Leveraging Social Media in the Pharmaceutical Industry - Innovation and Marketing in the Pharmaceutical Industry - page 479

new product diffusion (Bell and Song 2007 ; Foster and Rosenzweig 1995 ; Garber et al.
2004 ; Manchanda et al. 2008 ; Van den Bulte and Joshi 2007 ). Besides modeling the time
to adoption of a new product or service as a function of word of mouth, other dependent
variables analyzed include sales (Berger et al. 2010 ; Chevalier and Mayzlin 2006 ;
Liu 2006 ; Rui et al. 2011 ; Sonnier et al. 2011 ), prescriptions (Nair et al. 2010 ), retention
rate (Schmitt et al. 2011 ), product rating (Godes and Mayzlin 2004 ), and stock price
(Luo 2009 ). Research on the impact of WOM has predominately documented the
significant explanatory power of WOM on the dependent variable of interest.
Firm targeting strategy . It is widely recognized that social influence exists and
WOM is a cost efficient way to drive sales. Firms need to understand who to target
to maximize the lift from WOM and social influence. Two key factors determine the
success of a WOM campaign. First, firms should allocate marketing resources to
influence the influential members (influencers) of a network. However, there is
inconclusive evidence on who constitute the influencers. Consumers with greater
product expertise could be more influential than others. Consistent with this assump-
tion, it is common for sales force in the pharmaceutical industry to target high pre-
scribers and those in other industries to target heavy users of their products, under
the belief that heavy users have high “stand-alone” customer lifetime value as well
as “network value” or social influence. This view suggests that loyal customers who
have more experience with a product tend to be more satisfied with the product.
These customers are looked upon as opinion leaders or experts with respect to the
particular product. However, Godes and Mayzlin ( 2009 ) find that opinion leader-
ship is associated with a higher propensity to spread WOM only among more loyal
consumers, but not among less loyal customers. Iyengar et al. ( 2011b ) show that the
volume of product usage and self-reported leadership are only moderately corre-
lated with social-metric leadership. More research is needed for a comprehensive
framework that can guide marketers to effectively identify and target the influenc-
ers. By the same token, members of a network are not equally susceptible to influ-
ences. Iyengar et al. ( 2011b ) find that physicians who perceive themselves as
opinion leaders are less sensitive to influence from peers. Nair et al. ( 2010 ) find that
physicians' prescription volume is influenced by that of a peer they regard as an
influencer only after a change in FDA policy about usage.
The second factor that determines the success of a WOM campaign is an under-
standing of those susceptible to a firm's targeted efforts (Christakis and Fowler
2011 ). This aspect has been overlooked by many researchers and practitioners
who have concentrated on influencing the influencers. However, understanding the
susceptibility of network members to marketing effort is equally important.
WOM vs. traditional marketing . WOM and traditional marketing are two contrast-
ing types of communications that can drive sales. Trusov et al. ( 2009 ) find that
WOM referrals have substantially longer carryover effects and produce substan-
tially higher response elasticities than do traditional marketing actions. Villanueva
et al. ( 2008 ) document that marketing-induced customers add more short-term
value, but WOM-induced customers add nearly twice as much long-term value to the
firm. Manchanda et al. ( 2008 ) find that marketing plays a relatively large role in affect-
ing early adoption while contagion plays a dominant role from Month 4 onward.