50/viewer for time on program A $1/viewer for time on program B

  9. Empirical comparison of purchasing profile and hedonic models • We compare the statistical fit of a representative hedonic model to a series of PP-based models for explaining variation in prices paid in upfront market for ad time in prime time network television programs in the U.S. for the Fall 1997 TV season • Hedonic model: • Price/rating point = f(demographic variables, size of audience, TV network) • PP model • Price/rating point = f(range of products purchased by program’s viewers, similarity of viewers’ consumption choices, per customer profit on products purchased by viewers, effectiveness of TV in promoting products purchased by viewers, size of audience, TV network)

  10. Variables and data sources for hedonic model *Fall 97; **96-97 season

  11. Variables and data sources for purchasing profile models *Fall 97, **96-97 season

  12. Constructed audience variables • fi: Average fraction for an AdAge.com product category of a program’s viewers purchasing at least one product from the Simmon’s product categories corresponding to each of the top 33 AdAge.com product categories (which account for 98% of TV ad spending in U.S.) • PPI: For a program, the sum of the fi for the top 33 AdAge.com product categories • PPI-HHI: For a program, the sum of (fi/PPI)2 for the top 33 AdAge.com product categories

  13. Constructed profitability and TV suitability variables • Component measures: • Ad$=category ad expenditures [1996, Ad Age] • A/M=Ad$/(gross margin on sales) [1996, Ad Age] • PEN=% of U.S. population purchasing products from category [96-97 TV season, SMR] • USPOP=U.S. population [1996, U.S. Census Bureau] • CAT$=Total sales for products in category [1996, Ad Age] • AvgSalePrft= [Ad$/(A/M)]/[PENxUSPOP] • AveTVAd/Sales=Ad$/CAT$

  14. Predicted signs for critical PP model variables • PPI + • PPI-HHI + • AvgSalePrft + • AveTVAd/Sales +

  15. Comparison of hedonic and simplest PP model for Unit Rate *p<.05, **p<.01, ***p<.001

  16. Building to the complete PPM *p<.05, **p<.01, ***p<.001

  17. Assessment of PPM • Even though data is highly aggregated (thousands of products and brands collapsed to 33 product categories), full model explains about as much variation in prices as hedonic model • Would expect model with more refined data on advertised products to do even better • Supports a basic bottoms-up model of advertiser demand • Reveals importance of homogeneity in viewer purchasing behavior as factor influencing price • Points to more sophisticated viewer-as-consumer models of programming competition

  18. Implications for programming strategies • Traditional economic models of program choice predict that programs of different types will be supplied in proportion to the fractions of audience that want to see them • Audience maximization models based on assumption that all (demographically similar) viewers are worth the same • Our research shows that viewers are consumption differentiated and thus differ in their contributions to programs’ ad revenues in ways that should be reflected in networks’ programming strategies • Wildman (2003) and Kim and Wildman (2006) predict that program types that attract more consumption homogeneous audiences will be supplied in greater numbers than audience size alone would predict • The empirical study reported here says we can expect the same for program types attracting viewers whose product purchases generate higher profits for advertisers and viewers who purchase products well-suited to television promotion

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