Social media: The new hybrid element of thepromotion mix, Business Horizons

Mangold, G., Faulds, D. Social media: The new hybrid element of thepromotion mix, Business Horizons (2009) 52, 357-365

Mots clés Integrated marketing communications, Social media, Consumer-generated media, Promotion mix

Dans cet article, l’auteur développe l’argument qu’il faut utiliser les réseaux sociaux comme nouveau levier du Promotion Mix

Développement :

L’émergence des réseaux sociaux a permis a ce qu’une personne puisse communiquer à des milliers d’autres sur des produits et des entreprises.

L’impact des relations entre consommateurs a été donc grandement augmentée. Cet article argumente que les réseaux sociaux sont un élément hybride du promotion mix pare qu’il permet aux entreprises de communiquer avec leurs cconsommateurs et aux consommateurs dintéragir entre eux.

Les relations entre les consommateurs sur les réseaux peuvent être difficilement controlées et surveillées. Ce qui va a l’encontre des traditionelles méthodes de marketing ou une grande place est accordée au contrôle et à la certitude.

Conclusion :

Il faut donc apprendre à modeler les discussions des consommateurs de tels sorte à ce qu’il soit consistent et parle d’une même voix avec la mission de l’entreprise ainsi que ses valeurs.

Les méthodes qui peuvent être utilisées dans ce sens sont d’offrir aux consommateurs des plateformes sociales, et d’utiliser des blogs, des outils des réseaux sociaux et des outils promotionnels pour engager les consommateurs

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Maximizing the Spread of Influence through a Social Network

Fiche de lecture  :

Kempe, D., Kleinberg, J., Tardos, E. (2013), Maximizing the Spread of Influence through a Social Network

Mots clés : approximation algorithms, social networks, viral marketing, diffusion of innovations

Les auteurs ont mis en place des processus et algorithmes pour déterminer l’étendu de l’influence sur les réseaux sociaux. Ils tentent de déterminer quels algorithmes peuvent analyser comment l’influence se diffuse sur les réseaux sociaux et comment cibler des individus

Développement :

Les réseaux sociaux jouent un rôle clé dans la diffusion de l’information, des idées et de l’influence parmis les utilisateurs.

N’importe quelle idée peut apparaître sur les réseaux sociaux et elle peut soit disparaitre très vite ou se propager et marquer l’esprit collectif de la population.

Avec le Big Data collecté sur les réseaux sociauux, ont peut estimer avec quelle force des individus s’influencent et marketer un produit afin qu’il soit adopter par une grande partie d’un dit network. Les premisces du marketing Viral et de l’Influence marketing sont que en targettant quelques personnes influentes avec un contrat ou des produits gratuits on peut déclencher une cascade d’influence ou les influenceurs vont influencé leurs followers qui vont faire de même avec leurs followers et leurs amis, ainsi de suite, créant ainsi une vague d’influence qui se propage.

Conclusion : Afin que ce phénomène se produit, il faut réussir à cibler une poignée d’influenceur qui sont en adéquation parfaite avec les thèmes de la campagne, le produit et la marque.

Il faut que les followers voient dans ce relaie du produit par l’influenceur, un acte tout à fait naturel de part l’univers de celui-ci. La correspondace doit être parfaite notamment entre la base de followers de la marque et celle de l’influenceur

 

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Provost, F. and Fawcett, T. (2013), DATA SCIENCE AND ITS RELATIONSHIP TO BIG DATA AND DATA-DRIVEN DECISION MAKING

Fiche de lecture  :

Provost, F. and Fawcett, T. (2013), DATA SCIENCE AND ITS RELATIONSHIP TO BIG DATA AND DATA-DRIVEN DECISION MAKING: 10.1089/big.2013.1508

Mots clés : Data Science, Big Data, Data Driven decision making, 

Provst F and Fawcett T,explain how Data Science works wih Big Data. They show us how this data can be collected, used and analyzed to drive decision making.

Développement :

With vast amounts of data now available, companies in almost every industry are focused on exploiting data for competitive advantage. The volume and variety of data have far outstripped the capacity of manual analysis, and in some cases have exceeded the capacity of conventional databases.

At the same time, computers have become far more powerful, networking is ubiquitous, and algorithms have been developed that can connect datasets to enable broader and deeper analyses than previously possible.

The convergence of these phenomena has given rise to the increasingly widespread business application of data science. Companies across industries have realized that they need to hire more data scientists. Academic institutions are scrambling to put together programs to train data scientists. Publications are touting data science as a hot career choice and even ‘‘sexy.’’

The authors argue that there are good reasons why it has been hard to pin down what exactly is data science. One reason is that data science is intricately intertwined with other important concepts, like big data and data-driven decision making, which are also growing in importance and attention. Another reason is the natural tendency, in the absence of academic programs to teach one otherwise, to associate what a practitioner actually does with the definition of the practitioner’s field; this can result in overlooking the fundamentals of the field.

Data-science academic programs are being developed, and in an academic setting we can debate its boundaries. However, in order for data science to serve business effectively, it is important  to understand its relationships to these other important and closely related concepts, and (to begin to understand what are the fundamental principles underlying data science.

They present a perspective that addresses all these concepts by highlighting the data science as the connective tissue between data-processing technologies and data-driven decision making.

Conclusion :

Underlying the extensive collection of techniques for mining data is a much smaller set of fundamental concepts comprising data science. In order for data science to flourish as a field, rather than to drown in the flood of popular attention, we must think beyond the algorithms, techniques, and tools in common use. We must think about the core principles and concepts that underlie the techniques, and also the systematic thinking that fosters success in data-driven decision making. These data science concepts are general and very broadly applicable

Success in today’s data-oriented business environment requires being able to think about how these fundamental concepts apply to particular business problems—to think data-analytically. This is aided by conceptual frameworks that themselves are part of data science.

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