Viral

Discussion and Conclusion
Although the retailer may have hoped to boost its revenues through viral marketing,
the additional purchases that resulted from recommendations are just a drop in the
bucket of sales that occur through the website. Nevertheless, we were able to obtain a
number of interesting insights into how viral marketing works that challenge common
assumptions made in epidemicand rumor propagation modeling.

Bien que les marchands peuvent ont pu croire booster leurs bénéfice grace au marketing, les achats additionnels qui resultent de ces recommandations sont juste une goutte d’eau dans un océan des ventes qu’ils font a travers leur site. Néanmois, nous sommes capable d’obtenir nombreuses d’idees interessantes sur comment marche le marketing viral que les suppositionscommunes en font un modele de propagation epidemique telle une rumeur.

Firstly, it is frequently assumed in epidemic models (e.g., SIRS type of models)
that individuals have equal probability of being infected every time they interact
[AM02, Bai75]. Contrary to this we observe that the probability of infection
decreases with repeated interaction. Marketers should take heed that providingexcessive
incentives for customers to recommend products could backfire by weakening
the credibility of the very same links they are trying to take advantage of.

Tout d’abord, dans les modeles epidemiques il est frequemment supposé que les individus ont autant de chance d’etre infecté chaque fois qu’ils interagissent. Contrairement a cela,on observe que la probabilité d’infection baisse si lesinteractions sont repetees. Les vendeurs devrait tenir compte qu’encourager excessivement les clients à recommender les produits peut avoir un effet inverse en les decribilisant.

Traditional epidemic and innovation diffusion models also often assume that individuals
either have a constant probability of ‘converting’ every time they interact
with an infected individual [GLM01], or that theyconvert once the fraction of their
contacts who are infected exceeds a threshold [Gra78]. In both cases, an increasing
number of infected contacts results in an increased likelihood of infection. Instead,
we find that the probability of purchasing a product increases with the number of
recommendations received, but quickly saturates to a constant and relatively low
probability. This meansindividuals are often impervious to the recommendations of
their friends, and resist buying items that they do not want.

Les modeles de diffusion d’innovation et epidemique traditionnel suppose aussi souvent que les individus soitn ont une probabilité constante d’été affecté chaque fois qu’ils interagissent avec une personne déjà infecté ou qu’ils sont infectées une fois qu’une partie de contactinfecté depasse un certain seuil. Dans les deux cas, un nombre croissant de contact infecté debouche sur une tendance croissante d’infection. A la place. On trouve que la probabilité d’achat d’un produit augmente avec le nombre de recommendations recues, mais il est rapidement saturée à une constante et à une probabilité relativement basse.Cela signifie que les individus sont souvent impermeableaux recommandations de leurs amis et resistent a acheter des articles qu’ils ne veulent pas.

In network-based epidemic models, extremely highly connected individuals play
a very important role. For example, in needle sharing and sexual contact networks
these nodes become the “super-spreaders” by infecting a large number of people. But
these models assume that a high degree node has as muchof a probability of infecting
each of its neighbors as a low degree node does. In contrast, we find that there are
limits to how influential high degree nodes are in the recommendation network. As
a person sends out more and more recommendations past a certain number for a
product, the success per recommendation declines. This would seem to indicate that
individuals have influence over a few...