Forecasting Advertising Responsiveness for Short-Lifecycle Products*

Jackie Y. Luan Yale School of Management

135 Prospect St, PO Box 208200 New Haven, CT 06520

Email: jackie.luan@yale.edu Phone: 203-432-5661 Fax: 203-432-3003

K. Sudhir

Yale School of Management 135 Prospect St, PO Box 208200

New Haven, CT 06520 Email: k.sudhir@yale.edu

Phone: 203-432-3289 Fax: 203-432-3003

December, 2005

* We thank participants at the 2005 Marketing Science Conference at Atlanta and the 2005 AMA-Sheth Doctoral Consortium at the University of Connecticut for their comments.

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Forecasting Advertising Responsiveness for Short-Lifecycle Products

Abstract

Entertainment products such as movies and DVDs have short life-cycles; their demand is

usually greatest at the time of launch and declines very quickly in subsequent weeks. Hence

marketers have to decide on advertising levels for such products even before the product is

launched. This paper introduces the problem of “forecasting” advertising responsiveness prior to

product launch as a critical ingredient in new product sales forecasting models. The paper also

solves the “slope endogeneity” problem that arises from private information possessed by

managers about the heterogeneous effects of advertising on demand. This endogeneity is

different from the “intercept endogeneity” problem that has been widely recognized in the

literature, which is due to private information about the demand level (regardless of endogenous

variables). We develop a conceptually simple and easy-to-implement control-function approach

to correct for the slope endogeneity bias and apply this procedure to the DVD market. We find

that average advertising elasticity for DVDs would be underestimated by 20% if the slope

endogeneity problem were ignored. As the first empirical study of the determinants of DVD

advertising responsiveness and sales, our analysis yields findings of substantial interest to both

researchers and executives involved in entertainment marketing.

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1. Introduction

Entertainment products such as movies and DVDs have remarkably short life-cycles:

demand usually peaks upon product launch and the majority of sales occur within a few weeks

following the introduction. As an illustration, Figure 1 and Figure 2 show the sales patterns for

“The In-Laws” in the theatrical market and in the DVD market, respectively. This exponential

sales pattern is typical of over 90% of theatrical movies. Among all movie DVDs released during

2001-2004, sales during the first week (first four weeks) accounts for 32% (65%) of the sales

over the first 26 weeks.1

Given this typical exponentially decaying sales pattern, advertising for short life-cycle

products is also concentrated around the product introduction. Therefore, marketers need to make

their advertising (and other marketing-mix) decisions before the product is launched, and there is

little, if any, room for adjustment afterwards. Figure 3 depicts the average advertising levels, as

measured by TV Gross Rating Points (GRP), across new-release movie DVDs during 2001-2004:

76% of advertising happens on the week of product launch or earlier.

Choosing the right advertising level for such a product is particularly challenging. For

products with longer life-cycles such as most consumer packaged goods and services, marketers

can experiment with different levels of advertising and measure changes in demand to infer

advertising responsiveness. This is infeasible in the context of short life-cycle products such as

movies and DVDs, for which an accurate assessment of advertising responsiveness is needed

before product introduction. Therefore, it is not enough to infer advertising responsiveness from

the sales history of a particular product; rather, we need to forecast advertising responsiveness

for a unique product before it is introduced. This study develops an approach to forecast

advertising responsiveness for such short life-cycle products. It is important to note that while

there is a substantial body of literature on new product sales forecasting, there is no literature to

date on the forecasting of responsiveness to a marketing-mix variable.

We illustrate the approach using data from the U.S. DVD market. So far the DVD market

has received little attention in academic research compared to the attention given to the theatrical

movie market (e.g., Sawhney and Eliashberg 1996; Eliashberg et al. 2005). But DVDs currently

bring far greater revenues to studios than theatrical films do and have been growing much faster

1 The short life cycle of theatrical movies has been well known in the industry; but the short life cycle of DVDs has become evident only recently. Industry observers now note that DVDs are “disappearing from retail outlets almost as quickly as movies burn off at the box office” (Mohr 2005).

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than box-office revenues. In 2004, DVDs accounted for $15 billion in sales, whereas the box-

office revenue totaled only $ 9 billion. Further, the 2004 sales of DVDs represented a 33%

growth over the 2003 sales, while box-office sales were essentially stable. Thus the DVD market

has become the most important revenue stream for major studios, and a movie’s theatrical run is

virtually “a marketing campaign” for downstream products — particularly the DVD — to come

(Eisinger 2005). Another colorful description of its role as the studios’ cash cow is that DVD

serves as the “corporate ATM machine” for the studios (de Lisle 2005).

The basic idea in generating the forecast is to identify how similar products responded to

advertising historically and use these estimates to make a prediction for a new product. In

general, forecasting can be a particularly difficult task in the context of entertainment products

(such as movies and music) because such products are “unique” by nature. Even if many

quantifiable variables such as genre, star presence, and production costs could be used for

forecasting, there is still substantial variation that is unexplained due to each movie’s

idiosyncrasies. Fortunately, in the context of DVDs, this problem can be partially overcome

since there is considerable information about the DVD in the trajectory of box-office

performance for that movie, and this enables us to better account for the movie’s unique

characteristics when making forecasts about the corresponding DVD sales and advertising

responsiveness.

We will discuss in detail the issue of identifying attributes that will enable us to obtain

advertising responsiveness forecasts in Section 3. Even assuming that we can find a reasonable

set of attributes, a particular methodological difficulty in forecasting advertising responsiveness

is the endogeneity of observed advertising levels. That is, despite controlling for a set of

observable attributes that affect advertising responsiveness, there are still (econometrically)

unobserved attributes that are (partially) observable by the firm, which will affect the choice of

advertising levels for a particular product.

In recent years, there has been considerable interest in accounting for price endogeneity with

both aggregate and individual-level data (e.g., Berry et al. 1995; Villas-Boas and Winer 1999;

Nevo 2001). These papers have convincingly demonstrated that failure to account for price

endogeneity will lead to biased estimates of price elasticity in a demand model. However all

existing advertising response models treat advertising levels as exogenous (e.g., Bass and Clarke

1972; Tellis et al. 2000; Dube et al. 2004; Vakratsas et al. 2004).

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Endogeneity problems arise in estimating a sales response model when observed levels of

marketing-mix variables are correlated with the unobserved component(s) in the demand model.

Consider a simple linear sales response function: Pα β− where P represents price. Extant

research that corrects for price endogeneity has thus far assumed that unobserved (to researchers)

factors only affect the demand level (i.e., the intercept, α ), but not marketing-mix

responsiveness (i.e., the price coefficient, β ). This assumption is unlikely to be valid in many

marketing settings. An automaker may price a car brand higher (lower) than expected because of

a larger (smaller) intercept related to the brand’s prestige (that is not captured by observed

product attributes in the data) leading to intercept endogeneity. However, it is just as likely that

the automaker has private information that the car has a lower (higher) price coefficient (β ),

which may cause the higher (lower) prices leading to slope endogeneity. Marketing researchers

have typically paid attention only to the intercept endogeneity problem but have neglected the

slope endogeneity problem.

In this paper, we account for the possibility that firms can possess more information than

the researcher not only about the intercept, but also about the marginal effect (i.e. the slope

coefficient) of advertising; and that the private information will affect the level of advertising.

We develop a control-function approach that is based on previous studies in labor economics and

econometrics (e.g., Garen 1984; Wooldridge 1997) to address the slope endogeneity problem and

make it amenable to a context involving multiple endogenous variables. In our application, we

not only seek to estimate advertising responsiveness but also the responsiveness to the theater-to-

DVD release lag.

The “slope endogeneity” problem discussed here is similar to the “non-random marketing-

mix” variables problem discussed in Manchanda, Rossi and Chintagunta (2004). They estimate

the individual physician’s responsiveness to detailing, where the observed level of detailing is

chosen based on the physician’s detailing responsiveness. Similar to Manchanda et al., we also

do not make assumptions that managers set their advertising optimally, but allow for the

possibility that use the private information they have. Unlike their paper which estimates the

detailing responsiveness of different physicians for one product, our focus is on forecasting the

average advertising responsiveness in the market for different products (DVDs). Further, our

approach illustrates how to deal with multiple endogenous variables: advertising and theater-to-

DVD lag, both of which are jointly chosen by managers with private information that the

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researcher does not have. Finally, Manchanda et al. use a Bayesian estimation approach to solve

the problem. In contrast, our analysis uses a two-step control function approach involving OLS

regression to solve the slope endogeneity bias problem. Since it is intuitive and simple to

implement, we hope that this approach will be widely used in future work.

In summary, this paper makes the following contributions:

(1) It contributes to the entertainment marketing literature by extending the extant focus on the

theatrical film market to the increasingly more profitable market for DVDs. In the process,

we obtain several substantive insights about the determinants of DVD sales and of

advertising responsiveness.

(2) We introduce the problem of advertising responsiveness forecasting. While there has been

great emphasis on new product sales forecasting, these models typically do not take into

account the role of endogenous marketing-mix actions. Our research fills this void by

empirically forecasting responsiveness to advertising, a critical element of the marketing

mix.

(3) We address the issue of “slope endogeneity” and develop a flexible yet easy-to-implement

estimation approach when there are multiple endogenous variables, for which managers

may possess private knowledge about the marginal returns that the researcher does not have.

In the DVD context, two such endogenous variables are advertising and release timing. We

develop a two-step control-function procedure to address this problem.

The rest of the paper is organized as follows. In section 2, we discuss the empirical setting,

data, and generate hypotheses about the predictors of advertising responsiveness. In section 3, we

introduce the problem of slope endogeneity and discuss a bias-correction approach to solve this

problem with both cross-section and panel data. Section 4 discusses the empirical application

and the results. Section 5 concludes and discusses future research directions.

2. Empirical Data and Hypotheses

2.1. Advertising in the DVD category

The DVD (digital versatile or video disc) technology, commercially introduced in 1997, has

created a very profitable hardware and software market in just a few years. DVD players are the

fastest-growing consumer electronic product in history (The Digital Entertainment Group 2005),

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outpacing even CD players and PCs).2 As DVD players were adopted by 75 million U.S.

households (68% penetration rate) by June 20053, pre-recorded DVD software mushroomed

from 5,000 to over 40,000 titles. Over 3.9 billion pre-recorded DVDs were shipped to retailers

between 1997 and 2004.

The Digital Entertainment Group (DEG) reports that on average, a household that owns a

DVD player buys 16 discs per year; the purchase rate is as high as 24 discs per year for

households owning multiple players. Industry observers report that consumers show “an

insatiable interest in owning DVDs,” especially DVDs of feature movies (Kurt 2004). In 2004,

U.S. box-office gross remained stagnant at about $9 billion, while DVD sales accounted for

$15.5 billion4, a 33% growth from 2003, which far exceeds the theatrical revenue. The enormous

growth of the DVD market has far exceeded the expectations of the movie industry and is

fundamentally reshaping the landscape of the industry.

In a recent paper, Lehmann and Weinberg (2000) model the interaction between the

theatrical and home video (VHS cassette) markets using data from 1996-1997. They do not

consider advertising in the home video market because there was almost no advertising for home

videos in that period. However, advertising has grown dramatically with the advent of DVDs.

Advertising expenditures on new DVD releases have risen at a steep rate of 50-60% annually.

Studios spent $641 million on home video advertising in 2002, up 63% from the previous year,

according to TNS Media Intelligence. Experts in the media and entertainment industry credit the

high DVD advertising expenditures as among the major reasons why TV networks pulled in a

record-setting $9.2 billion in the 2003 upfront commercial buying period (Netherby and Magiera

2003). While marketing expenditure for DVD releases still lags far behind that of advertising for

theatrical releases ($3.3 billion in 2003), the motion picture industry is swiftly adapting itself to

the new revenue structure where DVDs are the major source of revenues by increasing

advertising for many new DVDs (Schiller 2004). For instance, Columbia/TriStar spent a record-

setting $100 million in a marketing campaign to promote the Spider-Man 2 DVD. The release of 2 It took only five years for 30 million DVD players to be sold, compared to about eight years for CD players, and 10 years for PCs to reach the same volume mark. 3 DEG reports that about 47 percent of DVD owners have more than one player, due to the growing popularity of home theater systems, portable DVD players, and DVD recorders. 4 DVD rentals totaled $5.7 billion, up from $4.5 billion in 2003. Couple that with DVD sales of $15.5 billion, the DVD market over twice as large as the theatrical exhibition market. With DVD penetration spiraling, VHS market has been dwindling: VHS sales dropped 42 percent to 240.4 million from 2002, while VHS rentals fell 23 percent to 53.2 million (MPAA 2004). Therefore, the empirical study does not consider the VHS market.

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Elf on DVD was backed up by a large-scale promotion that far exceeded the theatrical marketing

program. While studio advertising spending grows about 15% annually, DVD advertising

spending grows at a speed of 35% (Netherby and Magiera 2003).

2.2. Data

Our sample includes newly released movie DVDs that were introduced between January

2000 and October 2003.5 The movies in the sample opened in theaters between 1999 and 2003.

We exclude DVD titles with box-office revenues lower than $5 million. 6 For each of the

remaining 526 DVD titles in our sample, we collect data on box-office variables (e.g., box-office

opening date, number of exhibitors’ screens, box office revenues, advertising expenditure for the

theatrical release, competitive set, and seasonality), DVD variables (e.g., DVD release date,

retail price, sales, TV advertising GRPs,7 DVD content enhancements, and distributor label) as

well as movie attributes (such as its production budget, genre, awards and nominations, star

power, MPAA rating, and critical reviews). Table 1 reports the key descriptive statistics of the

sample, while Table 2 summarizes the relevant categorical variables. We also collect monthly

data on DVD player penetration rate in the U.S. to control for the effect of a growing hardware

installation base on the software sales. Figure 4 plots the growth in adoption of DVD players in

the U.S. between 2000 and 2003.

2.3. Predictors of advertising response

The basic idea in forecasting advertising responsiveness is to identify how similar products

introduced previously have responded to varying levels of advertising and use this to make a

prediction. In general, forecasting can be a particularly difficult task in the context of

entertainment products such as movies and music because such products are “unique” by nature.

But as discussed earlier, there is a substantial amount of information in the box-office

5 We do not consider catalog DVDs for three reasons. First, new release DVDs account for a large majority of revenues while catalog DVDs represent a small proportion of total pre-recorded DVD sales. Second, sales for catalog DVDs may be influenced by factors beyond what is considered in this study, such as a director or an actor’s other career achievements. Finally, advertising for catalog DVDs are minimal or non-existent. Note that these movies in the sample were released theatrically between June 1999 and June 2003. 6 We deem $5 million in theatrical revenue as a reasonably low threshold to be included in the sample: those movies under this threshold are typically small-budget movies targeted at a niche market and are marketed differently than the majority of Hollywood feature movies (e.g., independent distributors typically cannot afford any TV advertising at all). In addition, movies that performed poorly in theaters may not be released on DVD, which would give rise to an additional selection bias. 7 TV is the major channel for DVD advertising, representing 60-70% of the industry spending because of TV’s ability to show the DVD trailers.

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performance that can be used to account for the movie’s idiosyncratic characteristics in making

DVD sales and advertising response forecasts.

Table 3 summarizes a number of variables that we believe may affect how the market

responds to DVD advertising. First, consistent with product life cycle theory, we hypothesize

that advertising elasticity will decline from week to week.

Second, we expect word-of-mouth (WOM) to have a positive effect on advertising

responsiveness, since consumers tend to give greater attention and attach more credibility to ads

for a product that they have received positive word-of-mouth from friends and acquaintances.

Marketing researchers have been paying increased attention to explicitly measuring

consumer word-of-mouth communication and empirically inferring its role in influencing sales

(Chevalier and Mayzlin 2003; Dellarocas et al. 2004; Godes and Mayzlin 2004b; Godes and

Mayzlin 2004a). But these studies of WOM do not examine how word-of-mouth and advertising

interact to influence the consumers’ purchase decisions. Similarly, the current advertising

literature ignores the role of consumer word-of-mouth. Given that consumers receive advertising

and word-of-mouth about a product concurrently, investigating their interaction effects is

managerially relevant. For example, should the firm spend more or less advertising a product if it

knows that the product has received positive (or negative) WOM from consumers? It has been

suggested in a few studies that advertising and word-of-mouth may be substitutable, i.e., firms

should reduce advertising in the presence of good word-of-mouth (e.g., Monahan 1984;

Zufryden 1996; Mayzlin 2001), but such a conjecture does not consider the fact that WOM

communication may affect how consumers respond to advertisements. Since consumers tend to

give more attention and credibility to the ads for a product that they have received good WOM

from friends and acquaintances, WOM can increase the effectiveness of advertising.

Theatrical advertising, on the other hand, may serve as a substitute for DVD advertising, thus

reducing advertising elasticity. We also hypothesize that lower retailer price increases

advertising elasticity, an interaction effect that has been documented for consumer package

goods (Kaul and Wittink 1995).

The DVD is typically released four to seven months after the movie opens at the box office,

and there is usually a hibernation period after the movie exits most theaters and before the DVD

is released. We conjecture that a shorter DVD release delay may mean that the movie might still

be fresh in the consumer’s memory and therefore the marginal effectiveness of DVD advertising

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might be less than when there is a longer delay. However it might also be reasonable to argue

that DVD advertising may be more effective in rousing interest if the movie is still fairly fresh in

the consumer’s mind.

We hypothesize that box-office sales has a negative effect on advertising elasticity. Since

movies are experience goods, consumers who have viewed the movie in theater would rely more

on their own experience than on new DVD advertising to make their DVD purchase decisions.

Therefore, a higher proportion of experienced consumers in the market would mean a lower

advertising response. Also, we expect the market to be more responsive to DVD advertising

during the holidays (in particular the Christmas-New Year period), when the demand tends to be

more elastic due to gift-buying.

3. The Random-Coefficients Sales Response Model

3.1. The Slope Endogeneity Problem

We first introduce the problem of slope endogeneity in the context of cross-sectional data and

discuss a two-step estimation approach that corrects for the endogeneity bias. The framework

allows for multiple endogenous variables. We then extend the approach to panel data.8

Let Sj be the dependent variable that we seek to explain and x j be the vector of exogenous

explanatory variables. Aj and Lj are the two endogenous variables that affect Sj with potential

endogeneity problems. (We use notation consistent with our empirical application to be detailed

in the next subsection, where Sj would be the logarithm of DVD sales of title j, x j would

consist of exogenous variables affecting DVD sales such as product attributes, Aj would be the

level of advertising goodwill, and Lj would be the delay in DVD release.)

Suppose the sales equation that we seek to estimate take the form:

x A Lj j j j j j jS A Lβ γ γ ε′= + + + (1)

The coefficients Ajγ and L

jγ are specified as random coefficients that are composed of a

systematic observed component (i.e., a function of observed covariates) and an unobserved

component:

, ( ) 0A A A A Aj j j jw Eγ θ φ φ′= + = (2)

8 We take such a two-step description approach for two reasons. First, from an expositional point of view, the intuition behind the approach is more easily seen in a cross-sectional setting. Second, since many applications may use only cross-sectional data, the exposition here can facilitate future research on this issue.

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' , ( ) 0L L L L Lj j j jw Eγ θ φ φ= + = (3)

where Ajw and L

jw are a vector of observed moderators (including a constant) that impact the

marginal effects of A and L on S. Ajφ and L

jφ are zero-mean econometrically unobserved

components that impact the marginal effects of A and L.

Substituting equations (2) and (3) into (1), equation (1) becomes

'( ) ( )A A L L A Lj j j j j j j j j j jS X w A w L A Lβ θ θ φ φ ε′ ′= + + + + + (4)

In the standard random-coefficients model, Ajφ and L

jφ are assumed to be random draws

from a population with density ( )F φ , which is independent of the observed variables including

jA and jL ; consequently,

(( , ) | , ) 0A Lj j j jE A Lφ φ = (5)

Further, assume that jε is conditionally independent of jA and jL , i.e.,

( | , , ) 0j j j jE A L xε = (6)

Write the econometric error as A Lj j j j j ju A Lφ φ ε≡ + + . Under the assumptions in (5) and (6),

( | , , ) ( | , , ) 0j j j j j j j jE u A L x E A L xε= = (7)

and the sales equation parameters can be estimated consistently with OLS. Problems occur if

these assumptions are violated, a likely scenario if the decision-maker has private information

about the unobserved components, ( , , )A Lj j jφ φ ε , which are not observed by the econometrician,

and uses such information in choosing the levels of endogenous variables, ( , )j jA L . For instance,

an individual tends to know more about the marginal returns of education to his or her earning

potential than the researcher does and therefore may spend more or less on education; marketing

managers may have some knowledge about how the market will respond to the ads for a

particular product, through prior experience or market research, and this will affect the actual

advertising budget. In such cases, the decision-maker’s choice of endogenous variables would be

correlated with the (econometrically) unobserved components.

Note that this problem is different from the standard price endogeneity problem that has been

extensively studied in the economics and marketing literature (e.g., Berry et al. 1995; Villas-

Boas and Winer 1999; Chintagunta 2001). In these studies, the endogenous variable, (usually

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price but could be any marketing-mix variable), is allowed to be correlated with jε in equation

(4), which captures the unobserved component that influence jS regardless of the endogenous

variable(s), and the instrumental variable (IV) estimator can be used to correct the potential bias.

As discussed earlier, we refer to this type of problem as intercept endogeneity. It does not

consider the potential endogeneity arising from the correlation between the slope coefficients and

endogenous variables, resulting from what Bjorklund and Moffitt (1987) call “heterogeneity of

rewards.” To address the latter case, which we refer to as slope endogeneity, the unobserved

marginal effects ( Ajφ and L

jφ ) should be allowed to influence the endogenous decision variables

( jA and jL ). For instance, if the firm knows that advertising for a given DVD would be highly

effective (i.e. larger Ajφ ), then it will probably spend more on advertising this particular title (i.e.

higher jA ). In this case, not only is the OLS estimator inconsistent, but the standard IV estimator

also is.

3.2. A Control-Function Approach to Endogeneity Correction

The slope endogeneity problem has received some attention in the labor economics literature,

where researchers are interested in estimating the returns to a particular choice, such as education,

employment, or union membership.9 The well-known Heckman-Lee approach can be applied to

solve this self-selection problem when the endogenous variable is binary (Heckman 1976; Lee

1978), such as employment (i.e. entering the labor force or not) and union membership (i.e. join

a union or not).10 However, this procedure cannot be applied to situations where the endogenous

variables are continuous (e.g., level of education chosen by an individual, or the number of

advertising exposures chosen by marketers). Garen (1984) proposes a control-function procedure

to correct for endogeneity bias in continuous variables for cross-sectional data and use it to

estimate the returns to schooling. Other applications include the estimation of wage premium for

risky jobs (Garen 1988) and the impact of union membership on this premium (Sandy and Elliott

1996), among others.

9 It is variably termed as selectivity bias, self-selection bias, or endogenous treatment effects. 10 This correction procedure consists of (1) estimating a probit selection equation by maximum likelihood and (2) using the resulting estimates to form additional explanatory variables in the focal equation to be estimated by least squares. Here we use the term slope endogeneity to reflect the fact that the econometric bias is caused by the individual decision-maker’s interest-maximizing choice, not by non-random missing data, problems involving sample selection (e.g., Verbeek and Nijman 1992; Wooldridge 1995).

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Below we briefly describe this approach for cross-sectional data. In the next subsection, we

shall explain the method in our empirical context and extend it to accommodate our panel-data

structure.

Suppose there exists a set of exogenous (or predetermined) variables, jz , that influence the

firm’s choice of the endogenous variables and

A Aj j jA z λ η′= + (8)

L Lj j jL z λ η′= + (9)

Write ( , , )A Lj j j jφ φ φ ε ′≡ and ( , )A L

j j jη η η ′≡ . Suppose the following assumptions are valid:

(A1) ( | ) 0j jE zη =

(A2) ( | ) 0j jE zφ =

(A3) ( | , ) ( | )j j j j j jE z Eφ η φ η η= = Γ .

(A1) is satisfied if jz consists of predetermined variables only. (A2) is the key exogeneity

assumption, stating that jφ has mean zero conditional on jz . This implies that jz should be

uncorrelated with the unobserved components, ( , , )A Lj j jφ φ ε . (A3) assumes that jφ is conditional

mean independent of jz given jη , and imposes a linear relationship between ( | )j jE φ η and jη .

Γ is a 3 by 2 matrix of coefficients that characterize the linear mapping from jη to ( | )j jE φ η .

Though a relatively strong assumption, it is not overly restrictive; it only requires that the

conditional expectations of the unobserved heterogeneity are linear in jη . The joint normality

assumption over jη and jφ as imposed in Garen (1984) is a stronger assumption than A3.

This specification allows the random coefficient for jA to be correlated with observed jL and

vice versa, a flexible formulation made desirable by the fact that firms usually design multiple

marketing-mix variables simultaneously rather than separately. Under these assumptions,

( | , , x , ) ( | , , , x , ) ( , ,1)A Lj j j j j j j j j j j j j j j j j jE u A L E A L A L z A Lη φ φ ε η η= + + = Γ (10)

This term is not zero generally, thus the OLS estimator would be inconsistent. However, if we

first obtain consistent estimates for jη from a first-stage estimation of equations (8) and (9) and

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use the resulting estimates, ˆ jη ’s, in place of jη ’s in the sales equation, then consistent estimates

can be obtained. In the case of two endogenous variables, equation (4) can be re-written as

'

1,1 1,1 1,2

2,1 2,2 3,1 3,2

ˆ ˆ ˆ( ) ( )

ˆ ˆ ˆ ˆ

A A L L A A Lj j j j j j j j j j j j

A L A Lj j j j j j j

S X w A w L g A g A g A

g L g L g g

β θ θ η η η

η η η η ε

′ ′= + + + + +

+ + + + + % (11)

Note that a standard IV approach will not eliminate the endogeneity bias unless

( | ) 0A Lj j j j jE A L zφ φ+ = , which is generally not satisfied.11 Therefore, the standard IV approach is

not adequate to correct for the slope endogeneity (e.g., Verbeek and Nijman 1992; Heckman

1997).12

3.3. The Empirical Specification

In this subsection, we extend the aforementioned method to panel data. The model

development is tailored to our empirical application.

Suppose we have a panel for J DVD titles each with T weeks of sales and advertising data.

The sales equation in (4) can be written as

x ( ) ( )A A L L A Ljt jt jt jt j j jt jt j j jtS w A w L A Lβ θ θ φ φ ε′ ′ ′= + + + + + (12)

where A A Ajt j jtφ φ φ= + ∆ (13)

jt j jtε ε ε= + ∆ (14)

Here jS is the logarithm of DVD unit sales of title j in week t, and x j is the vector of exogenous

explanatory variables affecting weekly sales (such as prices and product attributes). jtA is the

level of advertising goodwill for title j in week t, and jL is the delay in DVD release. Note that

advertising goodwill is time-variant while release delay is not. In equations (13) and (14), Ajtφ∆

and jtε∆ capture the weekly deviations from the title-specific mean levels of Ajtφ and jε .

3.3.1. Endogenous Variables: jtA and jL

11 Wooldridge (1997; 2003) shows that the IV estimator for the slope coefficient can be consistent under further assumptions such as conditional heteroscedasticity (e.g., 2 2( | )j jE z φφ σ= ) or constant conditional covariance (e.g.,

( | )A Aj j jE A zφ α= ). However, such assumptions may be overly restrictive, especially in a panel data context.

12 Heckman (1997) points out that when “individuals possess and act on private information about gains from [a particular choice] that cannot be fully predicted by variables in the outcome equation, instrumental variables methods do not estimate economically interesting evaluation parameters.”

13

Here we focus on two endogenous variables in the DVD sales model: (1) advertising, jtA ,

and (2) DVD release delay, jL . 13 The advertising goodwill stock, jtA , is specified as a

discounted sum of weekly advertising levels:

1

1ln( )t

j jtt

A ADτ

τ δ −

=

= ∑ (15)

where jtAD is the TV advertising GRPs (Gross Rating Points) for DVD title j in week t.14

While the effect of advertising on sales is well-known, the effect of product release timing is

not generally captured in a sales response model. However, incorporating release timing in the

sales function is necessitated by the institutional structure of the motion picture market. A DVD

is typically released four to eight months after the movie opens in theaters; there is usually a

“hibernation” gap, a period of time after the movie is pulled out of most theater chains and

before the DVD is released. Such inter-release delay (part of the windowing strategy adopted in

the movie industry) has evolved as a convention among studios to protect the revenues from the

theatrical window.15 The length of delay ( jL ), may impact the DVD demand, since, as is widely

acknowledged in the industry, the faster the DVD release, the higher the consumers’ awareness

and interest in the DVD. The coefficient of jL is intended to capture the degree to which a

movie’s “buzz” generated by the theatrical opening dissipates when the DVD is released later.

A possible specification for jL would be the theater-to-DVD window, i.e. the number of

days between the theatrical opening and DVD release of a movie. While this would be a good

measure for the nearly 90% of movies with the exponential decay sales pattern16, it is a problem

for the small number of sleeper movies that gradually build up consumer awareness and

therefore may peak later and not decline in sales rapidly (i.e., they have long legs). Therefore, we

formulate an empirical measure for jL that adjusts for the pattern of theatrical runs: the log

13 Price is usually an endogenous variable as well; however, in the DVD market, the studios usually charge a uniform wholesale price to retailers; retailers decide upon the market prices for various DVD titles and often use them as loss leaders to boost store traffic (Kipnis 2005). Since we are focused on the studios’ (especially advertising) decisions in this paper, it seems reasonable to ignore the endogeneity of price. Our modeling framework, however, should be able to handle the price endogeneity problem well if needed. 14 Due to the presence of zero advertising, we add one to all advertising GRPs to ensure this variable is well-defined. 15 The problem of DVD release timing is the focus of Luan (2005), which develops a consumer dynamic choice model to examine the effect of a industry-wide faster DVD release. 16 For 85 % of the movies in our sample, the first four weeks account for more than 75% of overall theatrical revenue.

14

number of days between the time when the theatrical movie gains 75% of its total box office

revenue and the time when the DVD is released. Since the data only contains weekly (i.e.

discrete-time) box-office receipts over the first three months (i.e. right-truncated), we estimate

the 75 percentile thresholds empirically using a two-parameter Weibull density function:

1( | , ) , 0, 0, 0p jq j

jpj tj j j j j

j

pf t p q t e t p q

q− −= ≥ > > (16)

The Weibull parameters p and q are estimated for each movie, and the 75 percentile is computed

using the resulting cumulative distribution function.

3.3.2. First-stage regression

Supposed the distributor’s advertising and timing decisions for DVD j are influenced by a

set of pre-determined variables, jtz :

ln( ) ,A A A A Ajt jt jt jt j jtAD z λ η η η η′= + = + ∆ (17)

ln( ) L Lj j jDELAY z λ η′= + (18)

where jtz includes all the exogenous variables in x jt as well as a set of excluded variables that

are supposed to affect the supply-side (i.e. distributor’s advertising and timing) decisions but not

how consumers respond to advertising and release timing. Note that the advertising decision is

made for each week, t, while the release timing decision is made once for each DVD;

accordingly, jz includes the across-week mean for each element in jtz . Ajη is the disturbance

common to all the observed advertising levels for title j, and Ajtη∆ is the mean-zero week-specific

deviation from the mean. Ljη is the disturbance associated with the release delay for DVD j.

The pre-determined variables, jtz should, in addition to x jt and jtw , include a set of

excluded variables that are supposed to affect the distributor’s advertising and timing decisions

but not how consumers respond these decisions. A natural source of exclusion variables would

be the supply-side factors such as advertising costs and interest rates. For instance, if studio f has

lower advertising costs for DVD j, then its observed advertising level would be higher than

another release of similar characteristics; such supply-side shocks, nevertheless, should not affect

how consumers respond to the ads. We include the following exclusion variables in jtz : (1)

studio dummies; (2) movie production costs; (3) the ratio of production costs and box-office

15

revenue; (4) the half-life of theatrical runs, computed from the fitted Weibull model; (5)

interaction terms of studio dummies with x jt , jtw and production cost. The rationale behind

these exclusion variables is as follows.

The DVD market is an oligopolistic market, dominated by a number of major labels, in

particular Warner Home Videos (Warner Brothers), Buena Vista (Walt Disney), Universal, Fox,

Columbia/TriStar (Sony), Paramount. Table 4 shows various players’ market shares in the DVD

market; the market shares in our sample of DVDs closely resemble those of the entire market.

Different studios are likely to have different cost structure related to DVD advertising production

and placement, since it is noted in the trade literature that DVD advertising is usually created by

the studios’ in-house marketing divisions rather than advertising agencies (Cardona and Fine

2003) and that major studios, mostly part of media conglomerates, leverage their sister TV

networks to get deals on spot commercials (so-called “house ads”) (Adweek 2004). Therefore,

studio fixed effects are likely to explain part of the variation in observed advertising levels but

should not affect how consumers respond, given that consumers are usually unaware of the

identity of the DVD distributor17. Studios may also vary in their financial leverage on the capital

market; therefore, if studio f has higher interest rates on its borrowed investment on producing a

movie, then it is likely to release it faster on DVD to recoup the cost and avoid higher debts. The

ratio of a movie’s production cost to its box-office revenue serves as a measure of the studio’s

financial pressure to recoup the investment from the DVD market. The half-life of the theatrical

run may also affect the studios’ timing decision. Although the majority of box-office receipts

would go to the exhibitors later into the theatrical run, studios typically refrain from releasing the

movie DVD too soon for fear of aggravating the relationship with exhibitors. Since the

advertising and timing decisions can be made simultaneously, the instruments motivated for

advertising can also be used for release timing and vice versa.

Let ( , , )A Lj j j jφ φ φ ε ′≡ , ( , )A

jt jt jtφ φ ε ′∆ ≡ ∆ ∆ , ( , )A Lj j jη η η ′≡ , and A

jt jtη η∆ ≡ ∆ . We assume that

jtz is exogenous to all error components, namely

( , | ) 0j jt jtE zφ φ∆ = (19)

Further, assume

17 This assumption may not hold if the advertising created by different studios vary systematically in quality, but this is unlikely in the market, where TV ads typically consist of a trailer from the movie and do not carry other creative elements.

16

1( | )j j jE φ η η= Γ (20)

2( | )jt jt jtE φ η η∆ ∆ = Γ ∆ (21)

Note that the advertising carryover structure does not affect how the correlated random

coefficients are corrected for: while jtA includes lagged advertising GRPs, the error term in

current-period elasticity, Ajtφ∆ , is assumed to be solely a function of A

jtη∆ (i.e., not a function of

1 2, ,...jt jtη η− −∆ ∆ , conditional on Ajtη∆ ). This assumption should be reasonable, unless firms are

forward-looking so that they adjust advertising levels in advance of a predicted change in

advertising responsiveness in a given week.

For the system of two equations (17) and (18), a pooled OLS estimator can be computed,

which is consistent under the orthogonality assumption on the error terms. Notice that the pooled

OLS estimator does not impose any structure on the second-moments of the errors (except that

they are well-defined) and allows for arbitrary serial correlation, cross-equation correlation as

well as heteroscedasticity. Once we have obtained ˆ Ajtη and ˆL

jη , we can replace Ajη with

1

1 ˆT

Ajt

tTη

=∑ ,

jtη∆ with 1

1ˆ ˆ( )T

A Ajt jt

tTη η

=

− ∑ , Ljη with ˆL

jη , and estimate

1 2ˆ ˆ( ) ( ) ( , ,1) ( ,1)A A A Ajt jt jt jt j j j jt jt jt jt jtS X w A w L A L A vβ θ θ η η′ ′ ′= + + + Γ +Γ ∆ + (22)

The OLS estimator will be consistent. Note that jtv is generally heteroscedastic, so a

heteroscedasticy-robust standard errors and test statistics should be applied (e.g., White 1982).

3.4. Operationalization of Variables

3.4.1. WOM

The consumers’ word-of-mouth about a particular movie is likely to influence the DVD

demand as well as the market responsiveness to DVD advertising. However, unlike advertising

or price, word-of-mouth is difficult to quantify. Recently, marketing researchers have come up

with several innovative approaches to measure consumer word-of-mouth. For instance, Godes

and Mayzlin (2004b) use online newsgroup conversations to measure word-of-mouth about TV

shows; Chevalier and Mayzlin (2003) use online book reviews on Amazon.com and

barnesandnoble.com as measure for word-of-mouth. Dellarocas et al. (2004) use the Internet

17

users’ ratings on Yahoo! Movies and IMDB.com to proxy word-of-mouth for movies. Utilizing

such proxies requires that the online sample be reasonably representative of the target market and

that the online word-of-mouth communication process resembles the offline process.

While these assumptions may hold in certain markets, they may not be valid in general. For

instance, the fact that a viewer gives The Pianist a rating of 10 on IMDB.com does not directly

mean that she would highly recommend the movie to her friends (possibly due to the gravity of

the subject); she might be more likely to discuss Princess Diaries with her friends, although she

gives the movie a mediocre rating of 6.

To overcome such problems, we compute an empirical measure for word-of-mouth rather

than use proxies such as online reviews. It is well acknowledged by practitioners and researchers

that the box-office sales pattern reveals information about the consumers’ word-of-mouth

communication. Studio executives admit that “the movie marker’s job is to open the movie; after

the opening weekend, the success of the motion picture mainly depends on the playability of the

picture.” (Gilbert-Rolfe et al. 2003) Consumers’ word-of-mouth communication is believed to be

responsible for the playability (also called leg or longevity) of a movie (Elberse and Eliashberg

2003). Observing the box-office sales pattern after the opening weekend, therefore, enables us to

infer the nature of word-of-mouth communication for a given movie without resorting to a proxy

source. To construct this measure, we use a regression method similar to Elberse and Eliashberg

(2003):

0 1 2, 1ln( _ ) ln( ) ln( _ ) , 2,3,...jt jt j jt jt jBOX REV SCREENS SCR REV e t Tα α α −= + + + = (23)

where _ jtBOX REV is the box-office revenue for movie j in week t, jtSCREENS is the number

of total screens allocated for movie j in week t, and 1_ jtSCR REV − is the revenue per screen for

movie j in week t-1. The parameter 2, jα captures how the movie’s performance in the previous

week impacts its current performance and thus constitutes an intuitive measure for the word-of-

mouth effect: a very low 2, jα would suggest a poor word-of-mouth while a very high 2, jα would

suggest a very favorable word-of-mouth. Elberse and Eliashberg (2003) estimate this coefficient

by pooling over all movies; instead, we estimate it for each individual movie and use the

resulting estimates, 2,ˆ jα , as the empirical WOM index for that movie.

3.4.2. Competition

18

Previous research has shown the importance of modeling competition between theatrical

movies in studying box-office sales (e.g., Einav 2003; Foutz and Kadiyali 2003; Ainslie et al.

2004). However, no previous research has studied the competition for DVDs. Competition in the

DVD market is more complicated than competition for movies. First, what is the competitive set?

One intuitive answer would be that the set should consist of other DVDs that are released about

the same time as the focal DVD. However, competition may be not only from DVDs, but also

the movies in theaters. Due to the short life-cycle of both DVD and theatrical movie and the

potential substitution between these two forms of entertainment,18 the theatrical opening of a

box-office blockbuster can dampen the consumer interest, consequently the sales, of a DVD

released at the same time.

We construct two variables of competition, one capturing the competition from other new

DVD releases (COMP_DVD) and the other capturing the competition from movies playing at

the box-office (COMP_THEATRICAL). Specifically, COMP_DVD jt is the logarithm of the

sum of theatrical revenues of all DVDs (except for title j) that are released in the t-th week

following DVD j’s release date. This measure is preferred over a simpler measure such as the

number of other DVD releases at a given week because DVD titles can vary substantially in their

appeal and consequently their competitive strength; the weighting by box-office revenues,

therefore, solves this problem. COMP_THEATRICAL jt is the logarithm of the total box-office

revenues (for all movies playing in theaters) in the t-th week following DVD j’s release date.

4. Results

4.1. Determinants of Advertising Intensity and Release Delay

Although our objective is to forecast advertising responsiveness, it would be of descriptive

interest to understand how firms currently set their advertising levels and DVD release delay. We

report the results of our first stage regression of equations (17) and (18) in Table 5.

The first column shows the coefficients associated with the observed weekly advertising

levels. As expected, the advertising level that a studio sets for a DVD title is positively related to

box-office performance; specifically, one percent increase in box-office revenue leads to

approximately 0.7 percent increase in the DVD advertising budget, making the DVD advertising

budget roughly “commensurate with box office” (Netherby and Magiera 2003).

18 Luan (2005) shows that there is considerate overlap between movie-goers and DVD viewers.

19

Further, ln(DVD_BASE) has a significantly positive coefficient, meaning that studios have

increased their DVD advertising budgets substantially as DVD player adoption increased; i.e.,

advertising has increased with increase in potential market size. Movie advertising and DVD

advertising are negatively related. This implies that ceteris paribus, studios use lower DVD

advertising for a movie that has been aggressively marketed through advertising for its theatrical

release. Thus theatrical and DVD advertising campaigns are viewed as substitutes. But such

substitution is less for movies that have received good word-of-mouth from the audience, since

there is a positive interaction between ln(MOVIE_AD) and WOM.

Studios spend less on advertising for R-rated and PG13-rated DVDs, compared to more

family-friendly G- and PG-rated DVDs. This is not surprising since advertising budgets are

smaller for markets with restricted potential. Star rating, critical reviews, and Oscar nominations

have negligible impact on advertising budgets. In terms of genre, DVDs belonging to the action

romance and sci-fi genres are advertised relatively less; animations receive considerably more

advertising.

There are substantial differences between studios in their levels of advertising for DVDs.

Among the seven major labels19, five spend significantly more than non-majors (used as the base

line here), especially Studios 3, 4, and 7. Studios 2 and 5 spend moderately more than non-

majors, while Studio 1’s advertising is about the same as non-majors. Studio 6 has the least

advertising (even relative to non-majors) for its DVDs. Such differences may reflect the supply-

side factors such as advertising production and broadcasting costs, as previously mentioned20.

In terms of weekly trends, advertising is highest during the street week21 (an 89% increase

over the pre-street-week level) and sharply declines from week to week thereafter, reflecting the

pattern depicted in Figure 3.

The second column of Table 5 reports the regression results for ln( )DELAY . Box-office

performance does not seem to have a significant effect on the DVD release delay. Oscar-

nominated movies tend to have a longer delay in DVD release. Action movies are released faster

than movies of other genres, while documentaries are released slower. In terms of studio

19 The seven studio dummies are for Warner Home Video (Warner Brothers), Buena Vista (Disney), Universal, Fox, Sony, Paramount and MGM. Studio identities are disguised in the results for confidentiality. 20 The interaction terms between the studio dummies and other covariates (such as production cost and time trend) are included in the estimation but are suppressed here due to space constraints; these results are available upon request. 21 The pre-street-week dummy is normalized to zero for identification.

20

dummies, Studio 1 seems to have the shortest DVD release schedules (and, as previously shown,

it has low advertising budgets among majors) while Studio 3 the longest (and has among the

highest advertising budgets). DVDs with “making-of” features tend to have a slightly longer

delay, which may be explained by a longer post-production creative period.

4.2. Advertising Responsiveness

We present the estimation results of the sales equation (22) in Table 6 and Table 7. Table 6

presents the estimates related to advertising and timing responsiveness as well as the endogeneity

correction coefficients, while Table 6 reports the remaining coefficients of the sales equation.

The first column shows the results of the full-correction model that corrects for both intercept

and slope endogeneity. To investigate the effects of not accounting for endogeneity, we report

results from two other models. The second column shows the results of a partial correction

model, that accounts for only intercept endogeneity. This is equivalent to the standard IV

approach that does not address the slope endogeneity problem. The third column presents the

estimation results without any endogeneity correction.

First, we discuss the differences in the estimates of advertising elasticity between the

proposed model that fully accounts for endogeneity, and the other models. The release-week

advertising elasticity is estimated to be 0.330 in the full-correction model, while it is estimated to

be 0.265 and 0.263 in the partial- and no-correction model, respectively. Thus we see that in this

empirical setting, correcting for intercept endogeneity does very little to obtain correct estimates

for advertising responsiveness. This implies that the private information that managers have is

not so much in the aggregate level of demand for different DVDs (as is typically assumed in the

standard endogeneity correction literature) as in the advertising responsiveness. Our estimates

show that advertising elasticity is underestimated by about 20% if slope endogeneity bias is not

properly corrected for.

Advertising elasticity exhibits a rather small decline in the weeks following the DVD release

(the decrease is insignificant in the second and third week and becomes statistically significant at

21

-8% in the fourth week). These differences are not significantly different across the three

specifications.22

The results also confirm our hypotheses concerning the moderators of advertising

effectiveness. DVD advertising is more effective on movies that have received a better word-of-

mouth from theatrical viewers. Our estimates indicate about a 6% difference in advertising

elasticities between movies on the 90th percentile of WOM and the 10th percentile of WOM,

reflecting a complementary relationship between word-of-mouth and advertising. To our

knowledge, this is the first time that such complementarity between WOM and advertising has

been reported in the literature.

Advertising for the theatrical release negatively affects DVD advertising elasticity,

reflecting substitutability in advertising between the theatrical channel and the home video

channel.

Retail price reductions significantly increase the effectiveness of advertising. As previously

noted, major retailers (such as Wal-Mart and Best Buy) routinely use new DVD releases as loss

leaders to boost store traffic. Our results show that these retailer discounts substantially enhance

advertising responsiveness; specifically, a 1% price discount increases advertising elasticity by

approximately 0.103%. This is a 31% increase in advertising elasticity, relative to the intercept,

suggesting that studios should coordinate with retailers’ loss leader strategies by increasing

advertising intensity.

Box-office revenue has a negative impact on advertising effectiveness. This is consistent

with the informative effect rather than prestige effect of advertising in this market. If a higher

number of potential consumers have viewed the movie in theater, they are more likely to rely on

their viewing experience rather than advertising to make their purchase decision regarding the

DVD. Ackerberg (2001) found that advertising for a new yogurt brand influences inexperienced

consumers and has little effect on experienced consumers. Our finding here similarly suggests

that the role of DVD advertising is to inform consumers about the characteristic of the product;

therefore, a higher percentage of consumers who have experienced the movie renders DVD

advertising less effective.

22 The carryover coefficient, δ , is estimated using a grid search over the minimized sum of squared residuals. It is estimated to be 0.70 in the best-fit specification. This carry over effect is much larger than the average reported in Assmus, Farley and Lehmann (1984), but it should be remembered that we are estimating the models at the weekly duration level, where the carry over effects would be much larger compared to longer monthly or annual duration data.

22

DVD release delay has a positive influence on advertising effectiveness, as expected. This is

consistent with conventional wisdom that a longer delay induces market forgetfulness and

therefore DVD advertising is more effective as reminder advertising when accompanied with

long delays. The market is 25% more elastic to DVD advertising in the Christmas-New Year

holiday season.

The estimates for the correction terms confirm our conjecture that the marketing-mix

variables such as advertising and release timing are endogenously determined and are thus

correlated with the unobserved marginal effects of these variables. The estimates also provide

insights about how these unobserved advertising responsiveness characteristics affect observed

advertising or delay. The coefficient of ˆ Aj ADη is significantly positive, suggesting a positive

relationship between Ajφ , the unobserved component in advertising elasticity for DVD j, and ˆ A

jη ,

the realized error term in the advertising equation. This implies that firms do have private

knowledge about the product-specific advertising effectiveness and take it into account when

setting advertising levels for a particular DVD title: more advertising is given to titles that have

more advertising sensitivity. ˆ Ajt ADη∆ also has a significantly positive coefficient, implying that

the week-specific variation in advertising responsiveness, Ajtφ∆ , is also partially observed by the

studio and incorporated in setting the weekly advertising levels. The coefficient of ˆ Ajη is positive,

suggesting that DVDs with a larger demand disturbance, jε , is given a higher advertising budget,

consistent with the arguments underlying intercept endogeneity. The estimate for ˆ Ajtη∆ is

positive but not significant, possible because weekly deviations in sales are difficult to predict.

The coefficient of ˆLjη is negative, implying that DVDs with an exceptionally high demand

(i.e., larger ε ’s) are released faster, but the estimate is not significant. The coefficient for ˆLj ADη

is significantly negative, suggesting that DVDs that are more responsive to advertising is

released faster on average; on the other hand, ˆ Aj Lη has a positive coefficient, meaning that DVDs

less susceptible to release delay are advertised more. These interaction terms imply that the

endogeneity biases in multiple marketing-mix variables should be treated simultaneously if

possible. The coefficient for ˆLj Lη is insignificant, which may imply that firms are unable to

adequately assess the delay responsiveness for each particular movie well in advance of its

23

planned DVD release; however, they are capable of adapting their advertising levels immediately

around the release date (when market research information becomes available): allocating

smaller advertising budgets to movies whose attractiveness has diminished substantially (i.e., a

more negative Ljφ ) and bigger advertising budgets to movies whose attractiveness has been less

susceptible to the time elapse.

In summary, our findings indicate the following sources of endogeneity: (1) firms seem to

respond to an unexpectedly high DVD demand with higher advertising; (2) firms (at least

partially) observe advertising responsiveness of a specific DVD and tailor their strategies to such

private knowledge; (3) firms tend to make advertising and timing decisions in an integrated

fashion rather than independently. In addition, we find that firms have better private forecasts of

the individual effects of advertising than the effects of release delay.

4.3. Sales Forecasting

Table 7 presents the rest of the second-stage estimation results with the full (slope and

intercept) endogeneity correction. A movie’s box-office performance is usually used as the most

important predictor for its DVD sales. Figure 5 plots the box-office revenue and DVD sales of

each title in our sample in log-log scales. While there is a strong positive correlation between the

two variables (Pearson correlation coefficient = 0.80), there is considerable variance in DVD

sales that is not explained by box-office revenue, especially for in the low-to-medium range. Our

analysis shows that a one percent increase in box-office gross corresponds to a roughly one

percent increase in DVD sales. DVD player adoption has a significantly positive effect on the

sales of an average DVD. Even with the explosion in number of DVD titles, a 1% increase in the

number of DVD households still leads to an increase of 0.85% in the sales of an average DVD.

Theatrical advertising has no significant effect on DVD sales, while its interaction with the

word-of-mouth variable is significantly positive. This finding has important implications for

firms facing a sequential-channel marketing problem. In a parallel context, Erdem and Sun (2002)

show that advertising has a spillover effect for umbrella brands in frequently purchased packaged

goods category; however, to our knowledge, no empirical study has examined whether

advertising spillover effect exists for products marketed in sequential channels. Our finding

suggests that the vertical advertising spillover effect (or advertising trickle-down effect) only

happens when the product has received good-of-mouth in the upstream channel. For a movie

24

with poor word-of-mouth reviews, theatrical advertising has a primarily substitution effect of

channeling consumers from the home video channel to the theatrical channel.

Price elasticity is estimated to be -1.83 for smaller titles and -1.97 for box-office hits. This

result is quite intuitive: smaller titles are typically targeted at a niche market while bigger titles

are marketed to the mass market, which on average is more price-sensitive. The weekly trend

estimate of sales is -0.47, corresponding to a 37% decline in sales per week.23

DVD content enhancements (“extras”) help expand sales. Documentaries about the movie

production (“making-of”) and other behind-the-scene featurettes (MAKING_OF) increase DVD

sales on average by 13%. The presence of deleted scenes or alternative endings (DEL_SCENES)

increases sales by 9%, and music videos or isolated scores (MUSIC_VIDEOS) by 12%.

Filmmaker commentary (COMMENTARY) and interactive features (INTERACTIVE) do not

increase sales. Children’s games (GAME), such as sing-alongs and word games, increase the

sales (mostly of animation movies) by about almost 50%, reflecting the extreme popularity of

such materials with the target audience.

Star power (STAR) increases DVD sales, even after box-office gross and other variables

have been controlled for. Critical rating, somewhat surprisingly, has a negative effect, implying

that the majority of DVD buyers may have different tastes about movies than film critics. A

similar argument may explain the negative sign of OSCARS. R-rated DVDs seem to sell

significantly better than DVDs of other ratings (G, PG, and PG13) of similar box-office revenues.

Previous studies have found that family-oriented movies perform better in theaters while R-rated

movies, which target a more mature audience, tend to perform worse at the box office (Litman

1983; Ravid and Basuroy 2004; Sorensen and Waguespack 2005). For instance, out of the top 20

grossing theatrical films in 2003, only four of them are R-rated. In comparison, the finding that

R-rated movies tend to fare better in the DVD market relative to the theatrical market may reflect

the fact that the DVD market seems to appeal to a more mature audience. This may also be

partially due to fact that teens have freer access to R-rated DVDs than R-rated movies.

According to a FTC 2003 undercover study, the teen shoppers’ ages were asked by cashiers only

19% of the time for purchasing R-rated movies on DVD, in comparison to 48% of the time for

R-rated movie admissions.24

23 We also estimate the model with week dummies and the results are quite similar to this exponential decay sales pattern. 24 http://www.ftc.gov/opa/2003/10/shopper.htm.

25

Sequels perform worse than non-sequels in the DVD market, indicating that the exceptional

popularity of sequels in the theatrical market does not translate into success in the DVD market.

Among different genres, action, fantasy and war movies perform significantly better on DVDs,

whereas drama and romance DVDs perform significantly worse.

Competition from other DVDs newly released (COMP_DVD) negatively affect (statistically

significant) the DVD sales, but the magnitude of the effect is very small (about 0.02% for an

addition 1% increase in the competition measure), indicating that competition between DVDs

should not be a major concern for studios. A consumer may visit a store to buy the Bringing

Down the House DVD, and end up checking it out together with What a Girl Wants, another new

release title on the shelf. The finding supports the viewpoint of some industry observers that the

DVD market supports more “biodiversity” than the theatrical market (Cellini and Lambertini

2003) because DVDs allow households to inventory and watch multiple DVDs at convenient

times over a week. Hence the extent of substitution among DVDs is limited.

The major competition for a DVD, interestingly, is not what is also released in video stores

but what is playing in theaters. The theatrical releases (COMP_THEATRICAL) turn out to have

a substantial impact on DVD sales (i.e., 1% change in theatrical competition leads to 0.2%

decrease in DVD sales), reflecting consumers’ substitution between theater-going and DVD-

buying. This has important implications for DVD distributors, who should want to avoid

releasing their DVDs at the same week with big-budget box-office openings.

In terms of seasonality, the fall season has significantly lower DVD demand, while the

holiday season has higher demand for an average DVD, presumably due to the fact that

consumers are fond of giving away DVDs as gifts. The high number of releases clustered in the

holiday season may partially lessen this effect, but our finding suggests that DVDs still have

better sales in the high-demand period.

5. Conclusion

This paper introduces the advertising responsiveness forecasting problem to the marketing

literature. The problem is of particular interest in the context of short life-cycle products, because

traditional experimentation-based approaches to infer advertising responsiveness are ineffective

for them. The marketing-mix responsiveness forecasting problem is also of broader interest in

the context of sales forecasting. While there has been considerable research on sales forecasting

for new products, our analysis shows that developing better forecasts of marketing-mix

26

responsiveness can aid not only in the choice of the optimal marketing mix but also improve the

accuracy of sales forecasts for new products.

In solving the advertising responsiveness forecasting problem, we also solve the

methodological problem of “slope endogeneity”. We contrast this with the extant literature that

has focused on “intercept endogeneity.” Our solution is a simple and intuitive control-function

approach that is easy to implement. Effectively, this approach involves introducing additional

variables from the errors of a first-stage OLS regression into a second-stage OLS regression.

We hope the simplicity of this approach would aid in the use of such endogeneity correction

in the marketing literature when it is appropriate. In our application, we indeed find that studios

seem to possess private information (unavailable to the researcher) about sales and advertising

responsiveness of a given DVD and that failure to correct for the resulting endogeneity bias leads

to considerable forecasting error for advertising elasticity (an underestimate of 20%). We also

found that extant approaches that only correct for intercept endogeneity did little to remove the

bias in forecasting advertising responsiveness. Therefore, correcting for advertising

responsiveness endogeneity is critical in helping studio executives choose the right advertising

levels.

Our average estimate of advertising elasticity for DVDs is consistent with the meta-analysis

estimates of Assmus, Farley and Lehmann (1984), who find that the average short-run elasticity

for a wide range of products is roughly 0.3. In a sense, our study can be thought of as a meta-

analysis of the advertising responsiveness for different DVDs. A recent meta-analysis of price

sensitivity (Bijmolt et al. 2005) has shown that not accounting for price endogeneity under-

estimates price elasticity by about 34% (from -3.74 to -2.47). However, as we discussed earlier,

these corrections have only taken into account intercept endogeneity. Our results suggest that it

would be important to check in future research whether the magnitude of the endogeneity bias

would change if we also accounted for slope endogeneity in price responsiveness.

Our analysis yielded a number of empirical insights of substantive interest to studio

executives in the marketing of DVDs. We list a few key managerial takeaways based upon our

empirical findings:

(1) DVD advertising is more effective when the corresponding movie has better word-of-

mouth among consumers. Therefore, contrary to the extant theoretical literature which assumes

that advertising and word-of-mouth are substitutes, we argue that DVD advertising should

27

complement movie word-of-mouth. Thus studios will not find advertising an effective tool in

obtaining high DVD sales if the theatrical movie elicited poor consumer ratings. In short, studios

should not put “good money” after “bad money”.

(2) We find that advertising responsiveness is lower for large box-office movies, implying

that DVD advertising should be a concave function of box office revenues, controlling for other

effects. But since DVD advertising is more effective in high demand periods such as holidays,

greater levels of advertising should be used to promote DVDs released during high demand

holiday seasons.

(3) Spillover effects of advertising have been previously documented in the context of

umbrella branding. We find partial evidence for the advertising spillover effect in the context of

a product released in sequential channels: advertising for the upstream market (theatrical release)

benefits the downstream market (DVD release) only when the product has received good word-

of-mouth in the upstream market. We also find that advertising for the theatrical release serves as

a partial substitute for advertising for the DVD release for a movie with average word-of-mouth,

implying that, controlling for other effects, studios should spend less on DVD advertising if the

theatrical movie has been intensely advertised.

(4) Retailers often use popular DVDs as retail loss-leaders. We find that advertising

responsiveness is greater when accompanied by retailer price cuts, leading to greater sales. Since

DVD advertising tends to be non-price advertising, we obtain the well-known interaction effect

between price and non-price advertising on sales (Kaul and Wittink 1995). Hence studios should

leverage and piggy back on retailer pricing and advertise more if they expect retailers to use the

DVD as a loss-leader. More importantly, this suggests that studios may coordinate with major

retailers such as Wal-Mart and Best Buy on pricing and advertising along the lines of consumer

packaged goods manufacturers.

(5) Competition for a DVD movie is primarily not from other DVDs released at the same

time, but from other movies shown in theaters at the same time. Studios should therefore avoid

releasing a DVD head-to-head with major box-office hits.

Our empirical analysis of forecasting advertising responsiveness and sales of DVDs takes

advantage of the sequential nature of box-office and DVD releases. There are several markets

(e.g., books, music, software versions, video games, product line extensions) where products are

introduced sequentially and would benefit the forecasting approach developed in the paper. For

28

example, when books move from hardcover to paperback, firms have to decide how many copies

to print and how much advertising support to offer. Hardcover sales patterns can offer similar

information such as WOM etc. that we elicited from the movie data. Book publishers can

negotiate with retailers about obtaining potential price discounts in exchange for greater

advertising support for the books.

While we have illustrated how to solve the advertising responsiveness endogeneity problem

in the context of DVDs, the problem of marketing-mix responsiveness endogeneity is widely

relevant. In the context of promotions, firms allocate their consumer and trade promotions based

on what they believe would be most effective in raising sales. Within trade promotions,

managers can use free cases, off-invoices, bill-backs etc. as appropriate in these settings.

Consumer promotions can take forms such as targeted coupons and general price discounts. In

sales force allocation, more salespeople (or more capable salespeople) will be allocated to

markets where managers believe they will obtain greater “bang for the buck.” In allocating shelf

space to various categories in a store, managers will allocate greater shelf space to categories that

are likely to have greater marginal profits; across different stores, space allocations will vary

depending on similar managerial judgment about effectiveness. In general, many marketing

problems involve the “heterogeneity of rewards” and private managerial information; to estimate

unbiased marginal effects in each of these problems, one needs to account for responsiveness (i.e.

slope) endogeneity.

In summary, the marketing-mix responsiveness “forecasting” problem and the

“responsiveness” (slope) endogeneity problem, often tangled in real-world applications, are both

relevant in a wide range of markets. We hope this paper serves as an impetus to address these

two classes of problems in future research.

29

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33

Tables

Table 1: Key Descriptive Statisticsa

Variable Mean Median Std Dev. Maximum MinimumDVD sales, 4 weeks (mils.) 0.72 0.32 1.20 8.97 0.01DVD sales, 6 months (mils.) 0.99 0.50 1.50 11.29 0.01TV GRPs, 2 weeks before street date 7.80 0 37.71 567 0TV GRPs, 1 week before street date 75.18 0 127.66 584 0TV GRPs, Week 1 117.93 41 156.87 690 0TV GRPs, Week 2 46.18 0 94.23 612 0TV GRPs, Week 3 10.75 0 42.56 461 0TV GRPs, Week 4 7.26 0 33.75 522 0Total TV GRPs 265.90 85 400.80 2484 0Theatrical-to-Video Window (Days) 165.37 158.00 41.44 405 88DVD Retail Price ($) 19.84 19.60 1.89 33.98 14.16Box-Office Revenue ($ mils.) 55.05 34.56 58.20 404.76 5.11Production Budget ($ mils.) 41.46 35.00 31.01 200 0.16Theatrical TV Advertising GRPs (000) 1.43 1.34 0.81 5.44 0Star Power Rating (0-100)b 56.52 59.09 27.63 100 0Viewer Rating (0-10)c 6.07 6.10 1.14 8.9 2.4Critical Rating (1-10)d 5.42 5.00 2.14 10 1Oscar Nominationse 0.21 0.00 0.75 6 0 a Sample consists of 526 new DVD titles released between 2000/1-2003/10. b From Hollywood Reporter. c Avergate user rating from the Internet Movie Database (www.imdb.com) d From Metacritic.com. e Only include major categories: Best Picture, Best Director, Best Leading Actor, and Best Leading Actress.

34

Table 2: Categorical Variables

Variable Mean Genres Action 0.23 Adventure 0.13 Animation 0.06 Comedy 0.44 Crime 0.15 Documentary 0.01 Drama 0.42 Fantasy 0.06 Horror 0.10 Music/Musical 0.02 Romance 0.17 Sci-Fi 0.10 Thriller 0.27 War 0.03 MPAA Ratings R 0.43 PG 13 0.41 PG 0.12 G 0.04 DVD extras “Making-of”/Behind-the-Scene 0.69 Filmmaker Commentary 0.74 Deleted Scenes 0.52 Music Video/Isolated Score 0.32 Interactive Features 0.13 Children’s Games 0.03 Sequel Sequel 0.10

35

Table 3: Predictors of advertising responsiveness

Variable Predicted sign Hypothesis for advertising elasticity WOM + Ads are more effective for movies with better word-of-mouth.

MOVIE_AD - Theatrical advertising and DVD advertising may be substitutable. BOX_REV - Ads are less effective if box-office sales is greater.

PRICE - Ads are more effective when combined with lower prices. DELAY - Ads are less effective when DVD release is delayed.

HOLIDAY + Ad response is higher during high-demand holiday seasons. TREND - Ad response is highest upon DVD release and diminishes over time.

Table 4: DVD Market Shares (2003)

Studio Total sales (billions)

Market share

Warner $4.21 20.2% Buena Vista $3.38 16.2%

Universal $3.07 14.7% Fox $2.76 13.2%

Sony $2.63 12.6% Paramount $1.96 9.4%

MGM $1.11 5.3% Lions Gate $0.84 4.0%

Others $0.93 4.5%

C4 64.30% C6 86.30%

Source: Video Business (2004)

36

Table 5: Determinants of Endogenous Variables ln(AD) ln(DELAY) Constant 9.29 ** (1.720) 0.54 ** (0.889) log(BOX_REV) 0.70 ** (0.160) 0.05 (0.048) log(DVD_BASE) 0.49 ** (0.065) 0.16 (0.208) log(MOVIE_AD) -0.24 ** (0.111) -0.02 (0.047) WOM -0.04 (0.063) 0.03 (0.028) log(MOVIE_AD)*WOM 0.20 ** (0.055) -0.05 ** (0.022) STAR 0.01 (0.039) -0.01 (0.015) CRITIC 0.02 (0.111) 0.03 (0.042) R -0.61 ** (0.139) 0.02 (0.053) PG13 -0.66 ** (0.130) 0.06 (0.049) SEQUEL 0.01 (0.132) 0.02 (0.050) OSCARS 0.01 (0.057) 0.09 ** (0.024) MAKING_OF 0.15 * (0.085) 0.06 * (0.032) ACTION -0.23 ** (0.097) -0.10 ** (0.038) ANIMATION 0.90 ** (0.235) 0.03 (0.090) DOCUMENTARY -0.51 (0.443) 0.59 ** (0.210) DRAMA -0.08 (0.084) 0.04 (0.032) FANTASY 0.06 (0.154) 0.04 (0.057) HORROR -0.27 (0.138) 0.00 (0.053) ROMANCE -0.23 ** (0.108) 0.04 (0.042) SCI-FI -0.37 ** (0.131) 0.04 (0.049) THRILLER 0.03 (0.094) 0.01 (0.036) WAR 0.42 * (0.225) 0.03 (0.087) log(PRICE) -2.54 ** (0.445) -0.12 ** (0.053) log(PRICE)*BOX_HIT -0.20 * (0.122) -0.09 (0.057) STUDIO 1a 0.03 (0.160) -0.27 ** (0.093) STUDIO 2 0.33 ** (0.150) -0.05 (0.111) STUDIO 3 0.94 ** (0.171) 0.22 ** (0.081) STUDIO 4 0.90 ** (0.188) 0.09 (0.112) STUDIO 5 0.38 ** (0.155) 0.01 (0.084) STUDIO 6 -0.32 * (0.181) 0.14 (0.104) STUDIO 7 1.15 ** (0.202) -0.11 (0.113) WEEK 1b 0.64 ** (0.092) WEEK 2 -0.42 ** (0.092) WEEK 3 -1.51 ** (0.092) WEEK 4 -1.73 ** (0.092) Seasonal dummies Yes Yes Interaction terms Yes Yes R2 0.50 0.54 * p<.1; ** p<.05. Heteroscedasticity-robust standard errors are in parentheses. a Studio identities are disguised. b The base level is pre-street-week advertising level.

37

Table 6: Estimated elasticities and endogeneity correlations

Intercept and Slope Endogeneity Correction

Intercept Endogeneity Correction

No Endogeneity Correction

Advertising elasticity

Constanta 0.330 ** (0.075)c 0.265 ** (0.072) 0.263 ** (0.073)

WEEK 2b -0.004 (0.006) 0.003 (0.006) -0.005 (0.006)

WEKK 3 -0.011 (0.008) -0.001 (0.007) -0.014 ** (0.006)

WEEK 4 -0.025 ** (0.009) -0.015 * (0.008) -0.027 ** (0.007)

WOM 0.008 ** (0.003) 0.008 ** (0.003) 0.007 ** (0.003)

log(MOVIE_AD) -0.022 ** (0.007) -0.020 ** (0.006) -0.022 ** (0.006)

log(PRICE) -0.103 ** (0.025) -0.085 ** (0.025) -0.076 ** (0.025)

log(BOX_REV) -0.008 * (0.004) -0.009 * (0.004) -0.007 * (0.004)

log(DELAY) 0.015 * (0.009) 0.016 * (0.009) 0.017 ** (0.009)

CHRISTMAS 0.082 ** (0.023) 0.083 ** (0.020) 0.083 ** (0.021)

Release delay elasticity

log(DELAY) -0.078 * (-0.046) -0.014 (0.010) -0.083 ** (0.027)

Selectivity correction terms ˆ A

j ADη 0.010 ** (0.003) ˆ A

jt ADη∆ 0.005 ** (0.002)

ˆLj ADη -0.035 ** (0.013)

ˆLj Lη 0.004 (0.061)

ˆ Aj Lη 0.127 ** (0.053)

ˆ Ajη 0.050 ** (0.019) 0.076 ** (0.012)

ˆ Ajtη∆ 0.015 (0.012) 0.053 ** (0.008)

ˆLjη -0.087 (0.062) -0.090 (0.062)

* p<0.1; ** p<0.05. a This coefficient indicates the average advertising elasticity in the release week. b The three week dummies indicate the change in advertising elasticity relative to the release week. c Heteroscedasticity-robust standard errors are in parentheses.

38

Table 7: Estimates for the Sales Equation General sales predictors Constant 8.358 ** (0.483) log(BOX_REV) 1.045 ** (0.032) log(DVD_BASE) 0.853 ** (0.022) log(MOVIE_AD) -0.007 (0.030) WOM 0.073 ** (0.015) log(MOVIE_AD)*WOM 0.061 ** (0.016) log(PRICE) -1.828 ** (0.127) log(PRICE)*BOX_HIT -0.149 ** (0.033) TREND -0.472 ** (0.011) DVD content enhancements MAKING_OF 0.130 ** (0.023) COMMENTARY 0.026 (0.024) DEL_SCENES 0.089 ** (0.021) MUSIC_VIDEO 0.112 ** (0.021) INTERACTIVE -0.008 (0.033) GAME 0.399 ** (0.088) Movie attributes STAR 0.075 ** (0.010) CRITIC -0.070 ** (0.030) R 0.246 ** (0.041) PG13 0.019 (0.039) Sequel -0.152 ** (0.036) OSCARS -0.081 ** (0.015) ACTION 0.281 ** (0.027) ANIMATION 0.126 * (0.066) DOCUMENTARY 0.157 (0.106) DRAMA -0.055 ** (0.023) FANTASY 0.202 ** (0.041) HORROR 0.143 ** (0.037) ROMANCE -0.169 ** (0.030) SCI-FI 0.087 ** (0.035) THRILLER 0.103 ** (0.025) WAR 0.257 ** (0.061) Environmental and seasonality factors COMP_DVD -0.016 * (0.009) COMP_THEATRICAL -0.203 ** (0.050) SPRING -0.008 (0.037) SUMMER -0.027 (0.032) FALL -0.341 ** (0.036) HOLIDAY 0.080 * (0.042) * p<0.1; **p<0.05. Heteroscedasticity-robust standard errors are in parentheses.

39

Figures

Figure 1

Box-Office Revenue: "The In-Laws" (2003)

0

3

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Figure 2

DVD Sales: "The In-Laws" (2003)

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160

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Figure 3

DVD Advertising Intensity by Week

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week 2 week 3 week 4

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Figure 4

Diffusion of the DVD hardware in the U.S.

010203040506070

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41

Figure 5

1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.52

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DVD vs. theatrical sales