In the case study, "Global Attribution" (found in the text book), we learn that in this era of "big data," business decisions are increasingly being made on the basis of data-centered evidence. This is especially true in the online arena, where a tradition of A/B testing and data-focused decision-making has dominated. But even the best data does not necessarily facilitate accurate interpretation of customer actions. Any web analytics package will provide multiple attribution methods (methods for apportioning credit for the sale across various customer touchpoints), and none of those attribution methods is clearly superior to another. Thus, now more than ever, analysts need to understand the assumptions underlying any data-based decision that gets made.

So with that said, in this case,

1) Which of the ten media channels do you think was least reliably measured? Do you think the measurement of ad exposure in this channel was biased (systematically higher or lower than the real number) or just highly variant (sometime higher and sometimes lower than the real number)?

2) Could the methodology described in the case be applied to determine advertising effectiveness of a consumer packaged good like Tide? Why / why not?