The impact of intermediate advertising effects on advertising effectiveness and profitability

58 Pages·2011·0.78 MB·English

The impact of intermediate advertising effects on
advertising effectiveness and profitability
Daniel Klapper ∗
Goethe-University Frankfurt, School of Business and Economics, Department of Marketing
German Zenetti †
Goethe-University Frankfurt, School of Business and Economics, Department of Marketing
Abstract
In this study, we introduce a new approach to measuring the impact of TV advertising on
the demand for consumer goods. We combine three data sources (i.e., store-level scanner
data, advertising spending data and advertising tracking data) to assess the impact of ad-
vertising on demand. We estimate demand by employing a random coefficient logit model
that uses aggregate data, in accordance with Berry, Levinsohn & Pakes (1995). We model
the effect of advertising on consumer utility by applying an individual goodwill production
function that is calibrated based on a firm’s advertising efforts and tracking data. In turn,
the tracking data assess individual responses to various measures of intermediate advertising
effects which are related to consumers’ product experiences, advertising knowledge and ap-
preciation of advertising. These intermediate advertising effects help to improve researchers’
ability to identify the goodwill production function and to estimate the effects of advertising
on sales via goodwill. Our empirical findings show indeed that experience, cognition and
the appreciation of advertising significantly influence goodwill production and advertising
effectiveness. In other words, these factors determine how a company’s advertising strategy
∗Address: Grueneburgplatz 1, 60323 Frankfurt am Main, Germany, telephone: ++49-69-798-34648, e-
mail: [email protected].
†Address: Grueneburgplatz 1, 60323 Frankfurt am Main, Germany, telephone: ++49-69-798-34642, e-
mail: [email protected].
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translates into a stock of goodwill and how this goodwill stock influences sales and profits.
We then assume thatfirms set their advertising budgets strategically according to an Markov
perfect equilibrium and study the influence of exogenous shocks to intermediate advertising
effects on firms’ price and advertising strategies and their profitability in equilibrium.
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1 Introduction
In many markets, the link between advertising expenditure and profitability is difficult
to establish. Advertising strategies develop in a dynamic and competitive setting in which
advertising effects carry over to the future, and advertising campaigns often diverge from a
consumer’s actual moment of purchase. Accounting for advertising efforts is further com-
plicated by the co-occurrence of a brand’s own marketing activities and its competitors’
marketing activities, especially in the form of price and non-price promotions. Addition-
ally, advertising effectiveness largely depends on mental processes (e.g., the cognitive and
emotional effects of advertising), which are heterogeneous across consumers. These mental
processes are also unstable over time because of consumers’ product experiences, wear-in
and wear-out effects and the threat of competitors’ advertising.
Inthispaper,weintroduceamethodologytoaddresstheseissuesandtomeasurethetotal
effect of advertising on firm profitability by simultaneously accounting for the moderating
effects of advertising effectiveness. These moderating effects are linked to the cognitive
and emotional consequences of advertising. Our results show how changes in consumers’
appreciation of an advertisement affect a firm’s pricing and advertising strategies and its
profitability. Additionally, in a state of equilibrium, the changes in appreciation also affect
competitors’ pricing and advertising strategies.
We base our research approach on a structural model in which advertising affects a con-
sumer’s utility via goodwill, and utility, in turn, determines the consumer’s product choice.
OurworkisinspiredbythestructuralworkofDub´e, Hitsch&Manchanda(2005), Doganoglu
& Klapper (2006) and Draganska, Klapper & Villas-Boas (2010), who have shown that good-
willisanappropriatemeasureofthelinkbetweenconsumerutilityandadvertising. Goodwill
depreciates during each period, and advertising provides a means to replenish the depreci-
ated goodwill. In contrast to, for example, Dub´e et al. (2005) the depreciation of goodwill
and the impact of goodwill on utility depend not only on the advertising intensity per se
but also on the cognitive and emotional processes induced by a brand’s advertising and
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a consumer’s experience with the brand. Information about this intermediate advertising
effects come from the advertising tracking data that are combined with advertising spend-
ing and store level scanner data. Our empirical findings show that experience, cognition
and affect significantly influence goodwill production and advertising effectiveness. In other
words, these factors determine how a company’s advertising strategy translates into a stock
of goodwill and how this goodwill stock influences sales and profits. We then assume that
firms set their advertising budgets strategically according to an Markov perfect equilibrium
(MPE) and study the influence of exogenous shocks to intermediate advertising effects on the
firms’ price and advertising strategies and their profitability. Several counterfactual studies
will show the profit implications of exogenous changes in consumers’ product experiences,
advertising knowledge and appreciation of advertising.
A large body of research has focused on the impacts of cognition, emotions and consumer
experiences related to a brand or product on consumer behavior and preferences. However,
to the best of our knowledge, the profit implications of these factors have not been studied
in detail. For example, in a hierarchy of effects model (Lavidge & Steiner 1961), advertising
must inform and persuade consumers before they buy a firm’s products, but scholars have
not yet determined the order in which these effects take place (Barry & Howard 1990). Per-
suasive models (e.g., Petty & Cacioppo 1981a, Petty & Cacioppo 1981b) state that cognition
precedes affect, which, in turn, precedes behavior. In this context, behavior refers to the
conative part of advertising effectiveness, as does product experience. For example, Ehren-
berg (1974) established a trial-reinforcement model in which cognition precedes experience
and experience precedes affect. Other models with alternative orders (e.g., conation-affect-
cognition or conation-cognition-affect) have been introduced in the literature (e.g., Barry
& Howard 1990, Vaughn 1980, Vaughn 1986). Based on an extensive literature review,
Vakratsas & Ambler (1999) conclude that there is no support for a hierarchy of effects; but,
cognitive, emotional and conative effects simultaneously impact and moderate the influence
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of advertising on consumer preferences.1
Given the lack of evidence for a hierarchy of effects model (i.e., whether cognition pre-
cedes affect or the role of experience in the interplay between cognitions and emotions), our
approach uses these three intermediate advertising effects (i.e., experience, cognition and
affect) as the drivers of advertising effectiveness in a hierarchy-free manner.
Our work is also related to that of Srinivasan, Vanhuele & Pauwels (2010). The authors
had access to a nationally representative panel of households that provided information on
aided brand awareness, aided advertising awareness, advertising appreciation, consideration
sets and purchase intentions. The data consisted of monthly averages at the brand level and
wereclassifiedintofourproductcategories. Thesedatawerecomplementedbypurchasedata
and price data extracted from a national panel of households and from information about
firms’productdistributionsandadvertisingefforts. Srinivasanetal.(2010)usedthemonthly
time series to build a 15-equation VARX model at the brand level. The list of dependent
variables included three mindset metrics (i.e., advertising awareness, brand consideration
and brand appreciation). Each dependent variable was related to each lagged dependent
variable and to other exogenous factors to account for seasonal factors. By applying gener-
alized impulse response functions, the researchers could compute the sales impact of their
mindset metrics and relate these metrics to the marketing mix. The researchers found that
mindset metrics impact sales and that these metrics can monitor future declines in sales.
Our approach differs, although we use similar demand shifters. We begin by defining an
economic foundation of demand in which advertising affects consumer decisions through a
goodwill variable. The effect of goodwill on utility and the process by which goodwill is
established depend on intermediate variables, which Srinivasan et al. (2010) called mindset
metrics. Hence, in our model, the intermediate advertising effects influence the impact of
1Theconativedimensionaccountsforintendedoractualbehaviorwithrespecttopreviousexperienceand
canbemeasuredbytheconsumer’spurchaseintentions,recommendationsorproductexperiences(Vakratsas
& Ambler 1999, Shim, Eastlick, Lotz & Warrington 2001). Cognition consists of a person’s cognitive efforts
ormentalactivitiesandistypicallyoperationalizedbyrecall, recognitionorcomprehensionscores(Bettman
& Park 1980, Barry & Howard 1990, Aaker 1991). Affect is typically measured by a person’s appreciation
of and attitude toward an advertisement or image (Keller 1993).
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advertising on utility. We model demand by employing a flexible, random coefficient logit
model for aggregated data, which accounts for the heterogeneity of brand perceptions, price
responses and advertising responses. This model accounts for the possibility that compet-
ing brands will exhibit flexible substitution patterns, and it supports the observation that
consumers may encounter, experience and process advertisements differently.
Ourmodelingapproachcombinesmacroandmicrodata. Macrodataprovideinformation
about a company’s sales, prices, advertising and promotional effort. We extract the micro
data from advertising tracking data, which contain monthly repeated samples of consumers.
These consumers are interviewed regarding their product experiences, advertising recall and
advertising appreciation, among other measures. We use both data sources to investigate
the advertising sales relationship and to identify the effects of exogenous changes in expe-
rience, advertising appreciation, or advertising recall on a firm’s dynamic and competitive
advertising strategies and its profitability.
The remainder of the paper is structured as follows. After describing the data in detail,
we outline the structural model used to study the impact of intermediate advertising effects
on demand. Then, we analyze the estimation results and the impact of exogenous changes in
intermediate advertising effects on a company’s price setting, advertising budget and profits.
2 Data description: ground coffee
We study the influence of intermediate advertising effects (i.e., cognition, affect and ex-
perience) on firms’ advertising effectiveness and dynamic advertising strategies by examining
the ground coffee industry. We use data on the German market from 2000 to 2001. Pro-
ducers of ground coffee typically emphasize the importance of advertising as a means to
build brands and shift consumer preferences. Therefore, these producers invest a signifi-
cant percentage of their revenues in television (TV) advertising. For example, in the year
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2000, the German coffee industry made 4.09 billion euros in revenue at the retail level. The
manufacturers invested approximately 134 million euros in TV advertisements. In terms of
revenue, coffee is the most frequently consumed beverage in Germany, followed by mineral
water and beer. Ground coffee is one of the most frequently advertised products on German
TV (KloseDetering Werbeagentur 2001). In both 2000 and 2001, the average per capita
consumption of raw coffee was 6.7 kilograms, which corresponds to a total consumption
of 548,520 and 549,530 tons of raw coffee in 2000 and 2001, respectively (European Coffee
Federation 2002).
In the next section, we briefly describe our three sources of data: (1) a nationally represen-
tative sample of store-level scanner data, (2) national advertising budgets, and (3) tracking
data that provide information on the intermediate effects of advertising.
2.1 Macro data: sales data and advertising budgets
The market research company MADAKOM (Cologne, Germany) collected data from a
nationally representative sample of retail stores for the years 2000 and 2001. The stores
belong to six major retail chains (i.e., Edeka, Markant, Metro, Rewe, Spar and Tengelmann)
from which consumers purchased products in the demand model. We do not include discount
stores belonging to Aldi and Lidl in our data and analysis because these stores primarily sell
private labels and do not advertise on TV. We treat the coffee from these stores as an outside
option and concentrate on the retailers that predominantly sell national brands. To compare
theretailers’advertisingexpenditurestotheirrevenuesandprofitability, wederivetheirgross
national sales. Based on publicly available information about each retailer’s total revenues,
we can derive the retailers’ gross revenues. Our analysis focuses on the five largest coffee
brands (i.e., Jacobs, Melitta, Dallmayr, Tchibo and Eduscho), which are predominantly sold
in 500-gram packages (i.e., 98% of all units are sold in this size). We analyze thirteen sub-
brands of the five national brands. The store-level scanner data include information about
the stores’ prices and promotional activities. Because the stores’ featured advertising and
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in-store promotions frequently appear together, we combine these two factors into a single
variable called Promotion.
Figure 1: Monthly TV budgets in thousands of euros for the sub-brands.
A market research company collected and provided the monthly gross TV advertising
budgets for the thirteen sub-brands. The advertising budgets represent only the amount of
money spent on broadcasting and do not include the costs of creating the campaigns. Note
that throughout the time of the study, advertising campaigns do not change. Budgets do
not represent the actual amount paid by the manufacturers for TV advertising because the
market research company calculated the budgets based on the catalog prices of advertising
on the TV channels and the times when the TV commercials were broadcast. The media
agenciesthat managethemanufacturers’TV advertisements typically receive largediscounts
from the TV stations. These discounts are passed on to the advertisers. The national brand
manufacturers primarily use TV advertising, which accounts for more than 90% of their
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Brand Quantity Market Promotion Price TV
share budgets
Jacobs (Sb 1) 633.1 1.25 18.68 7.49 1050
Jacobs (Sb 2) 137.4 0.28 17.20 7.55 439
Jacobs (Sb 3) 32.2 0.06 10.52 8.13 484
Melitta (Sb 1) 367.7 0.75 19.06 6.69 1217
Melitta (Sb 2) 112.6 0.24 17.76 6.75 536
Dallmayr (Sb 1) 419.7 0.84 14.71 8.02 1606
Tchibo (Sb 1) 29.5 0.07 2.13 10.76 251
Tchibo (Sb 2) 341.4 0.70 12.47 8.61 251
Tchibo (Sb 3) 54.2 0.12 3.69 9.43 251
Tchibo (Sb 4) 56.9 0.12 3.74 9.42 251
Tchibo (Sb 5) 110.6 0.24 8.28 7.66 251
Tchibo (Sb 6) 69.1 0.15 9.22 8.03 251
Eduscho (Sb 1) 429.8 0.87 16.33 7.19 1300
Table 1: Mean values of sold quantity (in tons per week), market share (in percent per week),
promotion (in percent per week), price (in euros per kilogram per week) and TV budget (in
tsd euros per month) for each sub-brand.
total expenditures for advertising. The fact that monthly expenditures for TV advertising
are reported several months before the store-level scanner data are reported facilitates the
initialization of the goodwill production function. Figure 1 shows the TV budgets for each
sub-brand for the months during which the store-level scanner data are reported. Tchibo
engages in umbrella advertising, and its advertising budget is assigned to each sub-brand in
proportion to the number of sub-brands.
Table 1 provides a descriptive overview of each sub-brand’s market share, prices, promo-
tional efforts and advertising expenditures. The sub-brands tend to differ in their market
shares. The sub-brands with high market shares generally show lower prices, greater pro-
motional support and larger advertising budgets than do the sub-brands with low market
shares.
2.2 Micro data: advertising tracking data
The advertising tracking data were collected by a large market research company, which
conducted monthly, face-to-face interviews with approximately 320 respondents based on
a repeated cross-sectional design. These questionnaires are similar across industries and
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marketing research institutes. The sample is representative of the target group of coffee
consumers. Because the tracking data is measured at the brand level, we cannot differentiate
among the various sub-brands. Thus, we assign identical values to the sub-brands within
each brand. In addition, we match the monthly tracking data to the weekly sales data using
exponential smoothing.
Figure2: Purchasedrecently(PurchRecent), aidedadvertisingrecall(AdRecall)andattitude
toward advertising (Aad) during months.
The tracking data provide information about consumers’ experiences, cognition and af-
fect. We measure experience by analyzing the brands that the respondents have recently
purchased (denoted as PurchRecent). We operationalize the respondents’ cognition by mea-
suring their capacities for aided advertising recall (denoted as AdRecall). In addition to
advertising recall, the tracking data also contain information describing whether each re-
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