Downsizing Price Increases

8/2/2019 Downsizing Price Increases

1/41

1

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


2/41

2

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.


3/41

3

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.


4/41

4

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.


5/41

5

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


6/41

6

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


7/41

7

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


8/41

8

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.


9/41

9

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.


10/41

10

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


11/41

11

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.


12/41

12

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.


13/41

13

---------------------------------------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


14/41

14

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.


15/41

15

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.


16/41

16

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


17/41

17

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


18/41

18

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-


19/41

19

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.


20/41

20

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).


---------------------------------------

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


21/41

21

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


22/41

22

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.


23/41

23

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.


24/41

24

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

---------------------------------------


25/41

25

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


26/41

26

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


27/41

27

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


28/41

28

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.


29/41

29

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


30/41

30

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.


---------------------------------------

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 =


31/41

31

$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).


32/41

32

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.


33/41


34/41

34

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


35/41

35

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)


36/41

36

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


37/41

37

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


38/41

38

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


39/41

39

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


40/41

40

REFERENCES

Consumer Reports (2001), Selling It, Consumer Reports Magazine, November 2001, p.

67.

Gourville, John T. (1998), Pennies-a-Day: The Effect of Temporal Reframing on

Transaction Evaluation, Journal of Consumer Research, 24 (March), 395-408.

----- (2003), The Effects of Monetary Magnitude and Level of Aggregation on the

Temporal Framing of Price,Marketing Letters, 14 (2), 125-135.

Kahneman, Daniel and Amos Tversky (1979), "Prospect Theory: An Analysis of

Decision Under Risk," Econometrica, 47 (2), 363-391.

Koehler Jonathan J. (2001), The Psychology of Numbers in the Courtroom: How to

Make DNA Match Statistics Seem Impressive or Insufficient, Southern California Law

Review, 74, 1275-1306.

----- and Laura Macchi (in press), Thinking About Low Probability Events: An

Exemplar Cueing Theory,Psychological Science.

PepsiCo (2001), PepsiCo Reports Outstanding Q1 Results EPS Surges 17%:

Impressive Results Mark Sixth Consecutive Quarter of Double-Digit Earnings Growth,

PepsiCo Press Release, April 23, 2001.


41/41

Raghubir, Priya and Joydeep Srivastava (2002), Effect of Face Value on Product

Valuations in Foreign Currencies,Journal of Consumer Research, 29 (December), 335-

347.

Russo, J. Edward (1977), The Value of Unit Price Information,Journal of Marketing

Research, 14, 193-201.

Thaler, Richard H. (1985), "Mental Accounting and Consumer Choice,"Marketing

Science, 4 (3), 199-214.

Tversky, Amos, Shmuel Sattath and Paul Slovic (1988), Contingent Weighting in

Judgment and Choice,Psychological Review, 95 (3), 371-384.

Winter, Greg (2001), Pepsi Earnings Increase 18%, Continuing Growth Streak, The

New York Times, April 24, 2001.

Date post:	06-Apr-2018
Category:	Documents
Upload:	juan-pablo-dellacasa
View:	225 times
Download:	0 times

Downsizing Price Increases

Documents