• http://www.rimmkaufman.com/ George Michie

    Good stuff as always, Sid. As I pointed out a few weeks back (http://www.rimmkaufman.com/blog/attribution-myths-vs-reality-part-1-statistical-limits/09072013/) even high rent statistics struggles with the distinction between correlation and causation absent a controlled testing environment. Moreover, these systems generally fail to understand demand elasticity and the cost of incremental traffic.

  • http://www.rimmkaufman.com/ George Michie

    Good stuff as always, Sid. As I pointed out a few weeks back (http://www.rimmkaufman.com/blog/attribution-myths-vs-reality-part-1-statistical-limits/09072013/) even high rent statistics struggles with the distinction between correlation and causation absent a controlled testing environment. Moreover, these systems generally fail to understand demand elasticity and the cost of incremental traffic.

  • Raphael Calgaro

    The fact that most Analytics implementation are still in the visits level and not visitors level tend to make these stats inaccurate. The email i received on my phone in the morning triggers a search on my business computer and concludes with a transaction on my personal computer. Those are generally considered as 3 visits and three visitors whereas I am still only one person. The 3 touch point are not linked and no value is attributed to the prior visits.

  • Sid Shah

    @Raphael: I agree that analytics tracking cant capture cross device or online-offline sales funnels. You can over come some of these issues with econometric methods. However, my point here is that the statistics that you would get with any bottom up attribution method for the example you describe would still be inaccurate (first click, last click, “algorithmic” etc).