The tools we use

For those of you with a quantitative background, or in the happily case that you’re not turned off with the technical details, here’s a rough list of the tools and techniques we typically use when putting our hands on customers data.

toolset - by flickr & user flattop341

toolset - by flickr & user flattop341

  • data cleaning & transformation
  • creation and maintenance of large databases
  • definition, design and production of data attributes
  • descriptive statistics
  • data visualization
  • time series analysis
  • survival analysis
  • multiple regression
  • analysis of variance/covariance
  • generalized linear models
  • correlation analysis
  • cluster analysis
  • factor analysis
  • principal component analysis
  • multidimensional scaling
  • statistical quality control
  • unsupervised and supervised pattern extraction
  • classification and clustering techniques
  • association rules
  • decision trees
  • predictive data mining
  • neural networks
  • support vector machines
  • recommender systems
  • bayesian analysis

Let us also stress the extensive effort we each time put on interpreting the results of all the above techniques. So, rest assured that the outcomes we deliver are engineered to be easily comprehensive and directly actionable, free of jargon or any other technicality you won’t be perfectly comfortable with.

Should you want to learn more or discuss how these apply in your specific data and tangible problems, shoot us an email at

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