Abstract
In this paper we use Nielsen scanner panel data on four categories of consumer goods to examine how TV advertising and other marketing activities affect the demand curve facing a brand. Advertising can affect consumer demand in many different ways. Becker and Murphy (Quarterly Journal of Economics 108:941–964, 1993) have argued that the “presumptive case” should be that advertising works by raising marginal consumers’ willingness to pay for a brand. This has the effect of flattening the demand curve, thus increasing the equilibrium price elasticity of demand and the lowering the equilibrium price. Thus, “advertising is profitable not because it lowers the elasticity of demand for the advertised good, but because it raises the level of demand.” Our empirical results support this conjecture on how advertising shifts the demand curve for 17 of the 18 brands we examine. There have been many prior studies of how advertising affects two equilibrium quantities: the price elasticity of demand and/or the price level. Our work is differentiated from previous work primarily by our focus on how advertising shifts demand curves as a whole. As Becker and Murphy pointed out, a focus on equilibrium prices or elasticities alone can be quite misleading. Indeed, in many instances, the observation that advertising causes prices to fall and/or demand elasticities to increase, has misled authors into concluding that consumer “price sensitivity” must have increased, meaning the number of consumers’ willing to pay any particular price for a brand was reduced—perhaps because advertising makes consumers more aware of substitutes. But, in fact, a decrease in the equilibrium price is perfectly consistent with a scenario where advertising actually raises each individual consumer’s willingness to pay for a brand. Thus, we argue that to understand how advertising affects consumer price sensitivity one needs to estimate how it shifts the whole distribution of willingness to pay in the population. This means estimating how it shifts the shape of the demand curve as a whole, which in turn means estimating a complete demand system for all brands in a category—as we do here. We estimate demand systems for toothpaste, toothbrushes, detergent and ketchup. Across these categories, we find one important exception to conjecture that advertising should primarily increase the willingness to pay of marginal consumers. The exception is the case of Heinz ketchup. Heinz advertising has a greater positive effect on the WTP of infra-marginal consumers. This is not surprising, because Heinz advertising focuses on differentiating the brand on the “thickness” dimension. This is a horizontal dimension that may be highly valued by some consumers and not others. The consumers who most value this dimension have the highest WTP for Heinz, and, by focusing on this dimension; Heinz advertising raises the WTP of these infra-marginal consumers further. In such a case, advertising is profitable because it reduces the market share loss that the brand would suffer from any given price increase. In contrast, in the other categories we examine, advertising tends to focus more on vertical attributes.
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Notes
Nelson (1970) argued that most advertising contains no solid content that can be interpreted as signaling quality directly. He therefore argued that firms’ advertising expenditures could best be rationalized if the volume of advertising, rather than its content, signals brand quality in experience goods markets. This view has been challenged by Erdem and Keane (1996), Anand and Shachar (2002) and Ackerberg (2001). They argue there is compelling evidence that advertising does contain substantial information content. Abernethy and Franke (1996) have systematically analyzed TV ads, and concluded that more than 84% contain at least one information cue. Thus, it is an empirical question whether advertising signals quality primarily through content or volume.
A very fundamental issue is at stake in this debate. If we view advertising as a complement that raises a consumer’s WTP for the advertised good, then conventional welfare analysis using areas under demand curves remains valid, while in the information view it does not. The problem is that, if advertising conveys information about substitutes, then it may reduce WTP for a good without altering the utility a consumer receives from consuming the good.
Current sales may affect future demand if there is habit formation, or if consumers are uncertain about brand attributes and use experience reduces that uncertainty (see Erdem and Keane (1996)). In a simple two period model where current sales affect next period demand, the Lerner condition is modified to: \( P_{1} = \eta {\left( {\eta - 1} \right)}^{{ - 1}} {\left[ {{\text{mc}} - {\left( {1 + r} \right)}^{{ - 1}} {\partial \pi _{2} } \mathord{\left/ {\vphantom {{\partial \pi _{2} } {\partial Q_{1} }}} \right. \kern-\nulldelimiterspace} {\partial Q_{1} }} \right]} \) where π 2 denotes second period profits.
For instance, Wittink (1977) found that price elasticity of demand for a single brand was higher in territories in which advertising intensity was higher. Vanhonacker (1989), looking at two brands in the food category, found that increased ad intensity increased the price elasticity of demand at lower levels of intensity, and reduced it at higher levels. Telser (1964) did not find a positive correlation between concentration and advertising.
The fall in price does reveal something about welfare. Becker and Murphy (1993) show, in a model with fixed preferences where advertising is a compliment with the good advertised, that if advertising lowers the equilibrium price then it increases welfare. Such a welfare comparison is not possible in a model where advertising shifts tastes.
Alternatively, if advertising conveys information about available brands and their prices, making consumers more selective, it might reduce a (the maximum price that anyone is willing to pay for a brand) and also b (since the rate at which consumers are attracted to a brand as its price falls increases with more complete information). In this case η is increased. But a reduction in a holding b constant would have the same effect on η. And this is also a plausible scenario for what might happen if advertising is permitted in a market where it had been banned. A reduction in a holding b fixed would, of course, reduce profits. If advertising has this effect, it would explain why various industry and professional groups have supported advertising bans (see Bond et al. 1980; or Schroeter et al. 1987).
A similar problem may arise if the price coefficient is restricted to be equal across brands. Then a price/advertising interaction term may appear significant, simply because it captures the association that brands with less price sensitive demand advertise more. The bias here is again towards finding that advertising reduces price sensitivity.
Similarly, Eskin and Baron (1977) look at four field experiments in which new products were introduced in a set of test markets accompanied by different levels of (non-price) advertising. Price also varied across stores within each test market. They find that higher ad intensity in a market is usually associated with greater price sensitivity.
Including price net of redeemed coupon value in a brand choice model is equivalent to using (P ijt + d ijt C ijt ) as the price variable, where P ijt is the posted price, d ijt is a dummy for whether brand j was purchased, and C ijt denotes the coupon value that household i had available for purchase of brand j. Thus, one includes a function of the brand choice dummy as a covariate in an equation to predict brand choice! Erdem et al. (1999) provide an extensive analysis of how this procedure can lead to severe upward bias in estimates of the price elasticity of demand.
Note that the set of households who prefer brand j is given by those with taste parameters in the set: \( S = {\left\{ {{\left( {\alpha _{i} ,\varepsilon _{i} } \right)}\left| {\alpha _{i} + \gamma A + \varepsilon _{i} > \overline{U} } \right. - {\left( {\beta + \lambda A} \right)}P} \right\}} \). If λ > 0, then –(β + λA)>0 is decreasing in A. Let μ(S) denote the measure of set S. The rate at which Q = μ(S) decreases as P increases is decreasing in A. So dQ/dP is decreased if A is increased, tending to reduce η.
An awkward aspect of assuming the price coefficient is normally distributed is the implication that some households are insensitive to price. But this is a problem we share with the bulk of the literature on random coefficients demand models in marketing and industrial organization. The typical response is to reject models where the set of price insensitive households implied by the estimates is more than a small fraction. It should be noted however, that these are reduced form models, and it is not unreasonable to expect that some fraction of households really are indifferent to prices of low priced items like ketchup within the range of prices observed in the data.
Of course, predictable changes in tastes over time may arise due to seasonal factors and holidays. We can deal with this simply by including seasonal/holiday dummies in Eq. 6. Our results were not affected by adding such controls.
Assuming we reverse the signs of all correlations with the price coefficient, since for price a larger negative coefficient implies greater sensitivity.
We estimate that the elasticity of demand for toothpaste Brand 1 with respect to the prior use experience stock (i.e., the “loyalty” variable) is 0.61. Note that this elasticity must be less than 1.0 for stability of the model.
We estimate that the elasticities of demand for toothbrush Brand 2, Tide detergent, and Hunts ketchup with respect to the prior use experience stock (i.e., the “loyalty” variable) are 0.60, 0.54 and 0.48, respectively.
The dynamic simulation in the bottom panel of Fig. 4b implies that the LR effect of a 20% increase in Heinz advertising is to increase demand for Heinz by 22.3%, of which 8.9% is a direct effect of the higher long run ad stock, and the remaining 13.4% is an indirect effect due to higher values of the use experience stock. We estimate that the elasticity of demand for Heinz ketchup with respect to the prior use experience stock is 0.57.
Say we have two brands. A consumer buys brand 1 if \( \overline{V} _{1} + \varepsilon _{1} > \overline{V} _{2} + \varepsilon _{2} \), where \( \overline{V} _{j} \) is the deterministic part of the conditional indirect utility function for brand j (determined by price, advertising and other promotional activity), and ɛ j represents consumer tastes. Suppose that \( \overline{{\text{V}}} _{1} > > \overline{V} _{2} \), so brand 1 has a substantial market share. Then, the critical value of ɛ 2 − ɛ 1 such that a consumer would buy brand 2 is well out in the right tail of the distribution of ɛ 2 − ɛ 1. As long as the density of ɛ 2 − ɛ 1 declines sufficiently quickly as one moves further out into the tail, an increase in advertising for brand 1 that raises \( \overline{V} _{1} \) and shifts the cutoff point further right will reduce the derivative of market share with respect to \( \overline{V} _{1} \). This reduces the demand elasticity, provided the derivative falls more rapidly than P/Q increases.
A reverse pattern holds for low market share brands. An increase in advertising that raises market share of such a brand brings the cutoff point for buying that brand up into the “fat” part of the taste heterogeneity density. This tends to raise the derivative of demand with respect to price. This is one factor driving up the price elasticity of demand.
The “brand equity” literature in marketing asserts that emotional or self-expressive benefits (intangible, “soft” benefits) are more difficult to copy than functional benefits; and that positioning and communications strategies focusing on non-functional benefits create more differentiation (see Aaker 1991).
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This research was supported by NSF grants SBR-9812067 and SBR-9511280.
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Erdem, T., Keane, M.P. & Sun, B. The impact of advertising on consumer price sensitivity in experience goods markets. Quant Market Econ 6, 139–176 (2008). https://doi.org/10.1007/s11129-007-9020-x
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DOI: https://doi.org/10.1007/s11129-007-9020-x