Tag Archives: driver analysis

Freakometrics – This is the hidden side of business facts

Freakometrics – This is the hidden side of business facts


Why is a product bought by senior customers more attractive to young people? Why might a factor that is useless in predictive analytics be your most important reason for success? Why might providing some product samples be beneficial but their extensive use can harm sales? The following posts will explore the hidden side of business facts. Together we will venture in a world where you can see the reasons behind facts. Board now!

Why Do You Urgently Need to Escape the Data Fallacy?

Why Predictive Analytics don’t tell you anything about Success Drivers?

Why your world in business analytics is not flat?

4 Questions You Should Ask your Analytics Vendor?

Freakometrics – The hidden side of business facts Part 4 of 4

4 Questions You Should Ask your Analytics Vendor


The greatest fallacy of the ever growing data euphoria is that raw facts tell you anything on what’s important. No matter how you aggregate or disaggregate data you will not be able to find what is most valuable to drive your business. Todays business intelligence tools and market research reports produce transparency but no answers to “why” questions.

Multivariate statistics were designed to help, but by just grabbing in the toolbox of conventional statistics you’re close to tap in the next trap. Most prominently, it is an imperative to understand indirect relations between factors. This is one of the reasons why one should not solely rely on predictive analytics when it comes to find answers to “why” questions.

Finally, standard statistical methods force users to make assumption that often does not match with reality. What we need are methods that help us to explore nonlinear relations and complex interactions without assuming things you don’t know.

So, if you’re assessing customer insight agencies or software packages in your quest of making sense of data and your complex business, we recommend to ask the following questions:

  1. Are you using multivariate methods to prevent spurious findings?
  2. Are you using a path analytics approach to capture indirect and direct effects?
  3. Are you able to uncover nonlinearities and interactions – those kind where you don’t know that they exist?
  4. Are you able to capture a wide net of factors in order to explore important factors you don’t know of upfront?

With asking these questions, you are more likely to make sense of all this Freakometrics out there. I can promise – this venture will be truly exciting.  The beautiful part is that complex modeling of unknown properties will often quickly transform in something very simple. You will find those 20% simple but powerful insights that explain 80% of your success. That is how complex beauty transforms into simple actionable results.

If you find this post useful, we appreciate if you “like” it and share it.