CO-CREATING BENEFITS IN SOCIAL MEDIA CONTESTS AND ITS EFFECTS ON
PURCHASE BEHAVIORS
Edward C. Malthouse, Northwestern University, USA
Mark Vandenbosch, University of Western Ontario, Canada
Su Jung Kim, Northwestern University, USA
Corresponding Author
Edward C Malthouse
Northwestern University
1870 Campus Drive
Evanston, IL 60208 USA
ecm@northwestern.edu
+1-847-467-3376CO-CREATING BENEFITS IN SOCIAL MEDIA CONTESTS AND ITS
EFFECTS ON PURCHASE BEHAVIOR
ABSTRACT
This paper examines how participating in social media contests in which the consumer is asked
to co-create the core brand benefit affects subsequent purchase behaviors. Using data from
Canada's Air Miles Reward Program and three separate contests, we find that participation
increases mile accumulation behavior over a control group and after controlling for pre-contest
behavior. We propose an explanation for the effect, and discuss its implications and applications.
INTRODUCTION
The Internet and social media are enabling many new forms of “advertising.” The first decade or
so of Internet advertising–largely consisting of display “banner” ads and email blasts and later by
search–followed the approach of traditional print, broadcast and direct advertising, where the
advertiser “exposes” passive eyeballs to some message, perhaps with a “call to action” inviting a
click. The rise of social media and the proliferation of mobile devices are enabling new forms of
advertising that are more participatory and interactive. While in the past a firm would decide on
the positioning of its brand and then use advertising to broadcast messages conveying the brand
meaning, many firms are experimenting with allowing customers to co-create brand meaning,
advertising messages, and other parts of the value chain (Vargo & Lusch, 2004).
One form of co-creation is contests in social media forums, where customers create “usergenerated content” (UCG) in a public forum about a brand and are compensated by either a small
reward or the chance of winning a larger reward. Such contests are becoming increasingly
common. An older example is the Dove Cream Oil Body Wash ad contest shown during the
2007 Academy Awards (Deighton, 2008). Contestants were encouraged to create an ad for the
new Dove product. A similar and more current example is the Pepsi MAX contest, in which
Doritos and Pepsi rewarded winners by airing six consumer-created ads during the 2012 Super
Bowl. Sony is sponsoring the “make believe Giveaway A Day” contest on their Facebook
community, in which they will give away Blu-Ray Disc players and other electronics devices.
Entrants must share their email address, which is a lower level of involvement than creating a
TV advertisement, as in the case of Dove or Pepsi. Kit Kat Canada recently sponsored the
“Game Time Give Away” on Facebook. Participants visited the Facebook page and completed
an entry form. Prizes included NFL beverage pails, T-Shirts, footballs, and pens, and chocolate.
The focus here is on contests requiring co-creation. We make three contributions. First, while
such contests are becoming more common, there are no published studies measuring their effect
on purchase behaviors. We test whether participating in such contests affects purchase behaviors
and illustrate ROI calculations. Second, we propose an explanation for the effect, which will help
organizations create more effective contests going forward. Third, we will investigate the
longevity of the effect—after participating in such a contest, how long is behavior affected?
WHY CONTESTS SHOULD WORK
This section proposes an explanation for why contests work, which informs their design.
Contests are an effective way to engage consumers because they encourage them to share their
personalized experiences with a brand, which is an essential element of value co-creation
(Prahalad & Ramaswamy, 2000, 2004). Prahalad and Ramaswamy (2004) emphasized that the
current shift from a firm-centric to a consumer-centric view of the market requires companies to
provide unique value to customers and the base of that differentiation is the consumer's
experience with the brand. Rather than being (possibly) exposed to an advertiser-created
message, contests ask consumers to think about the brand and create their own message based on
their past or expected future experiences with the brand. This processing should be a potent form
of advertising if it is executed properly. Associations should be more salient and personal when
consumers think through their relationship with brand, as opposed to being told what a brand
should mean to them by an ad.
These contests can be an example of "encounter processes" (Payne, Storbacka, and Frow, 2008),
which refer to "the process and practices of interaction and exchange that take place within
customer and supplier relationship and which need to be managed in order to develop successful
co-creation opportunities (pp. 85-86)." Some encounters—those with more economic or
emotional involvement—are more important for value co-creation processes, while other
encounters may be trivial (Gremler, 2004). We therefore expect contests to be most effective
when the task consumers are asked to complete is aligned with the brand positioning, including
the core product attributes, their claimed consumer benefits and other higher-order associations.
For example, Starbucks is focused on the perception that they offer the highest quality coffee and
allow their customers to customize it. They also provide a warm atmosphere that becomes a
home away from home for loyal customers. We predict that a contest inviting participants to
discuss how they customize their cup of coffee or the role their favorite Starbucks store plays in
their lives would outperform a contest in which participants had to do something unrelated to the
brand idea, such as having to text or email Starbucks to be entered into a raffle to win a car.
Writing about how Starbucks makes “their” special cup of coffee or how a visit to the local
Starbucks provides a much-needed respite from an otherwise chaotic day forces consumers to
think about Starbuck’s brand. Contests about cars would make no sense for Starbucks. Likewise,
we predict that the Kit Kat contest, giving away NFL merchandise, would not be an effective
brand builder, since the NFL has at best a weak association with the Kit Kat brand associated
with taking a break. Sony would seem to be missing an opportunity by not having entrants write
something about why they want the electronic devices. The Dove and Pepsi contests should have
a strong effect on those creating the ads because these contests give the contestants an
opportunity to actively think about the meanings and benefits they associate with the brand.
EMPIRICAL TEST OF HYPOTHESIS
We analyze data from the Air Miles Reward Program (AMRP), which has been operating in
Canada since 1992 and is one of the largest loyalty programs in the world, with over 10 million
members representing over 67% of Canadian households. As a coalition loyalty program,
members collect miles at over 100 sponsors categories covering all aspects of purchases ranging
from groceries to gasoline to apparel and credit card purchases. Collected miles can be
exchanged for rewards ranging from travel to merchandize to discount coupons.
In March 2009 AMRP launched a social media website for members to discuss the program and
benefits. Member posts can be linked to their purchases, providing a unique opportunity to
measure the effect of social media forums on purchase behaviors. Figure 1 shows the distribution
of the number of posts per day during 2010. Posting is sporadic, with few posts on most days–the
median number of posts per day is only 7 and the third quartile is 27. There are, however, large
“spikes” in activity: the maximum number of posts on a single day was 6465. The spikes are
driven by email promotions, e.g., on 2/3/10 AMRP sent an email announcing the one-year
anniversary “Block Party,” in which members were offered up to 4 chances to win 25,000
reward miles by becoming a member, posting a tip, uploading a picture, or making an “I like
this” thumbs up to the community site. This contest gave an opportunity for members to share
their personal experience with AMRP or listen to other member’s experiences, which transforms
customers from “passive audiences” to “active players” (Prahalad & Ramaswamy, 2000). The
second contest, “Cruise,” culminated in a drawing to win a Caribbean cruise package for two
people. During the span of six weeks, entrants had to answer six different questions each week,
e.g., why they want to spend their week aboard or whom they want to take with them. The third
contest, “Winter,” offered members a small number of miles for discussing what they planned to
redeem their miles for in winter. This contest forced members to think about the core benefit of
AMRP, and what rewards they want. The reason for belonging to AMRP becomes prominent in
members’ minds, and the reward becomes more tangible by writing it down. There are some
other much smaller spikes for less-successful contests that will not be analyzed in this paper.
AMRP provided the mile accumulation history for a stratified sample of 143,000 members, with
all participants and a random sample of non-participants. Contest participants form the treatment
group and non-participants are the control group. Having a control group enables us to control
for threats to internal validity such as history. Pre-measures account for customer heterogeneity
when comparing those who participate with those who do not. Although members self-select into
participating in the contests, this before-after-with-control-quasi-experimental design is robust to
most threats to internal validity (e.g., Churchill & Iacobucci, 2007). We also have more detailed
information about one of the contests, which will be discussed in more detail below.
We analyze each contest separately. Black intervals on the horizontal axis of Figure 1 show study
periods, defining the independent variable: for a contest, members who posted at least once in
the study period are in the treatment group while others are in the control group. Let dummy
variable xi equal 1 if member i posted something during the study period and 0 otherwise. For
each contest the study period is labeled “1” above each of the black intervals on the horizontal
axis. Pre-measures of behavior are computed from the 4-week period immediately preceding
each study period, called the pre-period and labeled “0.” To study how long participation affects
behavior in the future there are 3 additional periods labeled periods 2, 3 and 4. Study periods
were selected to include most of the posting activities around a contest. The study period for the
Block Party contest was 2 weeks long. The Winter contest was much shorter, with most posts
occurring within a 1-week interval. The Cruise contest was longer, and its study period is 6
weeks. The pre-periods (0) were all four weeks long. The post periods were 2 weeks long for all
but the Winter contest, where the post periods were 1 week long. Period lengths were round
weeks because we suspect that accumulation behavior is at least somewhat periodic, with, for
example, some households doing their grocery shopping every Saturday, etc. The dependent
variables are number of miles accumulated during periods 1–4, yit for customer i during period
t=0, 1, 2, 3, 4. We use a multiplicative model:
log(yit+1) = α + β1 xi + β2 log(yi0+1).
The log stabilizes the variance and symmetrizes the distributions. We test H0: β1=0 to decide if
there is a difference between posters and non-posters. When we can reject H0 then we have
evidence that posting during the study period affects behavior. Since this is a multiplicative
model, exp(β1) describes how many times greater mile accumulation is for posters.
The results are provided in Table 1. In total, 12 regression models were estimated, one for each
time period-contest combination. The number of participants is indicated under the contest name.
The effect of participation in the Block Party contest on behavior during the study period (1) is
β1=0.29435, which is statistically significantly different from 0. The magnitude of this effect is
exp(0.29435)=1.34, implying that the accumulation behavior of those who participated in the
Party contest was about 34% higher than those who did not. The effects in periods 2 and 3 are
also significantly different from 0, but slightly smaller. Behavior is changed for a total of six
weeks (three periods). This is a long-lasting effect. The results are similar for the Cruise and
Winter contests. The effects of both contests are significant and substantial for all four periods.
We have more complete data from the party contest, with the email promotions executed before
the contest began. The addition of the email data enabled us to estimate more accurate estimates
of participation effect by controlling for the effects of email promotions. A total of 3,656,105
members were sent email invitations to participate in the contest. We also know which members
opened the email, clicked on the link bringing them to the community website, and participated
in the contest.
We have performed a simple analysis, as shown in Figures 2-4, by dividing our sample into 3
experimental blocks according to the number of miles they earned during the pre-period prior to
the contest. The "Low" block earned less than 10 miles during the pre-period, the "Medium"
block earned between 10 and 55 miles and the "High" block earned more than 55 miles. Each
block constitutes approximately one third of our sample. These blocks control for differences in
commitment to AMRP prior to the contest.
Consider Figure 2 for the Low customer block. The tree structure shows the number of
customers and the mean number of miles accumulated during the contest period. For example,
there were 7,846 people in the sample who did not receive the email and accumulated a mean of
6.2 miles during the contest period; 34,889 opened the email and accumulated an average of 6.1
miles, which is not significantly different from 6.2. Of those who received the email, 19,424 did
not open the email while 15,465 opened it. Those who opened it accumulated 7.09 miles on
average in the contest period, while those who did not open it accumulated 5.4 miles, which is
significantly lower (P<.0001). Those who opened it are further split out into clickers versus nonclickers. Clickers are split between those who participate in the contest and those who don't.
In comparing the trees for the three blocks, we note four points. First, there is an accumulated
effect as the customer becomes more engaged (i.e., from opening the email to clicking on the
email to participating in the contest). Those who are open are significantly better than those who
don't, those who click are better yet, and those who participate are even better. Participation
seems to have a stronger effect than opening or clicking. Second, the absolute lift is greater for
High customers (108.21-91.1=17.11) than for Low customers (17.9-7.8=10.1). Third, the relative
lift of participation is greater for "Low" customers (10.1/7.8=129%) than for "High" customers
(17.11/91.1=19%) (this interaction is significant). Fourth, High customers are more likely to
engage in all three activities (open, click and participate) than other blocks. We have also
estimated regression models that avoid the discrete blocks introduced by the above analysis,
allowing us to control for other differences between the groups, and include interactions between
pre-period characteristics and participation. The conclusions from these models are consistent
with those from the trees presented above. Space does not permit us to present the models here.
This analysis provides a stronger test of the hypothesis that participation increases accumulation
behaviors. In the earlier models participation was confounded with opening and clicking. Here
we show that within those who clicked, participation still has an effect.
CONCLUSIONS AND GENERAL DISCUSSION
We have examined 3 social media contests and shown that those who participate consistently
have significantly higher accumulation of miles during and after the contest. The longevity of
the effect impressed us and we theorize that it is due to the participant co-creating the benefit of
AMRP—earning a reward. By having participants write about the reward they want or why they
want it, the benefit becomes more salient in their minds. Such contests also allow for the
customization of the benefit, where participants can think about the reward that has unique
meaning for them. In particular, the Cruise contest asked customers to share their personal stories
about the meaning of the benefits they'll earn if they won the contest. These opportunities seem
to be an example of "critical encounters" in that participants are encouraged to articulate the
value of the brand promise in personalized contexts (Gremler, 2004). Such articulations should
be a more potent form of advertising than merely exposing participants to an ad message telling
them they can earn a reward.
This study has limitations and raises other questions. The quasi-experimental design used here,
with pre-measures and a control group, is fairly robust to threats to internal validity, but it would
be desirable to run true experiments where participation is manipulated, and to isolate the cocreation cause. Are contests where the task required for entry is aligned with the brand more
effective than those that are unrelated? Is it important for participants to share their goals and
experiences in a public forum, or is co-creating the benefit in private sufficient? Answering such
questions will require experiments. We analyzed the effect of participating in contests, but could
not examine the effect of reading entries made by others. Does reading a contest entry written by
another customer change purchase behavior? Future research should address this issue to present
a complete picture of how contests work for both active (i.e., creating messages) and less active
(i.e., reading other people's messages) forms of participation.
There are important implications and applications of this research. First, out models enable ROI
calculations for the contests. For example, rough ROI calculations can be produced from Figure
4 (High). The 1442 who participate accumulate 108.21-91.1 = 17.11 more miles, which means
that AMRP issued 1442(17.11) = 25,000 incremental miles due to participation among the High
customers. There are incremental miles from clicking and opening too. The total incremental
miles multiplied by the amount AMRP charges its sponsors gives incremental revenue. We must
do the same for subsequent time periods and the other two blocks. We would then deduct the
cost of the promotion (sending 3.66 million emails) and the contest prize (25,000 miles).
Another implication of the co-creation explanation is that companies should design contests that
force participants to focus on the core intended brand benefits. For example, the Kit Kat giveaway contest should be more effective if entrants discussed how core brand associations were
meaningful to them, e.g., “tell us how you take a break with Kit Kat,” or “tell us how you like to
share your Kit Kat with a friend.” In contrast, filling out a form to win an NFL beverage pail
would not seem to build the brand.
Allowing consumer to co-create brand meaning and benefits would seem to be a more effective
way to increase purchase behavior than simply exposing consumers to ad messages, but it also
creates new challenges and risks. The meanings that some consumers create may not be “on
strategy,” in that they differ from what the organization intends for consumers to think about its
brand. In some cases these alternative interpretations of the brand may be opportunities for the
firm to expand the meaning of its brand. The firm may not have realized that this alternative
meaning is the reason why a large number of consumers are loyal. It is also possible, however, to
envisage situations where the alternative interpretations cause the brand to lose focus and
confuse other customers who read them. Famous examples of such user-generated content
discussed by Deighton (2008) are the Dove Cream Oil Body Wash entries by “Biker Dave” and
“Bed Vlog.” Such ads are still available on YouTube and do not communicate the intended brand
meaning. An important opportunity for future research is in devising communication strategies
for responding to such user-generated content.
Figure 1: Distribution of the number of posts over time.
Figure 2: Tree diagram for "Low" customer block.
Figure 3: Tree diagram for "Medium" customer block.
Figure 4: Tree diagram for "High" customer block.
Table 1: Parameter estimates of β1, indicating the effect of participating in the contest. Below
each estimate is the t statistic and the number of participants in each contest . One star indicates
P<.05, two stars mean P<.01, and three stars mean P<.001.
Contest
Party
n1 = 3,639/132,307
Cruise
n1 = 19,473/136,237
Winter
n1 = 6,801/129,589
Period 1
Period 2
***
0.29435
1.34 (12.82)
0.20321***
1.23 (21.93)
0.24901***
1.28 (13.28)
Period 3
***
0.2345
1.26 (10.18)
0.14500***
1.16 (12.82)
-0.00388
1.00 (-0.21)
Period 4
***
0.22799
1.26 (9.63)
0.12547***
1.13 (10.88)
0.04963**
1.05 (2.74)
0.30723***
1.34 (12.71)
0.10049***
1.11 (9.04)
0.09235***
1.10 (5.49)
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