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Transcript of Downsizing Price Increases
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Harvard Business School Marketing
Research Papers
No. 04-01
June 2004
Downsizing Price Increases: A Greater
Sensitivity to Price than Quantity in
Consumer Markets
John T. GourvilleHarvard Business School
Jonathan J. Koehler
University of Texas at Austin
This paper can be downloaded without charge from the Social ScienceResearch Network Electronic Paper Collection:
http://ssrn.com/abstract_id=xxxxxx
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DOWNSIZING PRICE INCREASES:
A GREATERSENSITIVITY TO PRICE THAN QUANTITY IN CONSUMERMARKETS
Abstract
As the cost of goods increase, manufacturers routinely pass these costs on to
consumers through higher prices. A less obvious strategy is to maintain price, but to
reduce the size of the product. In many ways, this downsizing should mirror a straight
price increase when it comes to consumer behavior. Marketplace and experimental data
show this is not the case and that consumers are more sensitive to changes in price than to
changes in quantity.
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Consider the following scenario. A packaged foods manufacturer experiences a
sudden and permanent rise in the price of raw materials, increasing its cost of goods sold
and decreasing its margins. The firm decides it must respond. Is the firm better off (1)
increasing the sticker price of its products or (2) maintaining the price, but reducing the
content contained in its offerings?
The answer is not immediately clear. Both a sticker price increase and a
commensurate quantity decrease will result in a higher effective price to consumers. In
the case of the sticker price increase, the consumer is clearly paying more for the goods
obtained. In the case of a quantity decrease, the consumer is paying the same sticker
price, but receiving less for his money. While it is a less obvious form of price increase,
the net effect is quite similar.
Although a simple economic model of rationality predicts that consumers should be
sensitive both to an increase in a products price and a corresponding decrease in a
products quantity, there are reasons to suspect they are not. Consider the case of
PepsiCo, the parent brand for Pepsi soft drinks and Frito-Lay snacks. In announcing its
2001 first quarter earnings, PepsiCo reported its sixth consecutive quarter of double-
digit earnings growth (PepsiCo 2001). The company reported that this continued
growth partly reflected the impact of its recently introduced weight out strategy within
its Frito-Lay division. As captured by The New York Times, Net income grew to $498
million, or 34 cents per share, as the company continued to reap benefits from its weight
out strategy in which it cut costs by putting fewer chips in bags of Lays, Doritos, and
other Frito-Lay products (Winter 2001). This report is interesting for at least two
reasons. First, Frito-Lay increased the per-unit price of its products by reducing the
quantity of chips in each bag rather than by raising the sticker price of those bags.
Second, this tactic was deemed worthy of inclusion in PepsiCos corporate press release
as a key driver in the continued double-digit earnings growth.
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Chock Full o Nuts was one of the first and most visible companies to employ this
tactic back in 1988 when it reduced its one-pound tin of ground coffee to 13 ounces (it is
down to 11.5 ounces in 2004). More recently, Dannon trimmed the amount of yogurt
contained in its single servings from 8 to 6 ounces, Poland Springs reduced the quantity
of water in its large bottles from six gallons to five gallons, and Pampers reduced the
number of diapers contained in a typical package from, say, 44 diapers to 38 diapers.
The net result in each of these cases is the samea per unit price increase. Rather than
increase the price of a product by raising the sticker price, firms increased price through a
content reduction. We refer to this practice as a downsizing price increase or, more
simply, as downsizing.
Presumably, one intent behind downsizing is to reduce or eliminate the negative
impact one might otherwise expect with a straight price increase. Importantly, this result
demands systematically greater consumer sensitivity to changes in price than to changes
in quantity. The purpose of the current research is to assess whether such differential
sensitivity exists.
In Study 1, we use marketplace price and size data to assess whether manufacturers
behave as if consumers are more sensitive to price than to quantity. In Studies 2 and 3,
we employ laboratory experiments to examine the impact of product quantity
manipulations on consumers willingness to purchase and willingness to pay. And in
Study 4, we use marketplace sales data to demonstrate that consumers are highly
sensitive to changes in price but relatively insensitive to changes to quantity.
The remainder of this paper is in three parts. First, we review how consumers might
be expected to respond to changes in price versus quantity. In the process, we develop a
conceptual framework for understanding why consumers might be more sensitive to
changes in price than to changes in quantity. Second, we present four studies that
investigate consumers sensitivity to price versus quantity. Finally, we conclude with a
discussion of the managerial implications of this work and avenues for future research.
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THE PSYCHOLOGY OF RATE-BASED INFORMATION
How should a consumer assess the price (P) and quantity (Q) of a tin of coffee, a bag
of chips, or a box of cereal? More broadly, how should an individual react to any rate
that can be expressed as the ratio of an amount (the numerator N) and a corresponding
unit of measurement (the denominator D)? Consider the following vignettes:
In the 1990s, the U.S. Congress debated whether to continue support for the National
Endowment of the Arts. Proponents of cutting support noted that the current level of
support amounted to $200 million per year. Opponents countered that this amounted to a
mere 86 per person.
Two jurors are asked to consider whether DNA evidence found at the scene of a violent
crime belongs to the suspect. Juror A is told that the suspect matches the evidence and
that 1 in every 100,000 people would also match. Juror B is told that the suspect matches
the evidence and that 0.1 in every 10,000 people would also match. Juror A worries that
the match with the suspect may be coincidental; juror B does not worry about this
possibility at all.
A store manager faces a 50% wholesale price increase in the price of bologna. The retail
price of bologna was $4 per pound. Rather than post the new price as $6 per pound, he
posts the new price as $3 per half-pound because he thinks it sounds less expensive.
In each of these vignettes, while the framing of the rate varied, the actual rate
remained unchanged. Therefore, different reactions within any one of these vignettes
would challenge the normative principle of descriptive invariance (Tversky, Sattath, and
Slovic 1988), which argues that judgment and choice should be invariant across different
presentations of the same stimuli.
Rules of rational choice suggest that the ratio of a rates numerator (the amount) to its
denominator (the unit of measurement), rather than the absolute size of either component
viewed in isolation, provides the normative standard for judgment and choice. In the
above vignettes, whether congressional support was framed as $200 million or as 86 per
person, or whether courtroom data is framed as 1 in 100,000 or as 0.1 in 10,000 should
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not differentially impact an individuals decision making because the corresponding
ratios are identical.
But recent research suggests this is not the case. For instance, Gourville (1998, 2003)
has shown that the very same cost can differentially influence consumers based on
whether that cost is framed as a daily, monthly, or yearly expense. He finds that an
annual donation request for $1 per day is perceived as significantly more reasonable and
will result in far higher compliance than a financially comparable request for $365. To
explain this result, he argues that consumers generate far more palatable comparisons
when faced with a request for $1 per day, such as a cup of coffee, than $365 per year.
The failure, it seems, is in not considering the many days (365) over which that cup of
coffee will need to be donated.
Koehler (2001) and Koehler and Macchi (in press) also have found violations of
rationality in studies of legal decision making. Koehler and Macchi (in press, Study 2)
showed that a mock jurors assessment of guilt or innocence is systematically influenced
by the manner in which numerical evidence is communicated, with a juror significantly
more likely to convict when presented with evidence indicating a 0.1 in 10,000 chance
someone else committed the crime than by evidence indicating a 1 in 100,000 chance.
Again, it appears subjects overweighted the numerator relative to the denominator.
Finally, Raghubir and Srivastava (2002) showed that an individuals valuation of a
product in a foreign currency is biased towards that currencys face value, with
inadequate adjustment for the exchange rate. For instance, if the local currency is a
multiple of a travelers home country, as when 1100 Korean Won equals 1 US$, the
traveler will consider local prices to be high and be reluctant to purchase. In contrast, if
the local currency is a fraction of the home country, as when 0.4 Bahraini Dinars equals 1
US$, the traveler will consider local prices to be low and will be more likely to purchase.
They offered an anchoring and adjustment model to explain this effect, with individuals
anchoring on the face value of a foreign product and insufficiently adjusting for the
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exchange rate. As a result, products priced in Korean Won (e.g., 22,000 Won) seem
more expensive than an accurate conversion ($20) would suggest, whereas products
priced in Bahraini Dinars (8 Dinars) seem less expensive.
By holding the underlying rate constant, this existing research suggests that
consumers and other decision makers may attach too much weight to the numerator of a
rate-based statistic while paying insufficient attention to the corresponding denominator.
When Price/Quantity Does Change
But how do consumers respond to variations in rate-based statistics when the
underlying rate is notheld constant? Consider, for example, the rates used to describe the
price and quantity of competing marketplace goods in a particular product category.
Rational choice economists would say that consumers should consider the price and
quantity of each alternative. In comparing two tins of ground coffee, for instance, one
that costs $3 for 8 ounces and another that costs $4 for 12 ounces, a fully-informed,
rational consumer should take into account both the price difference ($3 vs. $4) and the
weight difference (8 ounces vs. 12 ounces). Similarly, when comparing the price of
competing yogurts, a rational consumer should consider the fact that some yogurts come
in 6-ounce containers and others in 8-ounce containers. Indeed, in the unit-pricing
research by Russo (1977) consumers were highly sensitive to the unit cost of grocery
items when that unit cost was made explicit.
Note that this does not mean consumers should blindly choose the product with the
lower per-unit cost. For instance, if a person needs coffee for only several days, choosing
the 8 ounces of coffee for $3 may make economic sense. However, across a wide range
of consumers, one would expect price versus quantity differences to have some impact on
decision making.
On the other hand, the practice of downsizing challenges the unit cost sensitivity
that Russo detected. In 1988, when Chock Full oNuts decreased its tin of coffee from 16
ounces to 13 ounces rather than increase the price of its 16-ounce tin, the company
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effectively bet that its customers would be more sensitive to a price increase than to a
corresponding quantity decrease.
Conceptually, such differential sensitivity to price over quantity is consistent with an
anchoring and adjustment model (see Raghubir and Srivastava 2002) in which consumers
fully adapt to changes in price, but only partially adapt to changes in quantity. Assume
that a product that sells at price P1 and quantity Q1 is repriced and repackaged to sell at P2
and Q2. Assume further that consumers completely adjust for the change in price from P1
to P2, but onlypartially adjust for the change in quantity from Q1 to Q2. Whereas the old
price/quantity ratio was P1/Q1, and the new price/quantity ratio should be P2/Q2, a model
that permits incomplete adjustment for quantity results in the following perceived ratio,
where reflects the incomplete quantity adjustment (0 1):
P2/(Q1*(1- ) + Q2 *).
When = 1, there is full adjustment for the change in quantity and consumers treat
the new offering as having quantity Q2. When = 0, there is no adjustment for the
change in quantity and consumers treat the new offering as still having quantity Q1. And
when 0 < < 1, there is partial adjustment to the change in quantity and customers treat
the new offering as having a quantity between Q1 and Q2. In this model, in all cases
where < 1, consumers will be more sensitive to changes in price (which they adjust for
completely) than to changes in quantity.
In proposing this model, we do not claim that consumers actually perform such
calculations. Instead, we propose that consumers behave as if they do (i.e., their
behavior can be predicted from this model). The key insight of this framework is that
changes in price may have a significantly greater impact on consumer judgment and
choice than changes in quantity. We now consider several domains in which evidence of
such differential sensitivity might be found.
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Competing on Price vs. Quantity The first place one might look for evidence of
differential sensitivity is in the price and quantity decisions of manufacturers. If
consumers are indeed more sensitive to price than quantity, and if manufacturers are
aware of this differential sensitivity, we would expect firms within a product category to
compete on price rather than quantity. Consider the case of a new cereal, one that costs
50% more to produce than its likely competitor. Is this manufacturer of this cereal better
off offering this cereal in the same size package as its competitor, but at a 50% higher
price or offering the cereal at the same price, but in a package that is 33% smaller. A
differential sensitivity to price over quantity would argue for the latter. More generally,
we would expect to see far greater variance in quantity than in price across alternatives
within that product category. We test this prediction in Study 1.
The Simple Reframing of Price A second place one might find evidence of
differential sensitivity to price over quantity is in a consumers relative preference for
price/quantity ratios with smaller as opposed to larger absolute numbers. Consider, for
example, the merchant who reframed the price of his bologna from $6 per pound to $3
per pound. In this example, P1 = $6 and Q1 = 1 pound, while P2 = $3 and Q2 =
pound. An economically rational consumer would realize that P1/Q1 = P2/Q2 = $6 per
pound and conclude that there is no difference between the two framings. But if
consumers adjust fully for changes in price but only partially adjust to changes in
quantity, the result would be different. If = 0.5, for instance, consumers will perceive
$3 per pound to be cheaper than it really is:
P2/(Q1*(1- ) + Q2*) = $3/((1 lb*0.5) + ( lb*0.5)) = $3 per pound = $4 per pound
At a perceived $4 per pound, consumers would find the $3 per pound bologna
more attractive than the $6 per pound bologna. We test this premise in Study 2 and test
an extension of this premise in Study 3.
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Downsizing Price Increases A third place to look for evidence of differential
sensitivity to price over quantity is in the case of downsizing price increases. If one
assumes that a change in package size entails greater manufacturer costs than a change in
sticker price, downsizing only makes economic sense for a firm if the negative impact on
sales is significantly less pronounced under downsizing than under a straight price
increase. Consider a manufacturer selling ground coffee at $3 (P1) for 16 ounces (Q1).
Faced with an increase in the price of coffee beans, the firm debates whether to increase
the sticker price to $4 or decrease the quantity to 12 ounces, with both actions
representing a 33% per-ounce price increase. Under the first option, P2P = $4 and Q2P =
16 ounces, with the subscript 2P representing the price and quantity under a price
increase. Under the second option, P2D=$3 and Q2D= 12 ounces, with the subscript 2D
representing the price and quantity under downsizing. If, as our framework suggests,
consumers fully adjust to price but only partially adjust to quantity, a firm should always
downsize. For example, if= 0.5, the analysis plays out as follows:
Downsizing Price Increase
P2D/(Q1*(1- ) + Q2D*) P2P/(Q1*(1- ) + Q2P*)
$3/((16oz*0.5) + (12oz.*0.5)) $4/((16oz*0.5) + (16oz.*0.5))
$3 per 14 ounces < $4 per 16 ounces
21 per ounce < 25 per ounce
In this exampleand in any example where < 1downsizing results in a smaller
perceived per-unit price for a product. As a result, the negative impact on sales of
downsizing will likely be less than the negative impact on sales of a price increase. We
test this possibility with evidence from the marketplace in Study 4.
Summary
If consumers are more sensitive to price than quantity, we would expect this
differential sensitivity to manifest itself in the marketplace. We would expect to see (a)
greater variations in quantity than price within a product category, (b) a preference for
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framing prices in smaller (yet still reasonable) quantity units, and (c) product sales data
that support the marketplace effectiveness of downsizing price increases. We test these
hypotheses across four studies.
STUDY 1
If consumers are differentially sensitive to price over quantity, and if manufacturers
are aware of this, ceteris paribus we would expect manufacturers to leverage this
differential sensitivity. Therefore, in product categories where price and quantity vary
freely,1 we would expect manufacturers to optimize on price as opposed to quantity. In
particular, we would expect a simple price versus size decision to go something like, Get
the price right and adjust quantity accordingly. Across products within a manufacturers
product line, this would imply far greater disparity around product sizing than product
pricing.
Description of the Data
To test this proposition, we collected price and size data in one northeast grocery
store in one of the largest and most visible consumer package goods categoriesready to
eat breakfast cereals.2
We chose cereals because there existed, within this store, five
large brands (i.e., Post, General Mills, Kelloggs, Quaker Oats, and the store brand), and
many cereal types (e.g., Corn Flakes, Raisin Bran) within each of those brands, providing
a robust data set to test our price versus quantity prediction. In addition, alternatives
within the ready-to-eat cereals category typically vary on both price and quantity, with no
preset standard for either.
Our sample consists of 157 SKUs, representing all of the single-flavor cereals from
each of the five major brands carried by this store (i.e., we eliminated variety packs). We
1 In the special cases where quantity is highly standardized, such as with a quart of oil or a gallon of milk, consumers
may be quite sensitive to any change in quantity.2 We also analysed the price and size data for ready-to-eat breakfast cereals available from Peapod (www.peapod.com)
and found the same pattern of results reported here, suggesting that these results generalize beyond a single store.
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collected three types of data for each cerealthe non-sale sticker price, the weight (in
ounces), and the number of servings per box.3
See Table 1 for a complete list of cereals
from one of the brands.
---------------------------------------Table 1 about here
---------------------------------------
Results
Panel A in Table 2 provides the mean and standard deviation for price, weight, and
number of servings data for the cereals in each of the five brands. For our purposes, for
each of our three variables, the statistic of interest is the ratio of standard deviation to
mean. In the case of price, for instance, we are interested in the ratio of the standard
deviation of price to the mean of price [i.e., st.dev. (price) mean (price)]. Across the
three variables, higher ratios indicate relatively more variation in the variable than lower
ratios.
With this in mind, the data reveal higher ratios for both weight and number of
servings than for price. As shown in Table 2, for instance, for the 46 SKUs of General
Mills cereals in our sample, the standard deviation and mean for price was $0.45 and
$3.71, respectively, for a ratio of 0.122. In contrast, the standard deviation and mean for
weight was 3.80 and 15.53 ounces, for a ratio of 0.244. And the standard deviation and
mean for number of servings was 3.74 and 12.78, for a ratio of 0.293. The same holds
true for the other four brands. Across the five brands, the ratio of standard deviation to
mean is 1.6 to 3.3 times greater for weight than for price, and 1.4 to 3.9 times greater for
number of servings than for price. Consistent with the notion that manufacturers adjust
quantity to maintain reasonably constant prices, there is a far greater variation in quantity,
measured either by weight or number of servings, than there is in price across the SKUs
offered.
3Because cereals vary in density (e.g., Raisin Bran is far heavier per volume than corn flakes), we measured productquantity both as weight and number of servings. This approach provides a more complete picture of quantity thaneither measure alone.
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---------------------------------------Table 2 about here
---------------------------------------
One potential problem with this analysis is that it includes multiple sizes of the same
cereal. For instance, in the General Mills cereals listed in Table 1, Cheerios appears in
10-ounce, 15-ounce, and 20-ounce packages that cost $2.69, $3.19, and $3.99,
respectively. Therefore, while quantity increased by a factor of 2 (from 10-ounces to 20-
ounces), price increased by a factor of only 1.5 (from $2.69 to $3.99), consistent with a
consumers expectation of receiving a volume discount for larger package sizes. A
similar situation exists for Kix, Total Raisin Bran, Cinnamon Toast Crunch, Honey Nut
Cheerios, Lucky Charms, and Cocoa Puffs. The volume discounts offered for cereals that
are packaged in multiple sizes could account for the greater observed variance in quantity
than in price.
In response to this potential problem, we conducted a second analysis that excluded
all cereals that came in multiple sizes. This reduced the number of SKUs in our sample
from 157 to 109 across the five brands. As shown in Panel B of Table 2, these exclusions
had no substantive effect on our results. For all five brands, the ratios of the standard
deviation to the mean for the two quantity measures (weight and number of servings) are,
once again, higher than the ratio for price. Specifically, the ratios are 1.9 to 3.1 times
greater for weight than for price, and 1.3 to 3.9 times greater for number of servings than
for price.
Discussion
Our analyses of the ready-to-eat cereal category reveal far greater variance in package
size than in package price. Across five major brands, the relationship of standard
deviation to mean for our two measures of package size weight and number of servings
was 28% and 30% respectively. In contrast, this relationship for package price was
only 12%. At least in the ready-to-eat cereal category, there clearly is far greater
variability around package sizing than package pricing. Because changing price
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presumably is easier and cheaper for manufacturers than changing package size,
manufacturers must have good reason for decreasing quantity rather than increasing
price. We suggest this reason is that manufacturers believe consumers are much more
sensitive to relatively large price changes than they are to relatively large quantity
changes.
STUDY 2
Whereas Study 1 looked at the actions of manufacturers and tried to infer their beliefs
about consumer sensitivity to price and quantity, Study 2 investigated the beliefs of
consumers themselves. In this study, we presented subjects with a scenario in which a
local gourmet coffee shop was to begin selling coffee beans in whatever quantity a
consumer desired. Subjects were told that the coffee was of high quality and would,
therefore, be priced substantially higher than that found in grocery stores. Finally,
subjects were told that the store was debating whether to list the price of the coffee as
$12 per pound or as $6 per pound. Subjects were asked which of the two pricing
options they would opt for if they were in charge of the coffee shop, and which of the two
pricing options they thought would be more effective at promoting sales.
Unlike Study 1, where one might argue that a manufacturers decision to vary
quantity more than price could be explained by factors such as operational efficiency,
Study 2 isolates the effect of price and quantity. If subjects believe that consumers and,
by extension, they themselves are less sensitive to quantity than to price, they should opt
to price the coffee at $6 per pound. If subjects believe consumers are equally sensitive
to price and quantity, they should be indifferent between the two pricing options. And if
subjects believe consumers are more sensitive to quantity than price, they should opt to
price the coffee at $12 per pound.
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Subjects, Design, and Procedure
Subjects were 60 adults waiting for flights in the Manchester, NH airport. They were
asked to fill out a one-paged survey and were given a small box of chocolates for their
efforts. Approximately 75% of those approached agreed to fill out the survey.
The survey instructed subjects to read a scenario, to imagine themselves in the
situation described, and to answer the questions that followed. All subjects were told that
there were no right or wrong answers and that we only were interested how they believed
they would act. The scenario presented to all 60 subjects read as follows:4
Coffee Heaven is a gourmet coffee shop in your neighborhood. It has recently decided to
sell bulk coffee to the public. The airtight, re-sealable bags that will be used allows for
anywhere from a few ounces of coffee all the way up to several pounds to be purchased.
Given the high quality of the coffee that Coffee Heaven will sell, the price of its beans
will be substantially higher than that found in grocery stores. In thinking about how to
advertise and list the price for this coffee, two different options are being debated.
The first option is to advertise and post the price as: $6 per pound
The second option is to advertise and post the price as: $12.00 per pound
While the owners of Coffee Heaven realize that the two proposed pricing options are
financially identical, they are wondering whether one of the two might be more effective.
Subjects were then asked to imagine that they were in charge of Coffee Heaven and
to answer the questions that followed. First, they were asked to indicate which of the two
pricing options they would adopt by circling one of the two options. Second, they were
asked to indicate which of the two pricing options they thought would be more effective
at promoting sales, and were provided with a 7-point Likert scale on which to answer.
This scale was anchored by $6.00 per pound will be much more effective at 1 and
$12.00 per pound will be much more effective at 7, with the rating of 4 labeled as
4 In this scenario and in the subsequent questions, the order of the two pricing options was counterbalanced acrosssubjects.
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They will be equally effective. Finally, subjects were asked to explain why they
answered the first two questions the way that they did.
Given the explicitly stated ability to purchase any amount desired, the two pricing
options are normatively identical. Therefore, a systematic preference for one of the two
pricing options or a systematic belief that one pricing option would be more effective
than the other at promoting sales would reflect an expectation that consumers are more
sensitive to either price or to quantity.
Results
Subjects responses to the two key questions pointed toward an expectation that
consumers would be more sensitive to price than to quantity. Regarding the first question
(i.e., Which of the two pricing options would you choose?), 52 of the 60 subjects (86.7%)
circled the $6 per pound option while only 8 subjects circled the $12 per pound
option, a proportion significantly different from chance (2(1) = 908.0, p < 0.001).
Regarding the second question (i.e., Which of the two pricing options will be more
effective at promoting sales?), subjects mean response of 2.85 on the 1 to 7 scale was
significantly different from 4 or equally effective (t59 = 5.45, p < 0.001). As Figure 1
shows, on average, subjects reported that the advertising and posting of price as $6.00
per pound would be significantly more effective at promoting sales than $12.00 per
pound.
---------------------------------------Figure 1 about here
---------------------------------------
To understand the reasoning behind these ratings, it is informative to review subjects
open-ended responses to the third question (i.e., What are your reasons for answering the
way you did in Questions 1 and 2?). First, about one-third of those subjects who favored
the pound pricing suggested that consumers would notice the price, but not the fact that
it was per pound. Comments such as, people have the tendency to look at the dollar
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amount only and most people will overlook that pound part were typical. Second,
another one-third of those who favored the pound pricing suggested that both the dollar
amount and the quantity would register with consumers, but that the price component
would register first and foremost. According to these subjects, this made the $6 per
pound seem less expensive than $12 per pound. Finally, the remaining one-third of those
who favored the pound pricing either offered no explanation or suggested that
consumers might mistakenly believe that they had to purchase a full pound of coffee if
the price was $12 per pound. Interestingly, among the few subjects who favored
posting the price as $12 per pound, almost all argued that pricing by the half-pound
seemed deceptive or manipulative.
Discussion
Overwhelmingly, subjects in this second study believed that pricing coffee at $6 per
pound would be more effective at promoting sales than pricing it at $12 per pound.
This was in spite of the fact that subjects knew that consumers could purchase any
quantity of coffee they desired, rendering the framing of price a purely perceptual issue.
This result is consistent with the results of the first study. Just as cereal manufacturers
appeared to treat consumers as if they were more sensitive to price than quantity, subjects
in this study viewed other consumers as differentially sensitive as well. And as the open-
ended responses of a large portion of the subjects suggest, this differential sensitivity took
one of two formseither a complete disregard of quantity (e.g., people have the
tendency to look at the dollar amount only) or a relative overweighting of price relative
to quantity (e.g., $6 would register first and then the pound quantity). Either of
these explanations is consistent with our central tenet that consumers are more sensitive
to price than quantity in consumer markets.
Both Study 1 and Study 2 were concerned with how people thinkconsumers might
behave rather than with how consumers actually do behave. It is entirely possible that the
manufacturers in Study 1 and the subjects in Study 2 systematically mispredicted
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consumer response. Thus, Studies 3 and 4 were intended to capture consumers own
predicted and actual behaviors.
STUDY 3
To more directly assess individuals relative sensitivity to price and quantity, Study 3
presented subjects with a hypothetical donation task. Subjects were told that a friend was
participating in a charity walkathon and was asking for pledges. Subjects either were told
that the walkathon was 10 miles or 25 miles. Also, subjects either were asked to provide
an overall pledge or a per mile pledge.
If consumers are systematically more sensitive to price than to the quantity over
which that price is allocated, we would expect two results. First, in the overall pledge
condition, we would expect subjects to be relatively insensitive to the number of miles
walked, resulting in similar total pledges across the 10-mile and 25-mile conditions.
Second, in the per mile pledge conditions, we also would expect subjects to be relative
insensitive to the number of miles walked. But, whereas this insensitivity should result in
similar per mile pledges, it also should translate into much larger total pledges in the
25-mile condition than in the 10-mile condition.
Subjects, Design, and Procedure
Subjects were 66 students at a large, Midwest university. They were recruited via
posters on campus offering $5 for a series of short studies lasting approximately 30
minutes. The present study was one of three unrelated studies presented to the students.
Subjects read a simple scenario entitled Charity Walk and answered the question that
followed. The survey indicated that there were no right or wrong answers and that
subjects should answer as if presented with the situation in real life.
The charity walk scenario was manipulated in a 2 (donation frame) x 2 (distance)
between-subjects design. Half the subjects were asked to make an overall pledge for
the walkathon and half were asked to make a per mile pledge. In addition, in a crossed-
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design, half the subjects were told that the marathon lasted 10 miles and half were told
that it lasted 25 miles. The scenarios read as follows, with each subjects seeing one of
the phrasings in parentheses:
A friend of yours is participating in a [10-mile, 25-mile] charity walkathon. She
is asking you to pledge [some amount of money, some amount of money for
every mile walked]. How much would you pledge?
[I would pledge _______ , I would pledge _______ per mile.]
Subjects were then asked to fill in the blank. In the overall pledge conditions, if
consumers are more sensitive to price than quantity, we would expect little difference in
the amount pledged between the 10-mile and 25-mile conditions. Similarly, in the per-
mile conditions, we would expect little difference in the per-mile pledges between the 10-
mile and 25-mile conditions. However, while insensitivity in the overall pledge
conditions should result in similartotalpledges, insensitivity in the per-mile pledge
conditions should result in significantly highertotalpledges in the 25-mile than 10-mile
condition.5
Therefore, with total pledge as our dependent measure, we would predict a
significant Donation Frame by Distance interaction.
Results
The results of a 2 (Donation Frame: aggregate vs. per mile) x 2 (Distance: 10-mile vs.
25-mile) ANOVA, with the dependent measure being the total amount pledged, support
the hypothesis that consumers are more sensitive to price than quantity. To begin, this
analysis revealed no main effect for Donation Frame (Xper mile = $10.54 vs. Xaggregate =
$7.94; F1,65 = 2.26, p = .1380), but a significant main effect for Distance ( X10-mile = $6.86
vs. X25-mile = $11.48; F1,65 = 8.18, p < 0.01).
More importantly, this analysis revealed a significant Donation Frame x Distance
interaction (F1,65 = 12.22, p < 0.001). As shown in Figure 2, a planned contrast revealed
that the mean total pledges in the aggregate conditions were similar regardless of whether
5 For subjects in the per-mile conditions, arriving at a total pledge merely required multiplying their pledges by thelength of the walkathon they were presented with.
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the distance of the walkathon was 10 or 25 miles (XAggregate/10-mile = $8.47 vs. XAggregate/25-
mile = $7.44; F1,65 = 0.20, p > 0.60). That is, subjects in the aggregate pledge condition
offered the same total amount regardless of the distance of the walkathon. In contrast, a
second planned contrast indicated that the mean total pledges in the per-mile conditions
were much smaller in the 10-mile condition than in the 25-mile condition (Xper-mile/10 mile =
$5.18 vs. Xper-mile/25 mile = $15.51; F1,65 = 20.19, p < 0.001).
---------------------------------------Figure 2 about here
---------------------------------------
Interestingly, however, the total pledges in the per-mile conditions reflect pledges that
averaged 53 per mile in the 10-mile condition and 62 per mile in the 25-mile condition,
figures that were not significantly different from one another (F1,65 = 0.01, p > 0.90).
Thus, it appears that subjects in the per-mile conditions also ignored the number of miles
over which their pledge applied. However, whereas this behavior resulted in similar total
donations in the case of the overall framing, it resulted in significantly different total
pledges in the case of the per-mile framing.
Discussion
Subjects in Study 3 were asked to donate to a worthy causea walkathon of either 10
miles or 25 miles. These subjects indicated they would pledge an amount that was
insensitive to the length of the walkathon. In the case of the overall pledge, this resulted
in pledges averaging about $8 in both the 10-mile and 25-mile conditions. In the case of
the per-mile pledge, this resulted in pledges averaging about 60 for, again, both
distances.
Thus, in much the same way that manufacturers seem to have a predetermined price
for a box of cereal, and adjust quantity to hit that price, individuals appear to have some
concept of an appropriate overall donation and an appropriate per-mile donation and offer
pledges in that amount regardless of the effort involved in the associated walkathon.
Donors appear to be highly sensitive to the dollar amount, as reflected by the highly
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consistent mean donations across conditions, but highly insensitive to the quantity (i.e.,
miles) over which that amount is allocated.
STUDY 4
We motivated our research with the corporate practice of downsizing price
increases. And while Studies 1, 2 and 3 provide evidence that consumers may be
systematically more sensitive to product price than product quantity, we have not yet
provided any direct evidence that downsizing is effective or that consumers heightened
sensitivity to price changes affects their purchasing behavior. This is the purpose of
Study 4.
In this fourth study, we analyzed 145 weeks of retail sales, pricing, and sizing data for
four ready-to-eat products offered by a major United States food manufacturer in the late
1990s. Whereas periodic promotions resulted in actual price fluctuations for each of
these products, the non-sale price remained fixed. In addition, part way through the
period of analysis, there was a decrease in product quantityi.e., a downsizing price
increase. Using ordinary least squares (OLS) regressions, we tested whether this
reduction in product quantity had any discernable impact on unit sales. If, as predicted,
consumers are relatively insensitive to quantity, we would expect the product-sizing
variable to have little explanatory power.
Description of the Data
The data used in this study consisted of (1) weekly retail unit sales, (2) the weighted
average retail price of those units, and (3) the weighted average package sizing for those
units across 145 weeks for four of the top selling SKUs in this food manufacturers
portfolio. For convenience, we refer to the four products as Products A, B, C, and D,
with each product being a different size and flavor combination within a single product
category. As Table 3 shows, Products A through D ranged in weight from ten to sixteen
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ounces and in retail price from just under $2.00 to slightly over $3.00. These products
could be classified as fast-moving, discretionary food items.
Critically, each of the four products experienced at least one reduction in product
quantity sometime during the 145 weeks. For Products A, B, and C, a single quantity
reduction hit the retail market at approximately the 95th week. Product A decreased from
10.5 to 10.0 ounces, Product B decreased from 13.25 to 12.25 ounces, and Product C
decreased from 14.5 to 13.5 ounces.6 For Product D, there were three quantity
reductions, one around the 40th week (from 15.75 to 15.25 ounces), one around the 80th
week (from 15.25 to 14.5 ounces), and one around the 100th week (from 14.5 to 13.5
ounces). These product size changes are captured in Figure 3.
---------------------------------------Table 3 and Figure 3 about here---------------------------------------
Given our purpose, we focused on four bits of data in Study 4. First, for each of the
four products, we looked at unit sales for each of the 145 weeks. Weekly sales for
Products A, B, C, and D averaged 1.7, 3.5, 3.5, and 2.7 million units per week,
respectively. However, we also observed that the combined weekly unit sales of these
four products increased by approximately 50% over the course of the 145 weeks, from
approximately 9 million units per week in the earliest weeks of the sample to
approximately 13.5 million units in the latter weeks.
Second, we tracked the weekly average price for each of the four products. This price
reflected the weighted average of the retail price of every unit of each product sold.
Thus, the $2.15 mean price for Product A in Week 52 could have come about from 50%
of stores pricing the product at $1.99 and selling 60% of all units sold that week, and the
other 50% pricing it at $2.39 and selling the remaining 40% of units. The weekly mean
price of any one product varied by approximately 50 over the course of the 145 weeks.
6 Note that while quantity changes are made at the manufacturer level, the sales data are at the retail level. Thus, there
is a changeover period of about four to six weeks as retailers sold out of the old size and began selling the new size.
This explains why we describe the retail timing of the quantity changes as approximations.
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For instance, the minimum weekly mean price of Product D across the 145 weeks was
$2.62 and the maximum weekly mean price was $3.11.
Third, we tracked the weekly mean per-unit quantity for each product sold. For most
of the 145 weeks, this figure unambiguously reflected the number of ounces contained in
each product. For instance, for the first 95 weeks, Product A contained 10.5 ounces.
And for the final 44 weeks, Product A contained 10.0 ounces, reflecting a ounce
downsizing of this product. Between these two periods, the mean per-unit quantity
gradually fell from 10.5 ounces to 10.0 ounces as retailers gradually sold out of the older
size and started selling the newer, smaller size. Thus, during this six-week transition
period, the weekly mean per-unit quantity reflects a weighted average of the old and new
sizes being sold in the marketplace.
Finally, to capture the temporal trend inherent in the unit sales data, we tracked the
week in which the sales, pricing, and sizing data were collected.
The Regressions
We employed five OLS regressions to estimate the relative impact of price and
quantity on overall unit salesone analysis that combined all four products under a
single regression and four individual regressions, one for each of the four products.
Combined Regression In the combined regression, the dependent measure was
weekly unit sales for each of the products (Salesij, where i reflects the product andj the
week). The six explanatory variables were:
WEEKj = The number of the week to which the sales, pricing, and sizing
data pertained, withj ranging from 1 to 145. This variable wasintended to capture any linear trend that might exist in sales.
PRICEij = The average weekly price for Product i in Weekj.
SIZEij = The average weekly quantity contained in Product i in Weekj.
ProdB = A 0/1 dummy variable for Product B, with ProdB = 1 when thedata pertained to Product B and 0 otherwise.
ProdC = A 0/1 dummy variable for Product C, with ProdC = 1 when thedata pertained to Product C and 0 otherwise.
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ProdD = A 0/1 dummy variable for Product D, with ProdD = 1 when thedata pertained to Product D and 0 otherwise.
Therefore, the OLS regression for this combined analysis was:
Salesij = 0 + 1 * Weeki + 2*Priceij+ 3*Sizeij + 4*ProdB + 5*ProdC+ 6*ProdD + i
Individual Regressions The structure of the four individual regressions was almost
identical to that of the combined regression, but without the dummy variables. In
particular, using only the data that pertained to Product A, a regression was run with the
following structure:
SalesiA = 0 + 1*Weeki + 2*PriceiA+ 3*SizeiA +
i
Similar regressions were run for Products B, C, and D. The purpose of the individual
regressions was to assess the robustness of the results from the overall regression.
Results
Aggregate results from these five regressions are shown in Table 4. All models were
highly significant (F 3,141 = 60.27 or more; p < 0.0001). In addition, the adjusted-R2
ranged from 0.5543 to 0.7894, suggesting very high explanatory power for each
regression.
Combined Regression In the combined regression, several results are quickly
apparent. To begin, the overall model was highly significant (F6,573 = 467.07; p < 0.0001)
with a very high degree of fit (adjusted-R2= 0.7799). Next, each of the product dummy
variables was highly significant (p < 0.0001 in each case). As for the explanatory
variables of interest, they suggest that consumers are more sensitive to price than
quantity.
---------------------------------------Table 4 about here
---------------------------------------
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First, Week was a highly significant predictor of unit sales with a parameter estimate
of 11,584 (t573 = 12.79; p < 0.0001). Therefore, as anticipated, it does appear that there
was a significant upward trend to weekly dollar sales, with sales estimated to increase by
over 11,000 units per week.
Second, consumers appeared to be quite sensitive to changes in the Price, with the
parameter estimate for a $1 increase in price coming in at 5.9 million units (t573 =
23.80; p < 0.0001). Given the 50 range over which the price of any one of the four
products varied, this leads to an estimated sales swing of close to 3 million units. We
note, however, that this apparent sensitivity to price could be explained through
unobserved marketing mix variables. For example, if these products were featured via
advertising or end-of-aisle display at the same time they were being price promoted, the
increase in unit sales could be attributed to a combination of these efforts rather than to
price alone.
Finally, Size was not significant (t573 = 0.78; p = 0.4362). In fact, the sign of this
parameter estimate (53,157 units) was opposite that which one might have reasonably
expected. Thus, unlike price, unit sales appears to have been unaffected by downsizing.
Individual Regressions To test the robustness of this finding, we also ran individual
OLS regressions for each of the four products in our data set. If demand characteristics
varied across the four products, it may be the case that the combined regression masked
patterns that existed within the individual products.
For the most part, this was not the case and the results closely match those for the
combined regression. First, each of the overall models was significant (p < 0.0001 in
each case) with a high degree of fit (adjusted R2 = .5543 or more).Next, Week was a
highly significant predictor of unit sales in each of these four regressions, varying from a
low of 2,247 units for Product A (t141 = 4.19; p < 0.0001) to a high of 24,085 units for
Product B (t141 = 10.19; p < 0.0001). Again, Price was highly significant across the four
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regressions, with a parameter estimate ranging from 2.2 million units for Product A (t141
= 13.21; p < 0.0001) to 9.4 million units for Product B (t141 = 17.62; p < 0.0001).
Finally, Size was significant in only one of the four regressions. Specifically, for
Product B, the parameter estimate for Size was 540,473 units (t141 = 2.44; p = 0.0142),
suggesting that a one-ounce decrease in quantity (i.e., from 13.25 to 12.25 ounces) led to
a sales decrease of 540,000 units. However, Size did not approach significance in the
other three regressions. For Products A, C, and D, the parameter estimates Size were
31,558 units (p = 0.7341), 85,696 units (p = 0.4640), and 22,733 units (p = 0.8516),
respectively. These estimates are far from significant and, again, have a counterintuitive
sign. Thus, three of the four regressions suggest that consumers are insensitive to
changes in quantity. At best, this offers conflicting support for consumers sensitivity to
quantity. More generally, it supports the argument that consumers do not tend to product
quantity, even when rules of rational choice suggest they should.
Discussion
Studies 1, 2, and 3 suggest that consumers are more sensitive to product price than
product quantity. If true, firms should see smaller impact on sales when they reduce
quantity than when they increase price. Study 4 tested this proposition directly. Using
marketplace data for four of the top-selling products in a manufacturers portfolio, we
observed three results. First, over a 145-week period, there was an steady increase in
sales consistent with expanding demand for the products in question. Second, price
changes had a large impact on unit sales in any given week. A 25 price reduction led to
a 1.5 million unit sales increase on cumulative sales that averaged 11 million units.
Finally, the quantity decreases that occurred during the 145-week period had little to no
effect on unit sales. In particular, Size was not significant in the combined regression and
in three of the four individual product regressions.
These results suggest that consumers, when faced with real world situations in which
both price and quantity change, are highly sensitive to price movements and relatively
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insensitive to quantity movements. With this in mind, it appears that the strategy of
downsizing price increases employed by Dannon, Frito-Lay, Poland Spring, and Chock-
Full o Nuts is an effective means by which to increase the per-unit price paid for a
product.
CONCLUSIONS AND GENERAL DISCUSSION
Summary of Research
Manufacturers routinely pass the increased costs of producing their products on to
consumers. Usually, this has meant a straight price increasea gallon of milk that costs
$1.99 one year may cost $2.49 the next. A less obvious, but increasingly common,
strategy is to downsizeto maintain the sticker price, but to reduce the size of the
product. Thus, a tin of coffee shrinks from 1 pound to 11.5 ounces over time, while price
remains relatively constant. The implicit assumption is that downsizing will have a less
negative impact on sales than a straight price increase. This raises the possibility that
consumers are more sensitive to changes in price than quantity. To conceptualize this
differential sensitivity, we offer a framework in which consumers fully adapt to changes
in price, but only partially adapt to changes in quantity. We then draw upon data from
the laboratory and the marketplace to test whether consumers are (or are thought to be)
more sensitive to price than quantity.
From the perspective of both the manufacturer and the consumer, we find evidence of
such differential sensitivity. In Study 1, we found that manufacturers of cereal held the
price of their products relatively constant while allowing the size of their cereal boxes to
vary substantially. We reported that the variance in quantity (measured in ounces or
servings) was 1.5 to 3 times greater than variance in price across five brands of breakfast
cereal.
In Studies 2 and 3, we also found evidence of differential sensitivity at the level of the
consumer. In Study 2, we presented subjects with two ways of communicating and
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promoting the price of gourmet coffee beanseither as $6 per pound or $3 per
poundand asked which they thought would be more effective. While these two
framings of price were financially identical, subjects almost uniformly believed that $3
per pound would be more effective at promoting sales because it made the coffee
seem cheaper. This study confirms manufacturers impressions that people pay more
attention to price than quantity when assessing value. Study 3 provided additional
support for this contention. Here, we presented subjects with a request to pledge money
to a walkathon, manipulating the length of the walkathon (10 vs. 25 miles) and whether
the request was framed as an overall donation or a per-mile donation. Interestingly,
regardless of whether the donation was framed as an overall or a per-mile donation,
subjects were insensitive to distance. As a result, however, while subjects in the
aggregate donation conditions ended up making similar total donations, those in the
per mile conditions ended up making total pledges that were nearly three times greater
in the 25-mile walkathon. It appears that individuals have a strong concept of what
constitutes a fair total donation and a fair per-mile donation, but they are insensitive
to the quantity over which that donation is allocated.
Finally, in Study 4, we directly assessed the effectiveness of downsizing using data
from the marketplace. We analyzed 145 weeks of retail sales, pricing, and sizing data for
four products offered by a large packaged food manufacturer. Importantly, partway
through these 145 weeks, this manufacturer reduced quantity in each of these four
products. Using a series of OLS regressions with unit sales as our dependent measure,
we found consumers were highly sensitive to changes in price, but were highly insensitive
to changes in quantity. Thus, while Study 1 suggests that manufacturers view consumers
as more sensitive to price than quantity, Study 4 suggests this view is well-placed.
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Managerial Implications
Across multiple product categories, we have shown a robust pattern in which
consumers are more sensitive to price than to quantity. These results should have
implications for how manufacturers should think about managing price and quantity.
Raising Price versus Lowering Quantity One obvious implication of this research
involves decisions about how to pass higher costs on to consumers. Firms often raise
prices to maintain margins when the cost of doing business increases. However, firms
may be better off maintaining margins by reducing the quantity contained in their
products.
But we would note that there may be product categories in which consumers may
have strong negative reaction to downsizing. For instance, when consumers needa
particular quantity for some purpose, downsizing may not be a good idea. Consumers
who purchase gloves or shoelaces need to purchase a set of two and might be unhappy
when they realize they will need to purchase two packages to meet their needs. Likewise,
when a recipe calls for a stick of butter, customers may be annoyed by a downsizing
strategy that makes it difficult to determine the exact quantity to put in.
Similarly, there are some product categories that are closely associated with certain
benchmark sizes. Consumers are accustomed to purchasing a dozen eggs, a half-gallon
of ice cream, a quart of oil, and a pound of bacon. Deviations from these standards may
register much more quickly with consumers than, say, changing the sizing of a bag of
corn chips from 13.5 to 12.5 ounces. In this regard, Chock Full oNuts decision to
downsize from the well-established pound of coffee clearly registered with consumers
in the late 1980s. That said, however, once the standard of one-pound had been broken,
other coffee manufacturers eventually followed suite and Chock Full oNuts has been
relative free to further reduce the content in their coffee to its current 11.5 ounces.
Interestingly, ice cream manufacturers have recently begun deviating from their
traditional half-pound sizing, with some of Breyers ice creams now being offered in
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1.75-quart containers. One could argue that Breyers is easing the way for future quantity
reductions in this product category.
Finally, downsizing may be more effective for discretionary goods that have elastic
consumption patterns than for necessities that have inelastic consumption patterns. For
instance, while a consumer my not mind (and may actually welcome) the downsizing of a
bag of potato chips from 14 ounces to 12 ounces, the mother of two small children may
greatly resent the downsizing of a package of disposable diapers from 44 to 38 diapers.
At the very least, while the downsizing the former may have no impact on a consumers
frequency of purchasing chips, downsizing the latter merely forces a parent to purchase
diapers more often.
Managing Products Over Time Managers could also use consumers differential
sensitivity to price over quantity to manage their products over time. Consider the case
of a food manufacturer that sells several different sizes (e.g., small, medium, large) of the
same basic product. In the face of ever increasing costs, this manufacturer may wish to
downsize these products periodically, with an eye toward eventually eliminating the
smallest size and introducing a new extra-large size (see Figure 4 for an illustration).
Following this strategy, the firm will have effectively increased its prices over time, but
through downsizing as opposed to straight price increases.
---------------------------------------Figure 4 about here
---------------------------------------
Product Portfolios Finally, a consumers differential sensitivity to price over
quantity could have relevance to product portfolios. Consider a frozen-foods
manufacturer that makes cheese pizzas, vegetarian pizzas, and pepperoni pizzas. Given
differences in the cost of ingredients for these three types of pizza, the manufacturer
could produce all of the pizzas to be the same weight (e.g., 15 ounces), but price them
based on their cost of ingredients (e.g., cheese = $3.29, veggie = $3.49, pepperoni =
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$3.69). Alternatively, the manufacturer could price the pizzas similarly (e.g., $3.49) and
adjust the weights of the pizzas to reflect the cost of ingredients (e.g., cheese = 16 oz.,
veggie = 15 oz., pepperoni = 14 oz.). The best marketing strategy for this pizza
manufacturer would depend on whether it wants consumers trading off its products on
price (e.g., $3.29 vs. $3.49 vs. $3.69) or quantity (e.g., 14 oz. vs. 15 oz. vs. 16 oz.). If
consumers are generally more sensitive to price, it would seem the latter strategy would
help keep consumer support for the vegetarian and pepperoni pizzas high and prove more
profitable for the manufacturer. Such thinking may help explain instances of uniform
pricing in the marketplace. For example, Pepperidge Farm recently priced its four
variants of Milano cookies (regular, milk chocolate, mint, double chocolate) at $2.59, but
varied the weight of those offerings (6, 6.25, 7, and 7.5 ounces respectively).
Future Research
In this research, we demonstrated that consumers are (and are thought to be) more
sensitive to price over quantity in a host of settings. We also offered a conceptual
framework that begins to explain this differential sensitivity. However, more research
should be conducted to determine the mechanisms that drive this effect. In particular,
while we have argued that consumers behave as if they adjust incompletely for changes
in quantity, there are many reasons why this phenomenon occurs.
As noted earlier, one possibility is that consumers are often unaware of item size,
thereby rendering changes in quantity relatively meaningless. By this argument, when a
bag of chips is reduced from 13.5 ounces to 12.5 ounces, consumers wont even notice
the change and are no less likely to make the purchase than they were prior to the
downsizing. Although a lack of size awareness may have some explanatory power, it
does not explain the results of Study 2 (where prices were explicitly provided as per
pound or per pound) or Study 3 (where distances were clearly identified as either 10
miles or 25 miles).
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A second possibility is that consumers evaluate quantity categorically. For
instance, they may evaluate the price of cereal per box, the price of bread per loaf,
and the price of coffee per container. Thus, they might be quite sensitive to price, but
much less exacting when it comes to quantity contained within a box or a loaf, so
long as that box or loaf does not deviate too far from an acceptable standard.
A third possibility is that consumers are aware of changes in quantity, but are simply
more willing to tradeoff quantity than price. Such could be the case if consumers view an
increase in price as a loss, but a decrease in quantity as a foregone gain. With losses
looming larger than gains (Kahneman and Tversky 1979; Thaler 1985) as suggested by
prospect theory, downsizing may be more palatable to consumers than a commensurate
increase in price.
A fourth possibility is that downsizing or quantity manipulation is effective because it
is relatively uncommon. That is, perhaps consumers pay less attention to quantity
variations because quantity, unlike price, rarely changes. If this is the case, one might
expect consumers to become increasingly sensitive to changes in quantity as quantity
changes become more common.
Finally we note that the differential sensitivity between price and quantity that our
studies support was found in the contexts where higher costs needed to be passed along to
consumers. Future research should consider whether the price sensitivity that we
detected also holds for deflationary contexts in which manufacturers must decide
between lowering prices and increasing quantities. On the one hand, if price sensitivity is
a general phenomenon, consumers may prefer to purchase the same sized product at a
cheaper price. On the other hand, if there is some stickiness around the traditional
product price, consumers might prefer to receive a larger quantity.
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Table 2:
Study 1 Aggregate Price and Size Data for Ready-to-Eat Cereals Across Brands
Panel A: All Cereals
Brand Statistic Price Weight (oz.) Servings (#)
General Mills (n=46) Mean $3.71 15.53 12.78
Std Dev. $0.45 3.80 3.74
Std Dev/Mean .122 .244 .293
Post (n=26) Mean $3.06 16.38 11.81
Std Dev. $0.26 4.33 3.29
Std Dev/Mean .086 .265 .278
Kelloggs (n=41) Mean $3.46 16.93 12.78
Std Dev. $0.52 4.13 4.11
Std Dev/Mean .151 .244 .321
Quaker Oats (n=16) Mean $3.50 16.15 13.31
Std Dev. $0.73 6.31 3.89
Std Dev/Mean .209 .391 .292
Store Brand (n=28) Mean $2.33 16.38 12.93
Std Dev. $0.17 4.09 3.84
Std Dev/Mean .075 .250 .297
Panel B: Multiple Sizes Excluded
Brand Statistic Price Weight (oz.) Servings (#)General Mills (n=30) Mean $3.71 14.74 11.77
Std Dev. $0.31 2.78 2.71
Std Dev/Mean .083 .188 .231
Post (n=24) Mean $3.04 15.42 11.79
Std Dev. $0.23 2.51 3.43
Std Dev/Mean .075 .163 .291
Kelloggs (n=17) Mean $3.74 17.19 11.41
Std Dev. $0.40 3.48 3.06
Std Dev/Mean .106 .203 .268
Quaker Oats (n=12) Mean $3.57 15.53 12.42
Std Dev. $0.82 7.03 3.73
Std Dev/Mean .230 .452 .300
Store Brand (n=26) Mean $2.33 15.89 13.12
Std Dev. $0.18 3.74 3.90
Std Dev/Mean .076 .235 .298
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Table 3:
Study 4: Mean Weekly Unit Sales, Dollar Sales, Price, and Size of Products
Product A Product B Product C Product DWeekly Unit Sales
(in millions)
1.736
(0.211)
3.535
(1.459)
3.490
(0.734)
2.673
(0.554)
Weekly Dollar Sales
(in millions)
$3.554
(0.355)
$8.469
(3.059)
$9.383
(1.756)
$7.896
(1.585)
Weekly Mean Price
(in dollars)
$2.05
(0.08)
$2.43
(0.11)
$2.70
(0.10)
$2.96
(0.08)
Weekly Size
(in ounces)
10.34
(0.23)
12.93
(0.46)
14.18
(0.46)
14.72
(0.86)
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Table 4:
Study 4 Regression of Weekly Unit Sales on Week, Price, and Size
Regression Results
Combined Individual Regressions
Product A Product B Product C Product D
Intercept: Estimate 13,553,487 6,340,204 17,489,770 15,416,277 13,208,376
t-statistic 15.94 6.25 5.88 9.32 6.85
p-value < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
Week: Estimate 11,584 2,247 24,085 10,173 10,596
t-statistic 12.79 4.19 10.19 8.27 4.06
p-value < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
Price: Estimate 5,902,422 2,163,897 9,357,615 4,239,770 3,711,187
t-statistic 23.80 13.21 17.62 13.51 8.19
p-value < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
Size: Estimate 53,157 31,558 540,473 85,696 22,733
t-statistic 0.78 0.34 2.44 0.73 0.19
p-value = 0.4362 = 0.7341 = 0.0142 = 0.4640 = 0.8516
Product B Dummy 4,143,422*
Product C Dummy 5,789,948*
Product D Dummy 6,510,572*
Adjusted R2 0.7799 0.5543 0.7894 0.7815 0.5889
F-Statistic F6,573 = 463.07 F1,141 = 60.27 F1,141 = 180.89 F1,141 = 172.69 F1,141 = 69.77
Number of Observations 580 145 145 145 145
Notes: * Product dummies are significant at p < 0.0001
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Figure 1:
Study 2 Distribution of Responses for More Effective Pricing
Figure 2:
Study 3 The Impact of Framing and Distance on Mean Donation Pledges
$12 per poundwill bemuch more effective
2 3 4 5 6 71
$6 per poundwill be
much more effective They will beequally effective
n=11
n=23
n=13
n=5n=3
n=5
n=2
$12 per poundwill bemuch more effective
2 3 4 5 6 71
$6 per poundwill be
much more effective They will beequally effective
n=11
n=23
n=13
n=5n=3
n=5
n=2
25 Miles10 Miles
$15
$10
$5
$0
62/mile ($15.51)
$7.44
52/mile ($5.25)
$8.47
Per-Mile Frame
Aggregate Frame
Total Pledge
Distance
25 Miles10 Miles
$15
$10
$5
$0
62/mile ($15.51)
$7.44
52/mile ($5.25)
$8.47
Per-Mile Frame
Aggregate Frame
Total Pledge
Distance
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Figure 3:
Study 4 - A Reduction of Product Quantity Over Time
8.00
14.00
10.00
12.00
16.00
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140
9.00
15.00
11.00
13.00
Quantity
(Ounces)
Week
Product A
Product B
Product C
Product D
8.00
14.00
10.00
12.00
16.00
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140
9.00
15.00
11.00
13.00
Quantity
(Ounces)
Week
Product A
Product B
Product C
Product D
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Figure 4:
Managing a Product Line Over Time
Time
Size
X
$2.99
$2.99
$2.99
$2.29$2.29
$2.29
$1.69
$1.69
$1.69
$3.89
Time
Size
X
$2.99
$2.99
$2.99
$2.29$2.29
$2.29
$1.69
$1.69
$1.69
$3.89
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