the project

For the first time in history, spending on Internet advertising surpassed newspaper spending, a trend that will increase in the future. The large bulk of this spending was invested in non-traditional social media marketing programs, involving multiple channels such as Twitter, social networks, blogs and more. Alongside the growth of social networks the birth of a new methodology of analysis: Social Network Analysis.

This methodology, based on graph theory and social analysis, is of increasing importance in Marketing 2.0. As online marketing has shifted away from a model based on Click-through rates and impressions and has moved towards a less measurable model – with multiple transmitters of information (tweeters, bloggers, fans, etc.) it is becoming increasingly difficult to measure ROI for these types of marketing actions.

The product to be developed during the project will be a SaaS system that will help companies (or their social media agencies) to increase the visibility of their products, services or brands, by means of:


1. Maximizing the impact of social media campaigns

Based on a previous analysis of the network structure of main social network services (Twitter, blogs and Facebook) and a deep content-based information diffusion analysis, users will learn to:

  • Identify influencers that could help to improve the diffusion of messages
  • Identify online communities that best fit their campaign target, and focus the actions on them.
  • Increase engagement with these actions, identifying the factors that improve involvement
  • Predict the social spread of the campaign, enabling the adjustment of the actions according to real performance and contrast with expectations.


2. Monitoring social media marketing impact

The aim will be to establish metrics for evaluate the efficiency of an online marketing campaign (ROI), evaluating with relevant indicators (percentage of conversion, increase of new users, etc.) campaign performance.



“The research leading to these results has received funding from the European Union’s Seventh Framework Programme managed by REA Research Executive Agency under grant agreement n° FP7-SME-2013-605353“