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CONSUMER RESPONSE TO UNCERTAIN PROMOTIONS:
AN EMPIRICAL ANALYSIS OF CONDITIONAL REBATES
Kusum L. Ailawadi a,*, Karen Gedenk b, Tobias Langer c, Yu Ma d, and Scott A. Neslin e
Tuck School of Business, Dartmouth College, 100 Tuck Hall, Hanover, NH 03755, U.S.,
[email protected]
b
University of Hamburg, Welckerstr. 8, 20354 Hamburg, Germany, [email protected]
c
1618 N Campbell Ave #3, Chicago, IL 60647, U.S., [email protected]
d
University of Alberta, Edmonton, AB, T6G 2R6, Canada, [email protected]
e
Tuck School of Business, Dartmouth College, 100 Tuck Hall, Hanover, NH 03755, U.S.,
[email protected]
*
Corresponding author. Tel.: +1 603 646 2845; fax: +1 603 646 1308.
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Note: Authors are listed in alphabetical order to reflect their equal contribution to the paper.
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ARTICLE INFO rth
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========================================================== First received in August 31, 2012 and was under review for 3 ½ months. Area Editor: Olivier Toubia ============================================================
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Acknowledgements:
The authors thank Neeraj Arora of the University of Wisconsin, Imran Currim of the University
of California, Irvine, Rakesh Sarin of UCLA, and session participants at the 2011 Marketing
Science Conference, 2012 ISMS Practice Conference, Universidad Carlos III de Madrid, KU
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Leuven, and Tuck School at Dartmouth for helpful comments. They are also grateful to the AE
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and reviewers for their valuable suggestions. 4
CONSUMER RESPONSE TO UNCERTAIN PROMOTIONS:
AN EMPIRICAL ANALYSIS OF CONDITIONAL REBATES
Abstract:
We formulate, estimate, and analyze a model of consumer response to promotions where
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consumers’ receipt of the promotional reward is uncertain. The model incorporates consumers’
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risk aversion and their subjective assessment of the probability that they will get the reward. It is
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used to assess the effectiveness of a “conditional rebate”, where the uncertainty arises because
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the reward is contingent on an external event, versus a traditional rebate, which is similar in all
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respects except that it is certain. We estimate the model using a conjoint choice experiment.
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Response to conditional rebates is highly segmented and related to perceived thinking costs and
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savings and entertainment benefits of conditional rebates as well as to event involvement and
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gambling proneness. In our application, conditional rebates are more cost effective than certain
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rebates, mostly because consumers’ subjective probability of the event occurring is higher than
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what market wisdom suggests.
Keywords: uncertain rewards, promotions, conditional rebates, consumer utility model, risk
aversion, subjective probability
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1. Introduction
Marketers are always looking for promotions that generate excitement and interest,
stimulate sales, and increase profits. Promotions offer a reward, for example a discount, gift, or
extra product, to the consumer who buys the company’s product. For the majority of
promotions, receipt of the reward is a certainty, but there are also several promotions where it is
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not. Uncertainty may be due to (a) the consumer’s own skill, e.g., contests; (b) pure luck, e.g.,
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sweepstakes; (c) the marketer’s decision to express the reward level as “tensile”, e.g., “X% to
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Y% off this week”; or (d) whether an external event occurs, e.g., “Buy the product now and get
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$X off if the Red Sox win the World Series”.
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Two issues immediately come into play with uncertain promotions: (1) consumers’ risk
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aversion, and (2) consumers’ perceptions of the probability, i.e., their “subjective probability”, of
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receiving the reward. Consumers are typically risk averse, which should work against uncertain
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promotions. However, consumers may believe the likelihood of receiving a reward is higher
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than it really is, due to innate optimism or an upward bias in assessing the probability of positive
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events. This should work in favor of uncertain promotions.
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Laboratory research provides important insights on how consumers respond to some
types of uncertain promotions (e.g., Dhar, Gonzalez-Vallejo, & Soman, 1995; 1999; Goldsmith
& Amir, 2010; Mazar, Shampanier, & Ariely, 2012). However, these studies simply document
average purchase likelihood or the percentage of consumers who prefer one or the other type of
promotion. To the best of our knowledge no one has developed and estimated a model of
consumer response to uncertain versus certain promotions. The benefit of a model is that, in
addition to the insights one can obtain from lab studies, it provides a decision tool to predict
consumer response to choices not necessarily presented in the measurement.
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We develop such a model in this paper and show how it can be used by marketers to
determine whether, and for whom an uncertain promotion may be more effective than its certain
counterpart. Our model captures risk aversion and the consumer’s subjective probability of
getting a reward, and allows for heterogeneity in these as well as other model parameters. We
apply this model to a class of uncertain promotions that has become increasingly prevalent in
recent years. In these promotions, often termed “conditional rebates”, the consumer makes a
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purchase at time t and receives a reward at a subsequent time t+x conditional on an uncertain
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money back if their home team wins a sports championship.
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external event occurring between t and t+x. For example, many companies offer their customers
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Table 1 lists examples of conditional rebates that we have compiled from the internet. As
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the table shows, these promotions are used in many countries; they are offered on big-ticket and
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relatively high-involvement products; and the external event usually involves sports or the
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weather. An entire industry has been built around such promotions, consisting of companies like
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Oddsonpromotions.com, Interactive Promotions Group, Sadler Sports and Recreation Insurance,
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SCA Promotions, and GrandPrizePromotions.com. These companies help client firms
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these promotions.
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implement conditional rebates, contests, and sweepstakes and offer insurance indemnification for
< Insert Table 1 about here > As noted above, conditional rebates are characterized by uncertainty and delayed
rewards. Figure 1 categorizes different types of promotions in terms of these attributes. Given
our goal of modeling the effectiveness of uncertain versus certain promotions, and our specific
interest in examining conditional rebates, we compare conditional rebates to their closest certain
analog, i.e., traditional rebates (hereafter termed “certain rebates”). As discussed for example by
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Baucells and Heukamp (2012), consumers have both a monetary discount rate (trading off
outcomes they receive immediately versus with a delay) and a probability discount rate (trading
off outcomes they receive with uncertainty versus certainty). Since we are interested in isolating
the impact of uncertainty, it is important to ensure the comparison is between promotions that are
similar in terms of delay. Both conditional rebates and rebates are delayed.
<Insert Figure 1 about here>
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To summarize, our objective is to present a model for quantifying consumer response to
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conditional rebates, as an example of the broader class of uncertain promotions, compared to
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certain rebates. Our substantive contribution lies in (a) assessing the relative attractiveness of a
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unique but prevalent type of uncertain promotion that has not been studied previously; (b)
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quantifying the market share impact of conditional rebates compared to rebates; and (c)
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characterizing segments of consumers who differ in their response to such promotions. Our
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methodological contribution lies in developing a consumer utility model that incorporates risk
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aversion and subjective probability. We estimate the model using a conjoint experiment,
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establish its superior fit and predictive validity over simpler benchmark models, and use the
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estimated model to simulate market shares of competing products in different promotion
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scenarios. Our model is useful for understanding consumer response to conditional rebates as
well as other types of uncertain promotions, and as a tool that can help managers decide whether
and for whom to utilize these promotions rather than their certain counterparts.
A few of our key empirical findings are as follows. First, we identify three segments that
differ substantially in their response to conditional rebates. Segment membership is driven by
perceived benefits and costs of conditional rebates, gambling proneness, and event involvement.
Second, market share simulations suggest that in our application, a conditional rebate can be
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more cost effective than a certain rebate in that it can recover the share lost to a competitor’s
certain rebate at a lower expected cost and hence greater profit. Third, consumers tend to
overestimate the probability of the event in the conditional promotion, enhancing its cost
effectiveness. Fourth, conditional rebates are more effective for TVs than for washing machines,
perhaps because TVs are hedonic, and washing machines are utilitarian.
The rest of the paper is organized as follows. In section 2 we review prior research,
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before we present our modeling framework in section 3. This is followed by a description of our
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data in section 4, and our empirical results in section 5. We conclude the paper with a summary
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of our key findings and implications for managers and researchers in section 6.
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2. Prior Research
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2.1 Consumer Response to Uncertainty
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Prior research on consumer response to uncertainty suggests a tension with respect to
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which will be more effective – a certain rebate or an uncertain conditional rebate. On one hand,
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consumers may prefer a certain promotion because of risk aversion. Consumers have been found
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to be risk-averse, even extremely so, in a variety of situations (Iyengar , Jedidi, & Kohli, 2008;
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Narayanan & Manchanda, 2009; Roberts & Urban, 1988; Gneezy, List, & Wu, 2006).
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On the other hand, a conditional rebate may be more effective because consumers
overestimate the probability that the event will occur. They may believe they are luckier than
others (Wagenaar & Keren, 1988) or have innate optimism (Alloy & Abramson, 1988;
Goldsmith & Amir, 2010). There is a strong “affinity” aspect to these rebates when they are
linked to sporting events, e.g., the Boston Red Sox winning the World Series or the German team
winning the European Soccer Championship, and people overestimate the probabilities of vivid
(Johnson et al., 1993; Lichtenstein et al., 1978; Weber & Hilton, 1990), desirable (Babad, 1987;
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Fischer & Budescu, 1995) and salient events (Bar-Hillel, Budescu, & Amar, 2008). They may
therefore think the likelihood of receiving a reward is higher than it really is. This tension
underscores the need to empirically assess the effectiveness of conditional rebates versus rebates
and to incorporate both risk aversion and subjective probability in modeling consumer response.
2.2 Effectiveness of Uncertain Promotions
Laboratory experiments have found positive consumer response to uncertain promotions
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in some situations. Mobley, Bearden, and Teel (1988) and Dhar, Gonzalez-Vallejo, and Soman
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(1995; 1999) show that consumers prefer tensile claims, where the size of the discount is
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uncertain, over certain discounts when the probability of getting a discount is low. Goldsmith
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and Amir (2010) show that in a low-stakes situation that does not demand much thinking (e.g.,
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choices involving candy as a reward) consumers prefer uncertain rewards almost as much as the
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more preferred outcome, and suggest that this is driven by innate optimism. Mazar, Shampanier,
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and Ariely (2012) show that given a choice between a certain promotion (e.g., 1/3 off the price of
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a candy bar) versus an uncertain promotion of equal expected value (e.g., 1/3 chance of getting
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the candy bar free) consumers are generally more likely to choose the latter because they want to
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avoid the “pain of paying”.
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However, conditional rebates differ from the types of uncertain promotions studied
previously in important ways that leave open the question of their effectiveness. First, they
require the consumer to make a purchase decision with the possibility of getting a reward later if
the event occurs. Thus, they do not alleviate the “pain of paying”. Second, conditional rebates
are usually offered on big-ticket products, as are rebates. Thoughtful consideration, which
Goldsmith and Amir (2010) find inhibit the attractiveness of uncertain promotions, is much more
likely in such contexts. Third, the uncertainty in conditional rebates depends not on stated odds
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as in the studies discussed above, but on the consumer’s subjective probability that the external
event will occur. This needs to be modeled given evidence from prior research that there are
biases in probability assessment, especially for vivid and desirable events.
Finally, previous studies only report average purchase likelihood or percentage of
consumers who prefer one or the other type of promotion. Without an underlying model of
consumer utility, one cannot simulate choices and market shares under different scenarios to
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assess the effectiveness of different types of promotions from the firm’s perspective.
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2.3 Heterogeneity in Response to Uncertain Rewards
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To the best of our knowledge, there is little research on heterogeneity in response to
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uncertain promotions. However, the literature suggests promotions provide not only economic
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benefits like savings, but also hedonic benefits like entertainment (Chandon, Wansink, &
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Laurent, 2000; Raghubir, Inman, & Grande, 2004), and consumers may differ in how they
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perceive these benefits. Gambling has entertainment value and shares some elements with
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uncertain promotions, so gambling proneness may be associated with response to conditional
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rebates. Excitement and sensation-seeking are important motivations for gambling (Coventry
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&Brown, 1993; Pantalon et al., 2008). Presumably, consumers who are more involved with the
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external event will get more excitement from participating in a conditional rebate. Conditional
rebates may require more thinking, a cost which some consumers can handle better than others.
Finally, demographic variables may contribute to heterogeneity since they are associated with
gambling and promotional games (Chalmers & Willoughby, 2006; Fang & Mowen, 2009; Mok
& Hraba, 1991).
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In summary, response to conditional rebates may be associated with consumer
characteristics such as the perception of savings and entertainment benefits, thinking costs,
gambling proneness, and involvement with the event, as well as demographics variables.
3. Modeling Framework
3.1 Overall Approach
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We formulate a utility model that incorporates the consumer’s risk aversion and
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subjective probability of the event occurring, while allowing for heterogeneity in these as well as
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the other elements of the model. We first use a simulated data test to ensure we can identify the
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model. We then estimate the model using choice data from a conjoint experiment. Respondents
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choose between two products offered in a series of choice sets. A product may be offered
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without a promotion, with a certain rebate, or with a conditional rebate, and the promotions have
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different discount levels. Finally, we use our parameter estimates to simulate choices under
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3.2 Consumer Utility Model
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different scenarios and compare the effectiveness of conditional rebates to that of rebates.
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We assume the consumer makes choices to maximize expected utility, which depends on
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(1) the consumer’s preference for the brand associated with the product, (2) the quality of the
product, (3) the discount offered with a conditional rebate or a certain rebate, (4) the consumer’s
subjective probability that the event will occur in the case of a conditional rebate, and (5) other
factors not observed by the researcher. We employ a quadratic utility function (e.g., Iyengar,
Jedidi, & Kohli, 2008; Narayanan, Manchanda, & Chintagunta, 2005), which can capture both
risk averse and risk prone behavior.
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A disadvantage of the quadratic utility function is that it is potentially non-monotonic in
the amount of the discount. However, within the range of discounts investigated in our study,
this occurred in only one out of twelve cases (two types of promotion × three segments × two
categories). We also tested a piece-wise linear model, exponential and logarithmic functions.
None of these alternatives dominated the quadratic function in hold-out fit and prediction. The
hold-out log-likelihood with the exponential function was slightly better in one product category
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but clearly worse in the other, and the exponential model estimates were highly sensitive to
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starting values. Therefore, we use the quadratic function in all our analyses.
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Expected utility is given by:
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(1)
, Consumer c’s expected utility for product i in choice set j.
Qualijc =
Quality of product i in choice set j of consumer c.
Discijc =
Discount offered for product i in choice set j of consumer c.
Rebijc =
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E(Uijc) =
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where:
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1 if certain rebate offered for product i in choice set j of consumer c, 0
otherwise.
CRijc
=
1 if conditional rebate offered for product i in choice set j of consumer c, 0
otherwise.
pc
=
Consumer c’s subjective probability that the condition specified in the
conditional rebate will occur.
βic0 is consumer c’s baseline preference for the brand associated with product i (there are
two brands in the study); and εijc reflects factors influencing customer c’s expected utility for
product i in choice set j, not observed by the researcher. If a certain rebate is offered, the
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expected contribution to utility from the discount is
, whereas if a
conditional rebate is offered, the expected contribution is
.
Note that we allow different discount parameters for the certain rebate versus the
conditional rebate (βc2 and βc3 vs. βc4 and βc5). This is important given the distinction made in
decision science research between strength of preference, i.e., the marginal value of additional
outcomes, and intrinsic risk attitude (e.g., Dyer & Sarin, 1982; Smidts, 1997). For both certain
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rebates and conditional rebates, we compute the traditional Arrow-Pratt index of relative risk
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, and
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(2)
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aversion, Rc (Pratt, 1964) which equals the negative of the elasticity of marginal utility:1
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. (3)
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For the certain rebates, Rc captures strength of preference, i.e., a positive RcREB would
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simply mean that marginal utility becomes smaller with increasing discounts. Because
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conditional rebates contain uncertainty, RcCR captures the consumer’s inherent risk attitude in
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addition to strength of preference.
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As we discussed previously, consumers typically have biases in their subjective
probability assessment. This makes it important to use their self-assessed probabilities of the
event even if a more objective measure (e.g., based on the wisdom of the market) is available.
However, cumulative prospect theory suggests that using consumers’ stated probabilities as is
may not be wise, because when making decisions under uncertainty consumers overweight small
probabilities and underweight moderate and high probabilities (Tversky & Kahneman, 1992).
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A negative Arrow-Pratt index implies risk proneness and a positive value implies risk averseness.
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We therefore use each consumer’s self reported probability of the event (sp) but apply the
probability weighting function proposed by Prelec (1998):2
(4)
,
where 0 < α < 1. As α approaches 1, there is no weighting, i.e., pc equals the stated probability.
As α approaches 0, pc is constant at 0.37, and for α values in between, small stated probabilities
are over-weighted, while large probabilities are under-weighted.
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One could also estimate pc from consumers’ choices. However, this would require
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constraining the discount parameters to be the same for certain rebates and conditional rebates
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(βc2=βc4 and βc3=βc5) in order to identify the model. Conceptually, we believe it is better to use
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the information contained in self-reported probabilities and retain the important property of
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different discount parameters for rebates vs. conditional rebates. Still we did test a model with
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estimated pc and found that it did not improve fit and prediction compared to our proposed model
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described in Equations 1 and 4.3
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,
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(5)
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We can write Equation 1 as:
where Vijc is consumer c’s deterministic utility for product i in choice set j. Assuming the
unobserved factors εijc are independently distributed extreme value Type 1 (Gumbel), and the
consumer is choosing between two products i and k, we have a binomial logit model:
(6)
,
2
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We thank the review team for suggesting this weighting function. We tried a more flexible two-parameter
weighting function also proposed by Prelec (1998), but it was not identified.
Details are available from the authors upon request.
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where Probijc is the probability that consumer c chooses product i rather than k in choice set j.
3.3 Heterogeneity
We model heterogeneity using a latent class model with concomitant variables
(Kamakura, Wedel, & Agrawal, 1994; Wedel & Kamakura, 2000). This model is very flexible
in that we do not need to make assumptions about the distribution of parameters, and it allows us
to derive meaningful interpretations for managers who are used to thinking about segments of
,
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(8)
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(9)
Probability that consumer c chooses brand i in choice set j, conditional on
belonging to segment s.
Vijcs
=
Deterministic utility of consumer c for product i in choice set j, conditional
on belonging to segment s.
L
=
Likelihood function.
ηcs
=
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Probijcs =
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A priori probability that consumer c belongs to segment s.
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Yjc
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where:
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,
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(7)
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consumers with different preferences. Our model thus becomes:
=
1 if consumer c chooses brand i in choice j; 0 if consumer c chooses brand k.
We ensure that the a priori probabilities of segment membership ηcs lie between 0 and 1
and sum up to 1 across segments for each consumer through the following formulation:
(10)
.
The probabilities of segment membership are in turn a function of concomitant variables.
Based on the literature reviewed previously, we include perceptions of savings and entertainment
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benefits, thinking costs, gambling proneness, and involvement with the event as concomitant
variables:
(11)
mcs = γs0 + γs1Savec + γs2Entc + γs3Thinkc + γs4Invevc + γs5Gambc ,
where:
=
Savings benefit of conditional rebates perceived by consumer c.
Entc
=
Entertainment benefit of conditional rebates perceived by consumer c.
Thinkc
=
Thinking costs of conditional rebates perceived by consumer c.
Invevc
=
Involvement of consumer c with the event in the conditional rebate.
Gambc
=
Gambling proneness of consumer c.
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Savec
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We initially also included gender, education, income, and age, but their effects were not
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statistically significant and likelihood ratio tests showed that, jointly, they did not improve fit.
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Therefore, we have dropped these demographic variables from subsequent analyses. We obtain
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maximum likelihood estimates of all the model parameters jointly.4
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3.4 Model Identification
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The procedure we use to check model identification is as follows. We create simulated
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data sets for 200 consumers based on our conjoint design with known values for the parameters
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and three segments. We then see whether our estimation can recover those parameters. We do
not include concomitant variables because our interest here is simply in ensuring identification of
our core utility model. In the Appendix, we present results for two sets of true parameter values
– one very similar to our results in the washing machine category, the other arbitrary. The
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We test various sets of starting values for the model parameters. We also rescale the Discount variable to avoid
very small discount parameters and facilitate estimation. We thank Kenneth Train for suggesting this rescaling.
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parameter estimates are not sensitive to starting values and none is significantly different from its
true value. Thus, we are confident that our model is identified.
4. Data
We utilize data from a choice-based conjoint experiment. Compared to methods for
measuring utility functions common in decision analysis, where respondents have to indicate a
certainty or probability equivalent (Smith & von Winterfeldt, 2004; Wakker & Deneffe, 1996),
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making choices between pairs of products is easier and more realistic (see Iyengar, Jedidi, &
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Kohli, 2008 for another example of using conjoint analysis to estimate utility functions). While
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we would have liked to make our conjoint analysis incentive-compatible (Ding, Grewal, &
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Liechty, 2005; Dong, Ding, & Huber, 2010), this is not feasible for the high-ticket products that
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rebates are typically used with – we could not make consumers pay that much and live with their
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decisions. Note however, that any bias due to the lack of incentive-compatibility applies to both
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certain and conditional rebates and should not affect the comparison between them.
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We investigate two product categories, TV sets and washing machines. Both are high-
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ticket durables but TVs are more hedonic and washing machines are more utilitarian. We
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conducted the study with German consumers solicited from an online panel provider in the
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summer of 2011. The external event for conditional rebates is often a major sports event (see
Table 1). We therefore chose the 2012 European Football Championship as our event, and the
condition for getting the conditional rebate was that the German team wins the championship.
The questionnaire consisted of three sections. The first screened respondents to select
those who either owned a washing machine/TV or were planning to buy one in the next two
years, and who are involved in the purchase decision. The second section was the choice-based
conjoint task. The third section obtained additional information from respondents including
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perceived benefits and costs of conditional rebates, involvement with the event, and gambling
proneness. Demographic information was available from the panel provider.
4.1 Design of Conjoint Experiment
For each product category, we designed a full-profile choice-based conjoint study. The
washing machines were specified to be front-loading and offered at a regular price of 600 €. The
TV sets were 32” flat screen HDTVs, also offered at a regular price of 600 €. The stimuli
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differed in brand, quality, and promotion (Table 2). In each category, we chose two well-known
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national brands that sell at similar prices. We used quality ratings by Stiftung Warentest, a
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consumer product rating agency (similar to Consumer Reports in the U.S.), that is well-known
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and highly respected in Germany. The ratings are on the same scale as German school grades
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(from 1 for “excellent” to 5 for “not sufficient”) and therefore very familiar to consumers. We
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used two levels, 2.3 representing “good” and 2.7 representing “satisfactory”.
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< Insert Table 2 about here >
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A brand may be offered without a promotion, with a certain rebate, or with a conditional
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rebate. We chose three discount levels for the certain rebate – 30, 60, and 90 €, which
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correspond to price reductions of 5, 10, and 15% respectively. Both the absolute amount and
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percentage discount are realistic given values observed in practice (Silk, 2004; Spencer, 2002).
At the time of our survey, a well-known betting website (www.bwin.com) offered odds that
translated to a 20% probability of the Germans winning the 2012 tournament. This “market
wisdom”, based on the assessment of the betting market, is a reasonable proxy for the objective
probability of the event a company might use in its planning. Accordingly, we used a 20%
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probability to compute conditional rebate discounts that are actuarially equivalent to the certain
rebate discount levels. These conditional rebate discount levels are 150, 300, and 450 €.5
In the survey, respondents first saw a scenario description. For TV, it read (for washing
machines, only the category name and product details were replaced):
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“Imagine that today is two weeks before the beginning of the 2012 European Football
Championship (UEFA EURO 2012), which will take place from June 8 to July 1, 2012 in
Poland and Ukraine. Assume that Germany has qualified as section winner and will play
Croatia, Sweden, and France in the first round. Beside the hosts – Poland and Ukraine –
Spain, the Netherlands, Italy, Portugal, England, Russia, Czech Republic, Slovakia,
Denmark, and Slovenia have qualified for the tournament.
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Imagine you have decided to buy a new TV. You have already gathered information
about current TV models and decided that you will buy a 32‘‘ flat screen HDTV from an
electronics retailer close to your home.
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In the following we will show you several pairs of TVs. The TVs differ in brand name,
picture quality, and the type of promotion offered if any. Assume that the regular price of
both TVs is 600 € and they are also identical in all other respects.”
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promotions were described as follows:
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This was followed by a description of the brand, quality, and promotion attributes, in which the
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 “Rebate promotion: Buy this flat screen HDTV before the beginning of the UEFA
EURO 2012. Just mail in your receipt and the company will refund X € to you within
six weeks of receiving your receipt.
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 Conditional promotion: Buy this flat screen HDTV before the beginning of the
UEFA EURO 2012. Just mail in your receipt and if Germany wins the Championship,
the company will refund X € to you within six weeks of receiving your receipt.”
Thus, the description of the promotions is identical except for the condition in the latter.
Next, respondents faced several choice tasks in each of which they chose one product out
of two.6 Figure 2 shows a sample choice task. We created stimuli and conjoint choice sets using
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Our design may inflate the importance of promotions because the number of levels for this attribute is higher
than for the others (Wittink, Krishnamurthi, & Reibstein, 1990), and because our design uses only three
attributes. However, if such a bias exists, it applies to certain rebates and conditional rebates to a similar degree,
and we are more interested in their relative than absolute impacts. We therefore did not increase the number of
levels for other attributes, which would have increased respondent burden.
20
the “complete enumeration” procedure in Sawtooth. This produces a randomized conjoint
design wherein each consumer receives different stimuli and this design accounts for the
principles of (1) minimal overlap, i.e., each attribute level is shown as few times as possible
within a single choice task; (2) level balance, i.e., the levels of an attribute occur with equal
frequency; and (3) orthogonality, i.e., all attribute levels are chosen independently of other
attribute levels (Hennig-Thurau et al., 2007; Huber & Zwerina, 1996). Each respondent
14
completed twelve choice tasks that were used for model estimation. We randomized the order of
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brand presentation to avoid order effects, but always presented product attributes in the same
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order to avoid respondent confusion.
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< Insert Figure 2 about here >
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4.2 Consumer Characteristics
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Respondents indicated their subjective probability of the event occurring by answering
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the following question: “What do you think is the percentage chance that Germany will win the
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2012 European Football Championship? (Please write in a number between 0 and 100)”.
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Respondents also indicated whether they had bought something with a certain or conditional
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rebate during the last three years. Finally, we measured perceptions of the benefits and costs of
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conditional rebates as well as the other consumer characteristics that serve as concomitant
variables in our model. Most items were taken from previously validated scales, as indicated in
6
We did not include a no-choice option to discourage respondents from choosing the “easy way out”. Our choice
tasks are complex, and previous research has shown that task complexity increases the likelihood of choosing the
no-choice option (Dhar, 1997; Tversky & Shafir, 1992). Not having a no-choice option does not pose a problem
for our analysis since we predict market shares not absolute sales levels. Also, choice sets with pairs are less
efficient and realistic than choice sets with more alternatives (Sándor & Wedel, 2002) but they are easier for
respondents to process. This increases internal validity (Louviere et al., 2008) and allows us to use more choice
sets (Zeithammer & Lenk, 2009).
21
Table 3. Participants were asked to indicate their agreement with these statements on a scale
from 1 (strongly disagree) to 5 (strongly agree).
< Insert Table 3 about here >
4.3 Data Collection
The data were collected through an online survey in June 2011. Both certain and
conditional rebates are rather new to the German market, so we do not expect a bias from
14
respondents being more familiar with one type of promotion than the other. One year before the
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event used in our conditional rebate is an appropriate time for data collection. The event was
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sufficiently salient, since the national teams were in the process of qualifying for the tournament,
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and ticket sales had started. Also, the odds of the German team winning were stable and not
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affected by short-term news, e.g., about players’ injuries.
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We specified to the panel provider a sampling frame of adults between the ages of 18 and
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79 with at least a secondary school degree (“Realschule” or higher) in order to assure response
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quality, and asked that our sample reflect the gender and age distribution of the German
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population. This sampling frame accounts for 92% of the online provider’s panel and about 60%
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of the German adult population over 20 years of age (www.destatis.de). Respondents were
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randomly assigned to the washing machine or the TV category.
The samples contained 376 respondents for washing machines and 375 for TVs.
Respondents who took less than 4 minutes, more than 24 hours, or gave straight line responses to
all the choices were deleted as part of survey quality control. The eliminated respondents were
not significantly different from those retained except for a slightly lower education level in the
washing machine sample. Our final sample size is 293 for washing machines and 271 for TV.
4.4 Descriptive Statistics
22
We summarize key characteristics of the two samples in Table 4. The data confirm that
German consumers have low experience with both certain and conditional rebates. Interestingly,
their self-reported probability is highly optimistic. The average (58% / 57%) is much higher than
what is suggested by market wisdom (20%). This is consistent with prior research showing
consumers’ propensity to overestimate the probability of desirable and salient events or engage
in wishful thinking. Such overestimation may be surprising given that we ran our study one year
14
before the event actually took place and construal-level theory suggests that temporal distance
20
activates abstract construal which in turn leads to under-estimation of probability (Trope &
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Liberman, 2003; Wakslak & Trope, 2009). However, psychological distance is just as important
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as temporal distance and the event’s salience is likely to reduce its psychological distance.
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< Insert Table 4 about here >
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5. Results
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We first determine the number of segments for our model using the Bayesian Information
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Criterion (Schwarz, 1978). We find that the BIC is best for the three-segment solution in both
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categories, so we use three segments for all further analyses. In the following, we compare our
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model with simpler benchmark models. We then discuss our parameter estimates and conduct
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market share simulations.
5.1 Comparison with Benchmark Models
We compare our proposed model to three benchmark models nested within it. Since the
key characteristics of the utility function for conditional rebates are subjective probability and
risk aversion, we choose benchmark models with simpler treatments of pc and of the discount
parameters. We start with a model without promotions, i.e., with only brand and quality as
independent variables (model B1). We then add promotion effects in their simplest form, i.e., we
23
use respondents’ stated subjective probabilities directly and constrain the discount parameters to
be equal for certain and conditional rebates (model B2). Next we relax the equality constraint on
the discount parameters (model B3). Finally, we bring in the probability weighting, which leads
to our proposed model.
We estimate all models using 200 randomly selected respondents leaving the rest (93 for
washing machines and 71 for TVs) for hold-out analysis. Table 5 presents in-sample fit and
14
hold-out performance for all the models. We obtained hit rates as follows. For each hold-out
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respondent, we first computed choice probabilities and consequent hit rates in each of the three
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segments. Next, we computed the overall hit rate for the respondent as the weighted mean across
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segments, using the a posteriori probabilities of segment membership as weights. Finally, we
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aggregated this hit rate across all the hold-out respondents.
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< Insert Table 5 about here >
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Our benchmark comparisons confirm that promotions are important – in-sample and
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hold-out log-likelihoods improve significantly when we add promotion to the model (B2 vs. B1).
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The improvement in hit rates is not large – brand and quality are relatively important for
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respondents and already explain a large portion of consumers’ behavior, yielding hold-out hit
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rates of 72.8% (washing machines) and 78.6% (TV). This could be due to the choice tasks being
hypothetical and not incentive-compatible – in practice promotion effects may be larger because
consumers spend and save real money. While this means we may be underestimating the
absolute effectiveness of promotions, it should not influence the comparison between certain and
conditional rebates, which is of key interest.
Next, we make the discount parameters promotion-specific. This significantly improves
in-sample fit (B3 vs. B2). Also hold-out performance becomes better, and the extent of that
24
improvement is meaningful, given that hit rates are already high in a model with no promotion
effects (B1). Finally, we move to our proposed model by adding the weighting of stated
probabilities as specified in Equation 4. Hold-out performance stays rather similar with a slight
improvement for TV, but not for washing machines (PM vs. B3). However, in-sample fit
improves in both categories, so we report results based on our proposed model.
5.2 Model Estimates
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Table 6 provides estimates for the washing machines sample. We find one large segment
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with 58% of respondents, and two smaller segments with 18% and 24%. The estimates of β0
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reflect brand preferences, which vary across the three segments. The estimates of β1 are
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plausible: utility increases with higher quality (β1 > 0), and there is substantial heterogeneity
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across segments. Quality sensitivity is rather high in the largest segment. This is consistent with
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our earlier finding of high hold-out hit rates even without promotion in the model.
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< Insert Table 6 about here >
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For each segment, Table 6 also lists the mean subjective probabilities stated by
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respondents (sp) as well as the transformed p’s based on the probability weighting coefficient α.
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In all three segments the transformed p’s are smaller than the probabilities stated by respondents
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(.49, .47, and .37 vs. .56, .65, and .57). Thus, the transformed p’s are more realistic than the
stated probabilities, but still substantially larger than the .20 suggested by market wisdom. This
is in line with previous research showing that consumers tend to overestimate probabilities of
favorable and salient events, and bodes well for the effectiveness of conditional rebates. In
segment three, the estimated weighting parameter α approaches zero, making the transformed
probability .37, and this segment is not sensitive to the conditional rebate discount. We note that
α would not be identified if the discount parameters were zero. Here, the parameters are non-
25
zero, albeit statistically insignificant, and we did not find any indication of a problem either in
model convergence or in robustness to starting values.
Table 6 also shows the segment-level Arrow-Pratt indices for certain and conditional
rebates, computed by substituting the estimated discount parameters and medium discount levels
(60 € for rebates and 300 € for conditional rebates) into equations (2) and (3) respectively. We
find that respondents are mostly risk averse (R > 0). In segments 1 and 2 the index is similar for
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certain and conditional rebates. In contrast, segment 3 is very risk averse for certain rebates, but
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not for conditional rebates. Recall, though, that the conditional rebate discount parameters are
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insignificant for this segment, implying that consumers do not respond to them.
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Table 7 contains parameter estimates for the TV sample. As for washing machines, we
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find one large segment and two smaller ones of about equal size. Again, brand preference and
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quality sensitivity are heterogeneous across segments, with quality sensitivity being high in the
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largest segment (segment 1). Segment 1 is also not sensitive to either the certain or the
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conditional rebate discount levels. In contrast, segment 2 is sensitive only to conditional rebate
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discounts and segment 3 is sensitive only to certain rebate discounts. Although the discount
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parameters are often insignificant, we still compute the Arrow-Pratt index which indicates that
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consumers are mostly risk averse.
< Insert Table 7 about here >
Segment 3’s stated probabilities do not need to be weighted (α approaches 1). In the
other two segments, as for washing machines, transformed p’s are smaller than stated p’s.
Again, the overall picture is that subjective probabilities (.42, .36, and .48) are smaller than
stated but larger than the .20 suggested by market wisdom. Whether consumers respond better to
certain or conditional rebates is determined by the interplay of subjective probability and risk
26
aversion. Next, we analyze how these effects balance out in the different segments and the
overall market based on market share simulations.
5.3 Market Share Simulations
To gain further insights into the relative effectiveness of certain vs. conditional rebates
and the segment structure, and to demonstrate the value of our model as a managerial tool, we
conduct market share simulations. We take the perspective of a brand manager working on a
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promotion program with a specific retailer, having to choose between offering a certain or
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conditional rebate. We assume the manager knows that a competitor will be offering a certain
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rebate. Brand A is the focal brand and brand B has instituted a certain rebate at a specified
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discount level. If unanswered, this would switch many consumers to brand B. Brand A can
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recoup its baseline share by offering the same rebate discount or a conditional rebate. We ask:
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What discount level for a conditional rebate would brand A have to implement in order to win
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back its baseline share? We call this the “CR equivalent”. Assume the manager believes the
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market-based assessment of the probability of the event (.20 in our case). Let Discr = the certain
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rebate discount. If .20 × CR equivalent < Discr, the expected value of the conditional rebate
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discount that brand A would have to pay is lower than the certain rebate discount necessary to
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recoup market share, indicating that conditional rebates are more effective than certain rebates.
We first simulate a base case in which consumers choose between brands A and B when
they are of equal high quality and neither is on promotion. We use individual parameters to
compute choice probabilities for each respondent and aggregate to obtain baseline market shares.
Next we repeat the simulation assuming brand B offers a certain rebate of a given discount level,
and re-compute market shares. We also confirm that brand A can recoup its baseline share by
offering the same certain rebate. Finally, we determine the conditional rebate discount that brand
27
A would have to offer in order to recoup its baseline market share. We repeat these simulations
for different levels of the certain rebate discount for brand B and for both categories.7
Figure 3 summarizes the results at the segment level. For washing machines, Bosch
served as brand B, which uses a certain rebate, and AEG served as brand A, which uses a
conditional rebate. For televisions, brand B was Sony; brand A Philips. Figure 3 shows the CR
equivalents for different rebate values. For example, in the washing machine category, if brand
#1
< Insert Figure 3 about here >
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of 117 € to recoup its baseline share with consumers in segment 1.
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B (Bosch) offers a 30 € certain rebate, brand A (AEG) would have to offer a conditional rebate
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The pattern of consumer response is similar in the two product categories. In both
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categories, Figure 3 shows that conditional rebates are most effective in segment 2 – CR
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equivalents are lowest here, i.e., the smallest discounts are needed for brand A to recoup its
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market share. The large segment 1, which comprises over half the sample in both categories,
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responds less strongly to conditional rebates, but even for this segment, conditional rebates are
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more cost effective than certain rebates. For example, in the washing machine category, the CR
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equivalent of brand A (AEG) for a certain rebate of 90 € by brand B (Bosch) is 295 €. If the
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manager of AEG assumes the probability of the German team winning the championship is .20
(the wisdom of the market value), then AEG is better off offering a conditional rebate because
the expected discount to be paid out, 59 € = .20 × 295 €, is much less than the 90 € certain rebate
brand A would need to recoup its share. The same pattern holds at other discount levels.
7
Results are substantively similar in simulations where brand B begins by instituting a conditional rebate instead
of a rebate.
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For consumers in segment 3, however, it is not profitable (or even not possible) for brand
A to recover market share with a conditional rebate. The relative values of the discount
parameters are such that for washing machines, the CR equivalents are too high (more than the
regular price of 600 € to offset a certain rebate of 60 or 90 €). For TV we cannot even compute
CR equivalents because a conditional rebate discount of up to 1,200 € is not sufficient to recoup
market share and beyond that point, utility starts to decrease with discount.
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For managers it is important to study CR equivalents at the level of the total market.
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Figure 4 shows these simulations, and reveals that conditional rebates are more cost effective
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than certain rebates at all discount levels. For example, in the washing machine category, the CR
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equivalent of brand A (AEG) for a rebate of 90 € by brand B (Bosch) is 342 €. Thus, the
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expected discount to be paid out for a conditional rebate, 68.40 € = .20 × 342 €, is lower than the
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90 € certain rebate brand A would need to recoup its share. Note that a conditional rebate of 450
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€ has an actuarial equivalent of .20 × 450 € = 90 €. So brand A can offer a conditional rebate
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anywhere between 342 € and 450 € and be better off than if it offered a 90 € certain rebate. It
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can recoup its share with a conditional rebate of 342 € or use a conditional rebate between 342 €
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and 450 € and achieve a higher share at the same expected expense as a certain rebate of 90 €.
< Insert Figure 4 about here >
Lower CR equivalents for TVs show that conditional rebates are more cost effective in
this category than for washing machines. This lends support to the benefit congruency
hypothesis proposed by Chandon, Wansink, and Laurent (2000), that hedonic promotions are
more effective in hedonic than in utilitarian categories. An alternative explanation could be the
higher usage complementarity for TVs than for washing machines – consumers watch soccer on
29
their new TV. This type of fit has been shown to increase the effectiveness of other promotions
like premiums (e.g., Harlam et al., 1995).
Overall, we find that conditional rebates are more cost effective than certain rebates: they
can recoup baseline market share at lower expected cost. Further, they are more effective for
TVs than for washing machines. Note that our calculations do not account for possible
differences in slippage rates (Gilpatric, 2009; Gourville & Soman, 2011) and insurance or other
14
costs for certain versus conditional rebates. However, our analysis allows firms to assess how
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large these differences can be for one or the other type of promotion to be more profitable.
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5.4 Observed Heterogeneity
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The five consumer characteristics – perceived savings benefits, entertainment benefits,
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thinking costs, gambling proneness, and involvement with the event – help explain heterogeneity
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across segments. The bottom halves Tables 6-7 show the impact of these variables on the
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likelihood of being in a particular segment, with segment 3 as the base case.8 To interpret these
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effects, it is useful to recall that for both categories, segment 2 is the most responsive to
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conditional rebates, followed by segment 1, and segment 3 is the least responsive.
In the washing machine category, we find that higher thinking costs make respondents
less likely to be in the conditional rebate prone segments 1 and 2; hence more likely to be in
segment 3, which does not like conditional rebates. Higher involvement with the event and
higher entertainment value makes respondents less likely to be in the large segment 1, and more
8
We use principal component scores for the consumer characteristics to alleviate multicollinearity. Details of the
principal component analysis are available from the authors upon request.
30
likely to either love (segment 2) or hate (segment 3) promotions around the event. In the TV
category, higher thinking costs make respondents less likely to be in segment 1. Higher
perceived savings and entertainment benefits, along with higher gambling proneness, place
respondents in segment 2, the most conditional rebate-prone segment.
Overall, we find that consumer response to conditional rebates is driven by perceived
thinking costs, but also by perceived savings and entertainment benefits as well as event
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involvement and gambling proneness. The hedonic aspect of conditional rebates is at least as
20
important as the utilitarian aspect: In the washing machine category, respondents who have high
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event involvement and associate conditional rebates with high entertainment benefits either love
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or hate these promotions, whereas in the TV category, conditional rebate proneness is associated
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with high entertainment benefits and gambling proneness.
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6. Discussion and Implications
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We have developed and estimated a model for assessing the effectiveness of uncertain
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promotions, focusing on a specific type – conditional rebates. The key features of our model are
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the incorporation of risk aversion and the subjective probability that consumers use in evaluating
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such promotions. We have used the model to generate insights on how conditional rebates work.
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We have compared the effectiveness of a conditional rebate linked to a popular sports event
versus a traditional certain rebate which is the closest analog amongst traditional promotions
without uncertainty. We have done this in the context of choices made by German consumers in
two product categories and have generated several interesting results:
First, our model successfully recovers the choices consumers make when they trade off a
conditional rebate versus a rebate. This is evidenced by hit rates of about 80%, plausible
coefficients, and superiority over several benchmark models. We find evidence that risk
31
aversion is different for conditional vs. certain rebates and that subjective probabilities are
smaller than stated, but much larger than the probabilities suggested by market wisdom.
Second, we find evidence of substantial heterogeneity with respect to response to
conditional rebates. Using a latent class approach, we find similar three-segment solutions for
both televisions and washing machines. There are high, medium, and low segments with respect
to their responsiveness to conditional rebates. We identify consumer characteristics that
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distinguish between the segments and hence drive the effectiveness of conditional rebates:
20
perceived savings and entertainment benefits, perceived thinking costs, gambling proneness, and
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event involvement. These results are plausible, and reinforce the segmented response to
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conditional rebates.
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Third, conditional rebates are more cost effective than certain rebates in that the expected
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cost of the discount required to off-set a competitor’s rebate is lower than the cost of that rebate.
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Finally, while the general pattern of results is similar for TVs and washing machines, conditional
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rebates are more effective for TVs, the more hedonic product. This is consistent with benefit
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congruency (Chandon, Wansink, & Laurent, 2000), but it could also be due to other reasons such
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as higher usage complementarity between TVs and sporting events.
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Our results have important implications for researchers. First, we have developed a
consumer utility model that can be used to evaluate consumer response to uncertain promotions
and shown that the key phenomena to include are the subjective probability of getting the reward
and risk aversion. Second, we add substantively to the literature on uncertain promotions. Some
recent research has argued that positive response to uncertain promotions occurs only in low
stakes situations with relatively small-ticket items, when consumers do not deliberate (Goldsmith
& Amir, 2010) or because consumers want to avoid the pain of paying (Mazar, Shampanier, &
32
Ariely, 2012). None of these situations holds for conditional rebates, which are almost always
offered on big-ticket items and the reward is delayed so the consumer does not avoid the pain of
paying. Yet we find that conditional rebates can work better than certain rebates. This result is
mainly driven by the fact that subjective probabilities are larger than “objective” probabilities
based on market wisdom.
Third, we add to the literature on deal proneness, which suggests that response to
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promotion is highly segmented and segmentation varies by type of promotion (e.g., Schneider &
20
Currim, 1991). Indeed, we find that almost a quarter of the consumers in our sample are not at
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all receptive to conditional rebates, about 50-60% are quite receptive, and the remainder are
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highly receptive. Fourth, we contribute to the literature on benefits and costs of promotions. We
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show that hedonic benefits can be as important as utilitarian benefits for explaining consumer
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response to promotions and the cost of thinking is very relevant, supporting Chandon, Wansink,
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& Laurent’s (2000) call for more work on these costs.
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For managers, our key messages are as follows. First, conditional rebates have a role to
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play in the brand’s promotion mix. We find them to be more cost effective than certain rebates
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in recovering market share when a competing brand uses a certain rebate. Second, promotions
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are a targeting device (e.g., Nies & Natter, 2010), and conditional rebates are no exception. The
segment most attracted by conditional rebates is one that finds promotions less taxing on the
brain. Perceived entertainment benefits, gambling proneness, and event involvement can also
play a role, but this may differ across product categories. Third, in their communication of
conditional rebates, managers should not only emphasize the potential savings, but also how
much fun it is to participate. Fourth, models can be used to show how the market for conditional
rebates is segmented and to simulate alternative promotion scenarios. The analysis here is not
33
overly complex – it requires a conjoint-type survey followed by a model that can be estimated
using maximum likelihood. Our procedure may be especially useful for managers who have
never used conditional rebates before and are seeking guidance before a full roll-out.
Our work has limitations that suggest avenues for future research. First, the conditional
rebates we studied involved a positive event, i.e., a soccer team winning a tournament. In recent
years, companies are also offering conditional rebates as “insurance” against negative events.
14
For example, a German manufacturer of solar heating systems offered a discount if the number
20
of hours of sunshine falls below a certain level. And Pirelli promised a refund of up to 50% on
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the purchase of winter tires, if there are not enough cold days in the next winter. During the
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recent financial crisis, some car manufacturers in the U.S. offered to cover monthly payments for
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a while if the customer lost his/her job. It would be interesting to see the impact of a negative
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external event on the effectiveness of such promotions.
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Second, the event in our analysis was a low but not very low-probability event. It would
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be interesting to study the response to much lower probability scenarios. There are at least two
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reasons why those may be even more effective. One is that consumers overweight very low
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probabilities and exhibit risk proneness when it comes to longshots, i.e., very small chances of
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large pay-offs, as evidenced by the popularity of state lotteries, especially jackpots (Chew &
Tan, 2005). Another is that very low-probability scenarios are often associated with free
products (e.g., Jordan’s Furniture Monster Hit promotion) and there is evidence that consumers
have much higher affect for free offers than for other price offers (Shampanier, Mazar, & Ariely,
2007). In addition, it may be interesting to study medium probabilities, which may be more
attractive for consumers because chances of winning are higher.
34
Third, it would be interesting to compare response to conditional rebates across cultures.
In Germany, neither certain nor conditional rebates are widely used. This was good for the
purposes of our study, since it equalized baseline responsiveness. However, in the U.S., certain
rebates are very common, although widely criticized, so it would be interesting to see whether
conditional rebates would suffer from the negative halo from traditional rebates, or benefit as a
more entertaining form of rebate.
14
Fourth, while we employed realistic scenarios with a real event – the European Football
20
Championship – the purchase situation was hypothetical. As explained above, because our
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measurement approach is not incentive-compatible, it may underestimate the absolute
31
effectiveness of both types of promotions studied. In addition, we may underestimate the
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effectiveness of conditional rebates relative to certain rebates because we do not capture effects
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of the advertising that often accompanies conditional rebates. We also ran our study one year
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before the actual event, so response in our study is probably less emotional than in practice.
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Thus, our findings on the effectiveness of conditional rebates may be conservative, and it would
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be worthwhile to validate them with field data.
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Fifth, we rely on the information in self-stated probabilities and use a probability
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weighting function to transform them into subjective probabilities. Future researchers could
design a study and gather data that permit the direct estimation of consumers’ subjective
probabilities.
Overall, our findings are that conditional rebates can be modeled at the level of the
consumer utility function, response to them is highly heterogeneous, and they provide a viable
alternative to traditional rebates. We urge further research on uncertain promotions in general,
and on conditional rebates in particular as a promising form of promotion.
35
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41
Table 1
EXAMPLES OF CONDITIONAL REBATES
Name of
Company
Year
Product
Category
Country
Nature of External Event
1998
U.S.
Sun rooms, spas,
and gazebos
Sports: Minnesota Vikings win last five
games of year by 7 points or more (football)
BrandsMart
1999
U.S.
Electronics
Sports: Kansas City Chiefs beat San Diego
Chargers on Halloween (football)
Epson
2003
Multiple
Printer
Sports: Home nation wins rugby World Cup
Canandaigua
Wine Company
2003
U.S.
Champagne
Weather: ≥ 4 inches of snow on New Year’s
Day Media Markt
2004
Germany
TV
Sports: Germany wins soccer EURO
Hipercor
2004
Spain
Electronics
Sports: Spain wins soccer EURO
Bayerische
Hypo- und
Vereinsbank AG
2006
(yearly)
Germany
Savings account
Media Markt
2006
Germany
TV
Media Markt
2006
Italy
TV
Ashley Furniture
HomeStore
2007
U.S.
Furniture
Furniture &
Appliance Mart
2007
U.S.
Jordans’
Furniture
2007
U.S.
Springers
Jewelers
2007
World Furniture
Mall
31
#1
20
14
PanelCraft Inc.
IJ
R
M
Vo
l
um
e
Sports: FC Bayern Munich scores/becomes
German soccer champion
Sports: Germany scores in soccer World Cup
Sports: Italy wins soccer World Cup
Sports: Memphis Tigers win NCCA
championship (basketball)
Sports: Green Bay Packers win Super Bowl
(football)
Furniture
Sports: Boston Red Sox win World Series
(baseball)
U.S.
Jewelry
Weather: ≥ 6 inches of snow on Christmas
2007
U.S.
Furniture
Sports: Chicago Bears shut-out Green Bay
Packers (football)
Media Markt
2008
Germany
Electronics
Sports: Germany scores in soccer EURO final
Panasonic
2008
Germany
TV
Sports: Germany wins Olympics gold
PAYBACK
Rabattverein
2008
Germany
Purchases in
partner stores
Sports: Germany wins Olympics gold
Powerade
2008
U.K.
Sports drink
Sports: Great Britain wins medal in randomly
assigned Olympics event
Jordans’
Furniture
2008
U.S.
Furniture
Sports: Boston Red Sox sweep World Series
(baseball)
Stacy Furniture
2008
U.S.
Furniture
Sports: Dallas Mavericks win NBA Finals
(basketball)
Paradise
2009
Canada
Spas, hot tubs
Sports: Saskatchewan Roughriders win Grey
Fo
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in
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Furniture,
appliances 42
Leisurescapes
Cup (football)
2009
U.S.
Golf driver
Sports: Phil Mickelson or Rocco Mediate
wins U.S. Open (golf)
Jordans'
Furniture
2009
U.S.
Furniture
Sports: Boston Red Sox sweep World Series
(baseball)
Simpson
Furniture
2009
U.S.
Furniture
Weather: ≥ 2 inches of snow on January 14,
2010
TomTom
2010
Multiple GPS
Sports: Home nation wins soccer World Cup
Currys
2010
England
TV
Sports: England scores in soccer World Cup
Nationwide
2010
England
Bond
Sports: England wins soccer World Cup
Trafalgar Wharf
2010
England
Boat storage
Sports: Andy Murray wins Wimbledon
(tennis)
Toshiba
2010
Multiple Laptop, TV
Sports: Home nation wins soccer World Cup
Carrefour
2010
France
TV
Sports: France advances at least to semi-final
in soccer World Cup Saturn
2010
France
TV
Sports: France wins soccer World Cup
Media Markt
2010
Germany
TV
Sports: Germany advances at least to round of
last 16 in soccer World Cup Banesto
2010
Spain
Deposit account
Sports: Spain wins soccer World Cup
Media Markt
2010
Spain
TV, projector or
TFT monitor
Sports: Spain wins soccer World Cup without
losing
Pc City
2010
Spain
TV
Sports: Spain scores in soccer World Cup
Slovenian tourist
board
2010
U.K.
Golfsmith
2010
U.S.
Perry’s
Emporium
2010
U.S.
Tom Kadlec
Honda
2010
Trafalgar Wharf
2011
M
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#1
20
14
Golfsmith
IJ
R
Trip to Slovenia
Sports: Slovenia advances at least to
quarterfinal in soccer World Cup Sports: Phil Mickelson wins Masters (golf)
Jewelry
Weather: ≥ 3 inches of snow on Christmas
U.S.
Cars
Weather: ≥ 5 inches of snow on Christmas England
Boat storage
Sports: England wins rugby World Cup
2011
England
Boat storage
Weather: ≥ 1 inch of snow on Christmas
Victor Chandler
2011
U.K.
Sports bets
Sports: Andy Murray wins Wimbledon
(tennis)
Jordans’
Furniture
2011
U.S.
Furniture
Sports: Boston Red Sox player hits homerun
on Jordans' sign (baseball) Mysportworld
2012
Germany
Sports goods
Sports: Germany wins soccer EURO PAYBACK
Rabattverein
2012
Germany
Purchases in
partner stores
Sports: Germany wins Olympics gold
Cadbury
2012
U.K.
Chocolate bars
Sports: Randomly assigned British athlete
wins Olympics medal
Santander
2012
U.K.
Bank account
Sports: Rory McIllroy wins a “Major” (golf) in
m
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Fo
Trafalgar Wharf
g
Golf driver
43
Table 2
ATTRIBUTES IN THE CONJOINT TASK
Washing Machines TV - Bosch
- AEG
- Sony
- Philips
Quality
Rating of washing performance by
Stiftung Warentest:
- Good (2.3)
- Satisfactory (2.7)
Rating of picture quality by
Stiftung Warentest:
- Good (2.3)
- Satisfactory (2.7)
Promotion
-
-
Fo
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co
m
in
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IJ
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M
20
#1
None
Rebate 30 €
Rebate 60 €
Rebate 90 €
Conditional rebate 150 €
Conditional rebate 300 €
Conditional rebate 450 €
31
e
Vo
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None
Rebate 30 €
Rebate 60 €
Rebate 90 €
Conditional rebate 150 €
Conditional rebate 300 €
Conditional rebate 450 €
14
Brand
44
Table 3
MEASUREMENT SCALES FOR CONCOMITANT VARIABLES
Items
Reference
Alpha
Savings benefit
Conditional promotions let me save
money.
… allow me to spend less.
Chandon, Wansink,
and Laurent (2000)
.73
Entertainment benefit
… are fun.
… are entertaining.
Chandon, Wansink,
and Laurent (2000)
.86
Thinking costs
... are hard to figure out.
… require a lot of thinking.
Involvement with event
The performance of the German team in
the European Football Championship
2012 is very important to me.
I am very interested in how well the
German team does in the European
Football Championship 2012.
Gambling proneness
I like to gamble.
I like to take risks.
14
.68
.91
Burton et al. (1998)
.91
Fo
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#1
20
Steenkamp, van
Heerde, and
Geyskens (2010)
45
Table 4
DESCRIPTIVE STATISTICS
TV
(n = 271) Respondents with rebate experiencea
12.6%
15.9% Respondents with conditional rebate
experiencea
6.5%
6.3% 58% (26) 20
Mean stated probability of event
14
Washing Machines
(n = 293) 57% (25) 2.9 (1.1) Mean entertainment perception
2.8 (1.2) 2.7 (1.2) Mean thinking perception
2.5 (1.1) 2.5 (1.1) um
e
31
#1
Mean savings perception
3.2 (1.4) Vo
l
Mean involvement with event
M
Mean gambling proneness
IJ
R
Males
in
g
Mean age
rth
co
m
Mean household income (€/month)
2.9 (1.1) 3.1 (1.4) 2.4 (1.1) 2.4 (1.1) 48.5% 50.7% 45.2 (14.8) 47.7 (15.6) 2,495 (1,261) 2,640 (1,334) 52.7% 43.7% Employed part time
14.1% 16.7% Unemployed
33.2% 39.6% College degree
34.1% 37.0% High school degree (Abitur)
24.6% 21.1% Secondary degree
41.3% 41.9% Fo
Employed full time
Standard deviations are in parentheses where necessary.
a
% of respondents who have bought something that had a rebate (conditional rebate) offer in
the last 3 years.
46
Table 5
HOLD-OUT COMPARISON WITH BENCHMARK MODELS
Model
#
Param.
χ2 a
In-Sample
LL
Hold-Out
LL
Hold-Out
Hit Rate
-522.29
.728
B1: No promotion
(β2, β3, β4, β5 = 0)
18
-1,062.00 B2: Common discount parameters
(β2 = β4, β3 = β5, pc = stated prob.)
24
-968.56 168.88***
(vs. B1) B3: No probability weighting
(pc = stated prob.)
30
-923.02 PM: Proposed model
33
-918.03 14
Washing Machines
.775
91.08***
(vs. B2) -460.04
.793
9.98**
(vs. B3) -468.16
.790
Vo
l
um
e
31
#1
20
-482.55
M
TV
18
-1,053.27 -344.11
.786
IJ
R
B1: No promotion
(β2, β3, β4, β5 = 0)
24
-968.03 170.48***
(vs. B1) -327.99
.811
B3: No probability weighting
(pc = stated prob.)
30
-950.13 35.80***
(vs. B2) -302.38
.827
33
-941.68 16.90***
(vs. B3) -301.17
.830
Fo
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co
m
in
g
B2: Common discount parameters
(β2 = β4, β3 = β5, pc = stated prob.)
PM: Proposed model
Estimation on 200 respondents for each category
Hold-out analysis on 93 respondents for washing machines and 71 for TVs.
a
Likelihood ratio improvement of in-sample LL (nested models); ** p < .05.
***
p < .01.
47
Table 6
MODEL PARAMETERS FOR WASHING MACHINES
β3
Conditional rebate discount
parameter
β5
Conditional rebate discount2
parameter
α
Weighting parameter for stated
probability of event
sp
Mean stated probability
p
Mean transformed probability
REB
R (60) Arrow-Pratt index for rebate of
60€
CR
R (300) Arrow-Pratt index for conditional.
rebate of 300€
η
Segment size
γ0
Constant
rth
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in
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Vo
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β4
Impact of savings benefit
γ2
Impact of entertainment benefit
γ3
Impact of thinking costs
γ4
Impact of involvement with event
γ5
Impact of gambling proneness
*
Fo
γ1
14
Certain rebate discount
parameter
Certain rebate discount2 parameter
20
β2
#1
Quality valuation
-.83***
(.24)
.46 **
(.23)
3.22 **
(1.43)
-1.37
(1.36)
2.92 ***
(.96)
-.30
(.18)
.30 *
(.16)
.65
.47
1.04
31
β1
-.09
(.14)
3.47 ***
(.24)
4.07 ***
(1.34)
-1.99
(1.41)
2.10 ***
(.54)
-.22 **
(.11)
.48 ***
(.18)
.56
.49
1.42
e
Brand preference for Bosch
Segment 2
Estimate
(SE)
um
β0
Segment 1
Estimate
(SE)
1.69
1.61
.58
1.00 ***
(.22)
-.04
(.19)
-.39*
(.20)
-.46 **
(.20)
-.40 *
(.21)
.12
(.20)
.18
-.29
(.43)
.02
(.26)
-.16
(.27)
-.78 **
(.31)
-.15
(.34)
.35
(.30)
p < .10 ** p < .05 *** p < .01
p and η computed based on parameter estimates for α and γ0-γ5 respectively.
Discount divided by 100 to facilitate the estimation.
Segment 3
Estimate
(SE)
.80***
(.15)
.26**
(.11)
4.96***
(.89)
-3.21***
(.96)
-.06
(.39)
.14
(.09)
.00
(.54)
.57
.37
3.48
-1.08
.24
⎯
⎯
⎯
⎯
⎯
⎯
48
Table 7
MODEL PARAMETERS FOR TV
β3
Conditional rebate discount
parameter
β5
Conditional rebate discount2
parameter
α
Weighting parameter for stated
probability of event
sp
Mean stated probability
p
Mean transformed probability
REB
R (60) Arrow-Pratt index for rebate of
60€
CR
R (300) Arrow-Pratt index for conditional
rebate of 300 €
η
Segment size
γ0
Constant
rth
co
m
in
g
IJ
R
M
Vo
l
β4
Impact of savings benefit
γ2
Impact of entertainment benefit
γ3
Impact of thinking costs
γ4
Impact of involvement with event
γ5
Impact of gambling proneness
*
Fo
γ1
14
Certain rebate discount
parameter
Certain rebate discount2 parameter
1.05***
(.14)
.60***
(.16)
4.60***
(1.05)
-2.27**
(1.12)
.60
(.40)
-.05
(.08)
1.00
(.89)
.48
.48
1.45
20
β2
-.82***
(.12)
.48***
(.12)
1.40
(.87)
.16
(.93)
2.82***
(.55)
-.30**
(.12)
.00
(.09)
.62
.37 -.12
#1
Quality valuation
.15
(.22)
3.44 ***
(.28)
1.69
(1.68)
-.96
(1.79)
.95
(.83)
-.07
(.17)
.24
(.38)
.56
.42
2.14
31
β1
Segment 3
Estimate
(SE)
e
Brand preference for Sony
Segment 2
Estimate
(SE)
um
β0
Segment 1
Estimate
(SE)
.79
1.76
1.00
.51
.90 ***
(.25)
.18
(.21)
.30
(.21)
-.41 **
(.20)
.10
(.21)
.00
(.21)
.26
.13
(.28)
.46*
(.24)
.42*
(.24)
-.25
(.24)
.40
(.25)
.38*
(.23)
.23
⎯
p < .10 ** p < .05 *** p < .01
p and η computed based on parameter estimates for α and γ0-γ5 respectively.
Discount divided by 100 to facilitate the estimation.
⎯
⎯
⎯
⎯
⎯
Certain Reward
49
Temporary
price reductions
Multi-item promotions
Figure 1
Traditional mail-in rebates
TYPES OF PROMOTIONS
Coupons
Free gifts/samples
ate Reward
Delayed Reward
Contests
Instant win sweepstakes
Sweepstakes
14
Tensile promotions (X% - Y% off)
#1
20
Conditional rebates (Buy now, get
Y% off if Z happens)
Fo
rth
co
m
in
g
IJ
R
M
Vo
l
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31
Uncertain Reward
Which HDTV would you rather buy?
50
Fo
rth
co
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in
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IJ
R
M
Vo
l
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31
#1
20
14
Figure 2
EXAMPLE OF A CHOICE TASK
51
Fo
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31
#1
20
14
Figure 3
MARKET SHARE SIMULATIONS: SEGMENT LEVEL
Note: TV Segment 3 is not shown because its discount parameters are such that even very high CR discounts cannot recoup base
market share.
52
Fo
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co
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in
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Vo
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m
e
31
#1
20
14
Figure 4
MARKET SHARE SIMULATIONS: OVERALL SAMPLE
Note: All segments are included in these simulations.
53
Appendix
MODEL IDENTIFICATION SIMULATIONS
Simulation 1
Segment 1
Est.
TV
SV
Est.
TV
SV
Est.
-.08
-1.19
-.04
√
-.78
-.24
-.62
√
.88
1.17
.97
√
β1
3.41
4.18
3.63
√
.36
-.22
.27
√
.24
.26
.40
√
β2
3.88
6.61
4.14
√
3.65
5.36
3.73
√
4.98
6.84
4.61
√
β3
-1.82
-1.22
-1.82
√
-1.77
-3.07
-1.84
√
-3.23
-2.06
-3.25
√
β4
2.09
2.16
2.58
2.55
4.00
2.37
√
-.07
-.56
-.72
√
β5
-.22
-.44
-.42
-.22
.29
α
.20
1.40
.13
√
.33
γ0
.96
.26
1.07
√
-.19
√
.13
.45
.00
√
-.78
.22
√
.00
-1.43
.00
√
-.94
.09
√
⎯
⎯
⎯
Vo
l
um
-.42
e
#1
20
β0
14
SV
Segment 3
31
TV
Segment 2
R
M
Simulation 2
TV
SV
Est.
√
2.00
3.51
1.96
Segment 3
TV
SV
Est.
√
-2.00
-2.38
-2.15
√
.00
1.23
β1
.00
.92
.00
√
-2.00
-3.80
-2.00
√
2.00
2.30
1.93
√
β2
2.00
1.99
2.01
√
3.00
2.89
3.17
√
.00
-1.41
.06
√
β3
-1.00
.16
-.97
√
-2.00
-1.02
-1.96
√
.00
-.51
.06
√
β4
2.00
1.11
1.91
√
-3.00
-2.88
-3.66
√
2.00
1.42
2.21
√
β5
-1.00
-2.07
-1.00
√
.00
1.20
.44
-1.00
-2.05
-.76
α
.50
1.00
.55
√
.10
-.40
.23
√
.01
-.03
.00
γ0
.80
.87
.78
√
-.80
-2.14
-.84
√
⎯
⎯
⎯
m
β0
Fo
.03
g
Est.
in
SV
Segment 2
rth
co
TV
IJ
Segment 1
√
TV = true value; SV = starting value; Est. = estimate; √ = estimate within 1 std. error of the true value.