Advertising is a powerful form of marketing communication used to "encourage, persuade, or even manipulate an audience to take or continue to take some action". The final desired result is to drive consumer behavior with respect to a commercial offering, although political and ideological advertising is also common. The marketing mix has been the key concept to advertising. The marketing mix refers to four variables (the 4 P's) that a marketing manager can control in order to influence
a brand’s sales or market share. The basic question that managers face nowadays is,
what
level or combination of these variables
maximizes sales, market share, or profit? Or, otherwise, "How do sales or market
share respond to past levels of or expenditures
on these variables"?
Researchers have
developed
a variety of econometric models of
market response to the marketing mix, most of these which have focused on market response to advertising or pricing (Sethuraman, 1991).
The reason may be that expenditures on these variables seem the most discretionary, so marketing managers are most concerned about
how they manage these variables. The basic philosophy underlying the approach
of response modeling is that past data on consumer and market response to the marketing mix contain valuable information that can
enlighten our understanding of response.
Seven response models to advertising are have been already patternalized: Current, shape, competitive, carryover, dynamic, content and media effects of advertising. The first four are common across price and the other marketing
variables. The last three
are unique to advertising.
Current and Carryover Effect
The current effect of advertising is the change in sales caused by an exposure of advertising occurring at the same time period
as the exposure. The x-axis here is time, while sales are on the y-axis and the baseline sales are the dashed line. The current effect of advertising is the
spike in sales from the baseline given an exposure of advertising. Decades of research indicate that this effect of advertising is small relative to that of other marketing
variables and quite fragile, as it can be easily drowned out by the noise in the data. It can be captured by the model:
Yt = a + b1 At + b2Pt + b3Rt + b4Qt + et
where Y represents the dependent
variable (e.g. sales), while the other capital letters represent variables of the marketing mix, such as advertising
(A), price (P), sales promotion (R), or quality (Q).
The parameters a and bk are coefficients that the researcher wants to estimate.
bk represents the effect of the independent variables on the dependent variable, where the subscript k
is an index for the independent variables. et are errors that can be approached as "white noise".
The carryover effect of advertising is that portion of its effect that occurs in time periods
following the pulse of advertising. The carryover effect may occur for several reasons, such as delayed exposure to the ad, delayed consumer response, delayed purchase due to consumers’ backup inventory, delayed purchase due to shortage of retail inventory, and purchases from consumers who have heard from those who first saw the ad (word of mouth). The carryover
effect may be as large as or larger than the cur-rent effect. Typically, the carryover effect is of short duration, rather than of long duration. It can be captured by the multiplicative model:
Yt = Exp(a)
At b1 Pt b2 Rt b3 Qt b4 et
with sales (Y), advertising (A), price (P), sales promotion (R), quality (Q) inter correlated. Here, the independent variables have a synergistic effect on the dependent variable. In many advertising situations, the variables could indeed interact to have such an impact. For example,
higher advertising
combined with a price decrease may enhance sales more than the sum of higher advertising or
the price drop occurring alone.
Shape Effect
The shape of the effect refers to the change
in sales in response to increasing intensity of
advertising in
the same time period. The intensity of advertising
could be in the form of exposures per unit time and is also called frequency
or weight. The x-axis now is the intensity of advertising in a period, while the y-axis is the response of sales. It can be captured by the exponential attraction model:
Mi = Exp (Vi )/ÎŁ Exp Vj
where Mi is the market share of the ith brand (measured from 0 to 1), Vj is the marketing effort of the jth brand in the market, ÎŁ stands for summation over the j brands in the market, Exp stands for exponent,
and Vi is the marketing effort of the ith brand. We know that Vi = a + b1 Ai + b2Pi + b3Ri + b4Qi + ei, as aforementioned, thus:
Mi = Exp (Vi )/ÎŁ Exp Vj = Exp (ÎŁbk Xk + e)/ ÎŁj Exp(ÎŁkbk Xik + ej),
where Xk (0 to m) are the m independent variables or elements of the marketing mix, a = b0 and Xi0 =
1. The use of the ratio of exponents in the above equations ensures that market share is an S-shaped function of share of a brand’s marketing effort.
The diagram above shows three typical
shapes: linear, concave and S-shape. Of these three shapes, the S-shape seems the most plausible because it
suggests that at some very low level, advertising might
not be effective at all because it gets
drowned out in the noise. At some high level, it might not increase sales (advertising elasticity-->0) because the market is saturated or consumers are insensitive from the repetitive advertising.
Competitive Effect
Advertising normally takes place in free markets. Whenever one brand advertises a successful innovation or successfully uses a new
advertising form, other brands quickly
imitate it. Competitive advertising tends to increase the noise in the market and thus reduce the effectiveness of any one brand’s advertising. The competitive effect of a target brand’s advertising is its effectiveness relative to that of the other
brands in the market. The simplest method
of capturing advertising response in competition is to measure and model sales and advertising of
the target brand relative to all other brands in the
market with more complex models, mainly based on the aforementioned econometric model of the "Shape Effect". These first four models may also apply to "Price" instead of "Sales" dependent variable.
Dynamic, Contents and Media Effect
Dynamic effects are those effects of advertising that change
with time. Included under this term are carryover effects discussed earlier and wearin, wearout effects. Wearin is the increase in the response of sales
to advertising, from one week to the next of
a campaign,
even though advertising occurs at
the same level each week. Wearout is the decline
in sales response of
sales to advertising from week to week of a campaign, even though advertising occurs at the
same
level each week. Wearout typically occurs
at the end of a campaign
because of consumer indifference.
Content effects are the variation in response
to advertising due to variation in the content or creative cues of the ad. This is the most important source of variation in advertising responsiveness and the focus of the creative talent
in every agency. Thus, the challenge for marketers is to include
measures of the content of advertising when modeling advertising response in real markets.
Media effects are the differences in advertising response due to various media, such as TV, or newspaper, and the programs within them, such as channel
for TV or section or story for newspaper.
The last three effects (dynamic, content, media) may be captured by models similar to the S-shaped response with modeling interactions integrated due to the competitive environment or even competitive distributed lag Models, however in reality their mathematics can be quite complex (Tellis, 2006).
Conclusion
Prudent marketing managers need to take into account how markets have responded to the
marketing mix in the past. Past may predict future with relative certainty but it also contains valuable
lessons that might enlighten the future. The aforementioned econometrics of response modeling describe how a researcher should model
response to the marketing mix and especially to advertising in order to capture and control the most important effects validly. Which may be the "raison d'ĂȘtre" for marketing managers, in the first place.
Wednesday, January 22, 2014
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