Archive for mars 6th, 2017

6 mars 2017

Biais cognitif: l’erreur fondamentale d’attribution

par marketingthema


Les journalistes vous semblent plus compétents que ceux et celles qu’ils interrogent ? Julien Lepers ou tout autre présentateur de jeu de culture générale vous semblent être très cultivés ?  Si oui, vous avez fait l’expérience de l’erreur fondamentale d’attribution, connu aussi sous le nom d’effet Julien Lepers !


6 mars 2017

Where is data-driven marketing headed in 2017? #Evolving_Practices

par marketingthema


‘Data-driven’ is one of those terms which seems unnecessary for marketing. Surely all marketing uses data to some extent, so why does there need to be a distinction?

As marketing increasingly moves to digital platforms, however, the concepts behind the term ‘data-driven marketing’ have become distinguished from more traditional marketing and even have their own vocabulary.

Terms like programmatic buying, real-time bidding (RTB), data management platform (DMP), customer data platform (CDP), and attribution modeling are now standard lingo when talking about using data for marketing nowadays. Without some grasp of these terms and the concepts behind them, marketers can quickly become lost when speaking with others in the biz.

Perhaps, then, it does make sense to talk about ‘data-driven’ marketing differently from other marketing which focuses more on the ‘four Ps‘ or ‘STP marketing‘.

For readers who feel that they need to catch up in this area, Econsultancy has a number of blog posts on these topics and Econsultancy subscribers can consult our recent research covering programmatic, data-driven branding and the role of the CRM in data-driven marketing.

Full article is here

6 mars 2017

Gartner 2017 Magic Quadrant for Data Science Platforms #kdnuggets #MarketingThema

par marketingthema


Gartner new 2017 Magic Quadrant for Data Science Platforms (called in 2016 « Advanced Analytics Platforms ») was published last week. The 2017 report evaluated a new set of 16 analytics and data science firms over 15 criteria and placed them in 4 quadrants, based on completeness of vision and ability to execute.

While open source platforms like Python and R play an important role in the Data Science market, Gartner research methodology does not include them, so this report evaluates only commercial vendors.

Fig. 1: Gartner 2017 Magic Quadrant for Data Science Platforms

Firms covered:

  • Leaders (4): IBM, SAS, RapidMiner, KNIME
  • Challengers (4): MathWorks (new), Quest (formerly Dell), Alteryx, Angoss
  • Visionaries (5): Microsoft, (new), Dataiku (new), Domino Data Lab (new), Alpine Data
  • Niche Players (3): FICO, SAP, Teradata (new)

Full paper here