International Journal of Market Research Vol. 52 Issue 4
The effects of product-harm crisis on
brand performance
Baolong Ma
Beijing Institute of Technology
Lin Zhang
Truman State University
Fei Li
Tsinghua University
Gao Wang
China Europe International Business School
The purpose of this paper is to offer a better understanding of the effects of
product-harm crisis on a brand’s performance and market structure. This research
is based on panel data on milk powder sales during the Nestlé product-harm
crisis in China. The NBD-Dirichlet model is used to evaluate the performance
of Nestlé and other leading milk powder brands before, during and after the
crisis. Our data show that product-harm crises disturb the market structure and
change customer behaviour. While a product-harm crisis had a negative effect on
Nestlé’s brand performance, it created opportunities for other brands. Overall,
our analysis shows that the NBD-Dirichlet model is a valid tool for monitoring
the performance changes of both crisis brand and other non-crisis brands during
a product-harm crisis. The managerial implications are also discussed.
Introduction
The term ‘product-harm crisis’ refers to well-known events related
to product defects or harm associated with some brands (Siomkos &
Kurzbard 1994). For example, in 2000, when news spread that more than
100 people had died in accidents involving defective Firestone tyres, the
company had to recall millions of its products (Advertising Age 2000). In
September 2008, 6244 babies in China were diagnosed as suffering from
numerous ailments after ingesting the poisonous Sanlu formula (People
Daily 2008). Having used all of its cash reserves for product recall and
Received (in revised form): 26 February 2010
© 2010 The Market Research Society
DOI: 10.2501/S1470785309201399
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The effects of product-harm crisis on brand performance
medical payments, Sanlu Company was declared bankrupt in 2009. In
2005, Nestlé Company mistakenly sold milk powder that contained more
iodine than Chinese national standards permit in the market. When the
news broke, Nestlé had to make a public apology and remove the defective
product from shelves (ABC News 2005). After this crisis, in an effort to
restore reduced sales, Nestlé gave out samples and stationed doctors in
Beijing supermarkets to deal with customers’ concerns.
These examples clearly suggest that a product-harm crisis can seriously
imperil a brand’s performance – and even totally destroy a company,
as in the case of Sanlu. Therefore, understanding how a product-harm
crisis influences brands and the market structure is of great practical and
theoretical interest.
The NBD-Dirichlet model (Ehrenberg 1988) is a well-established
statistical model and has been extensively used to audit and predict brand
performance measures (BPMs) under stationary and dynamic market
structures (Ehrenberg et al. 2004). However, this model has not been used
to analyse the influence of product-harm crises. Since a product-harm
crisis can greatly affect brand performance, it is reasonable to assume
that, by monitoring the BPMs through the product-harm crisis, we can
indirectly observe how a product-harm crisis influences brands and the
market structure of this product category. This approach should provide
some practical benefits from a new perspective.
Our main objective in this paper is to use the NBD-Dirichlet model to
monitor the leading brands’ BPMs during the 2005 Nestlé product-harm
crisis that occurred in China. By comparing the observed BPMs of the
pre-, during and post-crisis periods to those expected, and looking at
the differences between these three periods, we may come to understand
how a product-harm crisis influences brands (including both crisis brand
and non-crisis brands) and market structure. Limitations and managerial
implications are discussed.
The product-harm crisis and its influence on the brand
After a series of product-harm crises over the years, related studies have
been developed in a number of research fields. It has been widely accepted
that product-harm crises have a negative influence on crisis-brand equity
(Heerde et al. 2007). For example, a brand under crisis may lose its
baseline sales and become more sensitive to a competitor’s market activities
(Heerde et al. 2007). The crisis may also affect the crisis brand’s stock
price (Salin & Hooker 2001). Compared to limited systematic research
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International Journal of Market Research Vol. 52 Issue 4
into crises’ market sequences, past research has focused on consumers’ and
firms’ reaction towards such crises (Dawar & Pilluta 2000).
Depending on how it is conducted, most research in connection with
product-harm crises can be classified into three categories. The first
consists of descriptive checklists suggesting which strategies work or do
not work in terms of business practices (Mitroff 2004). Because this kind of
research cannot quantify the damage incurred, it can only provide limited
guidance for understanding the underlying mechanisms of product-harm
crises. Another stream of such research focuses on laboratory experiments. Equipped with psychological theories and different control variables, this
kind of research can help us understand the moderators that play a part
in influencing a product-harm crisis’s effects, and perhaps provides some
valuable insights (Dawar & Pilluta 2000; Vassilikopoulou et al. 2009). However, external validity is one limitation of such research.
Another research stream has recently grown in popularity, where
panel data gathered during real product-harm crises have been collected
and analysed using advanced mathematical models (Heerde et al. 2007;
Cleeren et al. 2008). How the crisis influences the performance of the crisis
brand or the affected product category can be tracked by monitoring the
brand’s stock price (Salin & Hooker 2001) or sales (Heerde et al. 2007). By all accounts, this research stream provides more practical insights
about the influence of the product-harm crisis.
This study contributes to the third research stream in that we used the
NBD-Dirichlet model to quantify the product-harm crisis’s effect on BPMs
(including penetration, market share, purchase frequency and share of
category requirement) and market structure during the 2005 Nestlé milk
powder crisis.
The NBD-Dirichlet model
The NBD-Dirichlet model was developed by Chatfield and Goodhardt
(1975), and improved by Ehrenberg (1988). This model is a statistical
model based on two well-established assumptions: customers are thought
of having steady habitual personal purchase propensities, and brands are
characterised with their purchase probabilities and their market shares. In
a word, it is based on a stationary market structure. This model has been
widely used to describe observed brand performance patterns, and helps to
explain and predict them (Kahn et al. 1988; Ehrenberg et al. 2004; Rungie
& Goodhardt 2004). With limited numerical inputs, the NBD-Dirichlet
model can be used to predict BPMs for particular brands if they are under
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The effects of product-harm crisis on brand performance
a stationary market (see Table 1 for detailed information). The model and
the empirical generalisations associated with them have been widely tested
and supported in marketing for over 30 years across European, US, Asian
and Australasian markets (Uncles, Ehrenberg & Hammond 1995).
Although the NBD-Dirichlet model is based on a stationary market
assumption, this does not limit its application only to steady markets
(Ehrenberg et al. 2004). For example, the model has been used to audit the
performance of established brands (Ehrenberg et al. 2004), as well as niche
and ‘change-of-pace’ brands (Kahn et al. 1988). It has been used to predict
and evaluate the performance of new brands (Schmittlein et al. 1987;
Ehrenberg & Goodhardt 2001), as well as access brands’ performance
under price promotion (Ehrenberg et al. 1994) and loyalty programmes
(Sharp & Sharp 1997; Meyer-Waarden & Benavent 2006). However, this
model has not been applied to product-harm crisis analysis, although past
research has shown that it would be a valid tool under such dynamic
circumstances.
In our research, we used the NBD-Dirichlet methodology to estimate
theoretical BPMs, and compared them with those observed measures. Because of the product-harm crisis, we would expect that the market would
no longer be stationary. As a result, there would be some discrepancies
between the theoretical, or predicted, BPMs and the observed values
(Meyer-Waarden & Benavent 2006). These discrepancies may indicate
how the crisis ‘disturbed’ the stationary market (Ehrenberg et al. 2004)
and affected these brands.
Table 1 Brand performance measures
Indicators
Explanation
Market shareThe percentage of the total sales of a given type of
product that is attributable to a given company
PenetrationThe proportion of the product category customers who
buy a special brand at least once in a given period
Purchase frequencyThe average purchase rate for those consumers who buy
this brand over a special period; this can indicate the
customers’ loyalty
Share of category requirements (SCR)The brand’s share among the group of consumers who
bought the brand at least once during a special period
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Data and methodology
The Retailing Research Center of Tsinghua University provided the panel
data used in this analysis, which includes purchase data on 53 product
categories from 336 families in the Beijing area during the period from
January 2004 to December 2005. These data recorded the detailed
purchase information of each product category (for example, brand name,
purchase volume, purchase price, purchase time and purchase locations). Our analysis shows that there were more than 70 milk powder brands in
the market during this period. However, for the purposes of this study,
we focused only on four leading brands: Yili, Nestlé, Wondersun and
Mengniu.
The Nestlé milk powder incident began on 25 May 2005 (ABC News
2005), and we can assume that the market before May 2005 was stationary. We first used the 12 months’ data before the crisis (i.e. April 2004–April
2005) to fit the NBD-Dirichlet model, and predict the normal brand
performance and market structure. We then focused on data from three
distinct three-month periods (pre-crisis – 25 February 2004 to 24 May
2005; during-crisis – 25 May 2005 to 24 August 2005; and post-crisis –
1 October 2005 to 31 December 2005). Theoretical BPMs (penetration,
purchase frequency, share of category requirements (SCR), etc.) from
each period were estimated using the fitted NBD-Dirichlet model, and
these estimated values were then compared to those observed values. This
enabled us to detect whether there were deviations due to the productharm crisis. When determining whether the deviations were significant or
not, we used the standards from Fader and Schmittlein (1993): +/– 3%
for market share, +/– 3% for penetration, +/– 0.3 for purchase frequency,
and +/– 3% for SCR. For example, if the theoretical SCR is different from
the observed SCR by more than 3%, we determined that the discrepancy
is significant.
The three-month period was chosen based on the following factors. First,
we wanted to set the period as short as possible, so that the companies
could quickly track the transitions of BPMs. Besides, it is impractical for
companies to wait for a long time before they can make any estimation of
the crisis’s influence. Second, according to Ehrenberg et al. (2004), the base
analysis period for BPMs should be greater than the average inter-purchase
interval. Since our data show that the average inter-purchase interval
of milk powder is around 1.3 months, a three-month period should be
appropriate. Third, the extensive media coverage on this product-harm
crisis extended from 25 May to the end of August 2005. Therefore, we
think the period from 25 May 2005 to 24 August 2005 should be one
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The effects of product-harm crisis on brand performance
when the market was greatly disturbed by the crisis. Fourth, the BPMs are
dependent on the period. For example, a brand’s penetration will be much
higher in a year than in a week. In contrast, SCR will be lower in a longer
period (Ehrenberg et al. 2004). To minimise the error when comparing the
BPMs across different periods, we chose the same duration for each period.
Results
Model fitness and brand performance measures before the crisis
Table 2 presents four key brand performance measures based on 12 months’
data before the crisis (i.e. April 2004–April 2005). These theoretical
BPMs match very well with the observed values (the correlations between
observed and theoretical values are 0.99 for market share, penetration
and SCR, 0.92 for purchase frequency). This result shows that this was a
stationary market.
The observed purchase frequencies and penetrations of four leading
brands are represented in Figure 1, where the x-axis marks the penetration
while the y-axis indicates the purchase frequency. The estimated values
from the NBD-Dirichlet model are also marked in Figure 1. The straight
line in the figure connecting the observed values represents a least squares
regression line using the observed penetration and purchase frequency. The other line connects the theoretical values. The points in Figure 1
clearly mark the marketing positions of each brand. It is obvious that a
small brand has fewer buyers (lower penetration) than larger brands, and
its customers tend to buy it less frequently (less purchase frequency); this
is the so-called ‘double jeopardy’ phenomenon (Ehrenberg et al. 1990). It is also clear that the variation on the observed penetration and market
Table 2 Part BPMs from the 12-month period before the crisis
Nestlé
Brand
Yili
Wondersun
Mengniu
T
O
T
O
T
O
T
O
Market share (%)
13.45
12.00
13.30
11.83
7.23
7.69
5.41
6.59
0.99
Penetration (%)
23.90
24.86
23.67
24.29
13.70
14.00
10.44
10.29
0.99
1.9
1.61
1.9
1.67
1.78
1.59
1.75
1.48
0.92
38.13
40.61
38.05
35.89
34.99
32.58
34.10
37.04
0.99
Purchase frequency
SCR (%)
Correlation
Notes: T = theoretical value; O = observed value; the correlation is based on all brands in this product
category (more than 70 brands).
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International Journal of Market Research Vol. 52 Issue 4
3.0
2.8
y = 0.009x + 1.419
R2 = 0.717
2.6
2.4
2.2
Purchase frequency
2.0
Mengniu
1.8
Yili
Wondersun
Nestlé
Yili
1.6
1.4
Mengniu
Nestlé
Wondersun
1.2
1.0
Observed values
0.8
Theoretical values
0.6
Linear regression values
0.4
0.2
0
7
9
11
13
15
17
19
21
23
25
27
Penetration (%)
Figure 1 Double jeopardy in the pre-crisis market
share among different brands were greater (for example, the penetration
from maximum 24.86 of Nestlé to minimum 10.29 of Mengniu, a ratio of
2.4 to 1), while the purchase frequency and SCR variation were relatively
smaller (for example, the observed purchase frequencies are all within the
1.57 +/– 0.1 range). This is consistent with previous findings (Ehrenberg
et al. 2004).
We also used the NBD-Dirichlet model to analyse the three-month precrisis data. Both the predicted BPMs and the observed BPMs from this
period are listed in Table 3. Clearly, there were no significant deviations
between theoretical measures and the observed measures, and this shows
that the market was stationary. This also indicates that choosing a threemonth period as the basic period is appropriate. Similar to the one-year
period data, the market share and penetration differed greatly among four
brands, while the purchase frequencies and SCRs had tighter distribution.
From observed values during this period, Yili and Nestlé had greater
market share than the other two brands. Yili also had the highest purchase
frequency and SCR, although some of the margin was not significant.
Comparing the BPMs from the one-year period and those from the
three-month period, we found that the penetrations of four leading brands
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The effects of product-harm crisis on brand performance
Table 3 Part BPMs from the pre-, during and post-crisis periods (each period lasts three
months)
Nestlé
Brand
Yili
T
O
Before
9.36
9.19
8.13
Market share (%) During
4.34
3.86
10.93
5.74
5.17
Before
11.83
During
7.39
After
Before
During
After
Penetration (%)
Purchase
frequency
SCR (%)
After
T
Wondersun
O
Mengniu
T
O
T
O
10.29
8.85
7.35
5.48
5.25
12.63
9.28
7.37
8.12
6.32
8.60
9.59
8.27
6.64
7.68
5.90
12.57
10.33
10.86
11.21
12.00
7.06
7.43
7.62
17.73
18.10
15.23
16.19
13.45
14.29
9.27
9.57
13.62
13.91
13.12
13.91
12.24
13.04
1.72
1.59
1.71
1.96
1.72
1.43
1.69
1.54
1.58
1.38
1.66
1.89
1.64
1.24*
1.62
1.20*
1.46
1.27
1.49
1.63
1.48
1.13*
1.48
1.17*
Before
55.80
53.03
55.34
57.33
55.61
55.90
54.38
56.67
During
42.20 32.36*
45.05 62.07*
44.32
45.65
43.81
41.00
After
45.84 41.85*
47.00 59.09*
46.87
49.25
46.63
45.56
Notes: T = theoretical value; O = observed value; * indicates that there is significant difference between
the observed and the theoretical indicators.
from the one-year period were greater than those from the three-month
period. On the other hand, the SCRs from the one-year period were
smaller than those from the three-month period. This is consistent with
previous findings (Ehrenberg et al. 2004).
Brand performance measures during the crisis
To check how much the brands were affected by the product-harm crisis,
we also used the model to analyse the during-crisis period data. Both the
predicted BPMs and the observed BPMs are listed in Table 3.
Obviously, there were significant deviations between the predicted
measures and the observed measures (see Table 3). For example, the
observed SCR of Nestlé was 9.84% less than the theoretical value, while
the observed SCR of Yili showed a positive deviation of 17.02%. In
addition, the observed purchase frequencies of Mengniu and Wondersun
were significantly lower than those predicted values (by 0.4 and 0.42
respectively). It is clear that the Nestlé product-harm crisis disturbed the
market and the market was not stationary during this period.
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International Journal of Market Research Vol. 52 Issue 4
During the crisis, Yili had the highest market share, penetration and
SCR, while Nestlé was the lowest among these four brands. Yili also led in
terms of purchase frequency, while the other three brands had very close
purchase frequencies.
Brand performance measures after the crisis
During a product-harm crisis, extensive media coverage draws public
attention and therefore the crisis has maximum effect on the market
structure. However, after a period of time, the media will reduce its
coverage of the crisis, the crisis’s influence on the market will decline
and the market will probably shift to another stable condition. How the
brands perform under this new market structure is of great interest. As one
of our efforts to shed light on this question, we used the NBD-Dirichlet
model to analyse the three-month post-crisis data. The results are listed
in Table 3, which shows that the observed BPMs had some deviations
from the expected measures. These deviations are similar to those in the
during-crisis period. For example, the observed SCR of Nestlé and Yili
showed –3.99% and 12.09% deviation respectively. On the other hand,
the observed purchase frequencies of Mengniu and Wondersun were 0.31
and 0.35 less than the prediction, respectively. Clearly, four months after
the outbreak of Nestlé’s product crisis, the market was still not steady.
During this period, Yili maintained the highest market share, while Nestlé
had the lowest market share. However, the difference between Nestlé and
Wondersun or Mengniu was insignificant. In terms of penetration, Nestlé
was still the lowest, but the other three brands were all very close to one
another. Yili maintained the highest purchase frequency and SCR, while
Nestlé had the lowest SCR.
Compare brand performance measures across three different periods
To understand how the product affects the brand performance of the
leading brands, we put the BPMs of these brands from the three periods
(pre-, during and post-crisis) together in Table 3. Because the market was
not steady in the during and post-crisis periods, we focused only on the
observed BPMs when comparing these brands.
In terms of market share, Nestlé experienced significant changes across
these three periods: it had 9.19% market share in the pre-crisis market, it
dropped to 3.86% during the crisis and recovered to 5.17% in the postcrisis period. This indicates that the product crisis resulted in a market
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The effects of product-harm crisis on brand performance
share shrink for the crisis brand, and the market share recovered slowly
after the product crisis but was still less than the pre-crisis level. At the
same time, the other three brands all experienced insignificant changes
– that is, they increased from pre-crisis levels to during-crisis levels, and
eventually dropped back in the post-crisis period.
Examining the penetration of the four brands during these three periods,
we found that they all experienced significant changes. For example,
following the trend of its market share, Nestlé’s penetration was 12.57%
in the pre-crisis period; it dropped to 7.62% during the crisis and recovered
to 9.57% in the post-crisis period. On the other hand, the penetration of
the other three brands experienced similar transitions, increasing from
the pre-crisis period to the during-crisis period, then dropping to an
intermediate level (between the pre-crisis and during-crisis periods) in the
post-crisis period.
One interesting finding is that the purchase frequencies of the four
brands dropped from the pre-crisis period to the during-crisis period,
and kept dropping into the post-crisis period. Although each step of
the changes was insignificant, if we compare the post-crisis level to the
pre-crisis level, we find that all four brands displayed a significant or
marginally significant drop. Overall, Nestlé did not seem different from
the other three brands.
The SCRs of these four brands experienced a totally different transition
than the other three BPMs. For example, Nestlé, Mengniu and Wondersun
all experienced SCR drop from the pre-crisis period to the during-crisis
period; and all recovered somehow in the post-crisis period. The only
exception is Yili: its SCR increased from 57.33% in the pre-crisis period
to 62.07% in the during-crisis period and eventually dropped back to
59.09% in the post-crisis period.
To better display the performance transition from the pre-crisis period
to the post-crisis period, the penetrations and purchase frequencies of
these four brands during these three periods are all plotted in Figure 2. Two things are obvious: first, this crisis harmed Nestlé’s penetration
greatly, while the other three brands benefited to some extent from this
crisis; second, the purchase frequencies of each brand and even the entire
product category fell after this crisis. However, Yili always had the leading
purchase frequency while the other three brands’ purchase frequencies
were close to one another.
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International Journal of Market Research Vol. 52 Issue 4
2.2
Nestlé (N)
Ybefore
Purchase frequency
2.0
Wondersun (W)
Yduring
1.8
Mbefore
Wbefore
1.4
Nduring
Nafter
1.2
1.0
Mafter
7
Mengniu (M)
Yafter
Nbefore
1.6
Yili (Y)
9
11
Mduring
Wduring
Wafter
13
15
17
19
Penetration (%)
Figure 2 The performance transitions from the pre-crisis period to the post-crisis period
Discussion
This study focused on the Nestlé milk powder crisis that occurred in China
in 2005. Based on panel data from 336 families in the Beijing area, we
studied the effects of a product-harm crisis on brands and market structure
using the NBD-Dirichlet model. We found, first, that the market structure
in the pre-crisis period was stationary. The product-harm crisis disturbed
the balance, and the market during the crisis was no longer steady. As a
consequence, the predictions of purchase frequencies and SCRs were not
consistent with the observed measures. Four months after the outbreak
of the crisis, the overall market was still not back to stationary status
and there were some deviations of SCRs and purchase frequencies. This
indicates that the crisis’s influence on the market still existed, and it might
last for some time. However, it is interesting to point out that the observed
market share and penetration seemed to match the predicted measures
in all three periods. When the market was not steady in the during-crisis
and the post-crisis periods, detailed data show that in most situations
the observed purchase frequencies (Mengniu and Wondersun) or SCRs
(Nestlé) were significantly lower than the estimated measures. This may
indicate that Mengniu and Wondersun acquired some new customers from
Nestlé, and these new customers did not commit heavy purchases to these
brands. (This is explained in more detail in the following paragraph.) Our
data also show that, among those non-crisis brands, Yili was an exception,
with higher than expected SCR in both the during and post-crisis periods. In addition, the observed purchase frequencies of Yili in the during and
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The effects of product-harm crisis on brand performance
post-crisis periods were always greater than the predicted measures, even
though the difference was less than 0.3. This may relate to its market
position: in both periods, Yili had the highest market share, with more than
3% margin, while its penetration was similar to that of its competitors.
Second, a product-harm crisis hurts the crisis brand greatly. Our data
clearly showed that the market share and penetration of Nestlé dropped
significantly during the product crisis. Even though these measures
recovered during the post-crisis period, they were still significantly lower
than their pre-crisis levels. On the other hand, those non-crisis brands
benefited from this crisis with significantly higher penetration, although
our data also show a trend of returning to their pre-crisis levels. The
decreased market share of Nestlé redistributed to all other brands (more
than 70 brands), and each of them took only a part of it. Therefore, no
significant change of market share was seen in the non-crisis brands.
Third, the crisis’s influence on purchase frequency and SCR of the crisis
brand was not significantly different from those of the non-crisis brands. In terms of purchase frequency, this similarity lies in two aspects: in all
three periods, Nestlé’s relative purchase frequency was always the same
(significantly lower than Yili but marginally higher than the other two
brands); and during the transition from the pre-crisis period to the postcrisis period, all four leading brands dropped by around 0.3. Similarly,
the observed SCRs of Nestlé, Mengniu and Wondersun all experienced
a similar drop from the pre-crisis period to the during-crisis period, and
recovered to some extent in the post-crisis period. Previous research shows
that new brands garnered customer loyalty immediately after they were
introduced (Ehrenberg & Goodhardt 2001). Our data further show that
even a product-harm crisis will not hurt the crisis brand’s customer loyalty.
Overall, Nestlé’s penetration and market share decreased dramatically,
while other brands experienced an increase in penetration at the same time. Associated with this transition, the purchase frequencies and SCRs of most
leading brands dropped significantly. Combining these observations, one
reasonable explanation can be given. The product-harm crisis drove some
of Nestlé’s customers away to other brands. These switched customers did
not form stable purchase behaviour – in other words, they did not limit
their purchases to any one special brand. These switched customers were
somehow like ‘change-of-pace’ customers (Kahn et al. 1988). Even though
the non-crisis brands attracted some new customers and increased their
market share or penetration, the average loyalty of their new customer
group was low. As a result, their purchase frequencies or SCRs became
even lower. This combination of higher penetration and lower loyalty is
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International Journal of Market Research Vol. 52 Issue 4
similar to what happens to change-of-pace brands (Kahn et al. 1988) or to
normal brands under deep price cuts (Bhattacharya et al. 1996).
Finally, we may need to pay particular attention to Yili. Yili was special
in many aspects. First, it always had the highest market share in all three
periods. Second, in both the during and post-crisis periods, its observed
SCRs were significantly higher than the estimated SCRs. Third, its observed
SCR increased from the pre-crisis period to the during-crisis period, while
all the other brands experienced SCR drop. Fourth, its observed purchase
frequency always led the market with a significant margin. All these may
relate to one fact – that Yili was the biggest brand in the pre-crisis market. Our research indicated that, even though all non-crisis brands benefited
from Nestlé’s crisis, different brands gained differently; the most dominant
brand gained more in terms of market share, even increasing SCR, while
the smaller brands had smaller gains in market share and also had to
sacrifice SCR. This looks like another ‘double jeopardy’ smaller brands
may have to face in reality. Interestingly, according to Heerde et al. (2007),
smaller brands are more damaged than bigger brands after a productharm crisis. Our research is consistent with that of Heerde et al. (2007) in
supporting that it is always good to be a bigger brand and it is always bad
to be a smaller brand.
Conclusion
To the best of our knowledge, this study is one of the first attempts to
apply the NBD-Dirichlet model to investigate the effects of a productharm crisis. This paper provides a methodology by which to assess the
impact of a product crisis in a quantitative way, and applies the model
to a product-harm crisis for Nestlé milk powder in the Chinese market. The research shows that this model is a great tool by which to monitor
or track the development of a product-harm crisis. The modelling and
estimation processes are straightforward, and the required data can easily
be collected. Using this model helps us to monitor the crisis’s influence on
the crisis brand, non-crisis brands and even the entire category market.
Our research shows that a product-harm crisis can greatly damage the
crisis brand’s market share and penetration. However, purchase frequency
and SCR were not damaged at similar levels. These combined data indicate
that the market was not steady, and a product crisis changed customers’
purchase behaviours. More specifically, a product-harm crisis drove away
some customers of the crisis brand and these switched customers did not
make up their minds about which brand to eventually switch to. The
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periods during and after the crisis were critical to both of the crisis brands
and non-crisis brands. For crisis brands, they need to maximise their
efforts to attract back this group of customers before they commit to other
brands. However, for non-crisis brands, even though they had improved
their market share and expanded their customer base, they should realise
that this is just a temporary improvement. They need to try their best
to keep these new customers satisfied, and eventually make them loyal
customers. Their strategies and practices during this period will eventually
affect whether they can keep these new customers.
Limitations
This study has its limitations. First, it used data only from 336 families
in the Beijing area, so probably cannot represent the entire population. Future research collecting data from a more representative population
might provide greater insights. Second, our data covered just seven
months after the outbreak of the crisis, and the market was still not
steady. Future research with a longer period of data might provide more
detailed information. Third, we focused only on the product category
of milk powder, which is characterised by high purchase frequency. Therefore, caution needs to be exercised before the conclusions in our
study are applied to other products with lower purchase frequency (cars,
for example).
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About the authors
Baolong Ma is Assistant Professor of Marketing at Beijing Institute of
Technology and a primary researcher of The Retailing Research Center
at Tsinghua University. He works primarily in the areas of customer
relationship management, brand management and crisis management and
has written numerous scholarly articles on topics in these areas.
Lin Zhang is Assistant Professor of Marketing at Truman State
University. She completed her Ph.D. in Marketing at Mississippi State
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University in 2006. Her research interests include brand management,
comparative advertising and unhealthy consumption behaviour. Her
research has been published in journals such as Marketing Management
Journal and Management Research News and AMA proceedings.
Fei Li is Professor of Marketing at Tsinghua University.
Gao Wang is Professor of Marketing at China Europe International
Business School.
Address correspondence to: Lin Zhang, School of Business, Truman State
University, Kirksville, MO 63501, US.
Email: linzhang@truman.edu
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