Defining Justifiable Targets for Analytics Success Part of a Continuing Series on Straightforward Analytics
by David Loshin
Originally published May 26, 2011
Our expectation is that the team focusing on analytics is doing so to help achieve some business objective, even if that objective is stated in high-level terms. Yet in order to determine whether the analytics technology is going to yield some benefit, as we have discussed in previous articles, the expectation of achieving the objective must make sense within the context of what is feasibly doable in the organization.
Directive by FiatLet’s start with an example of “directive by fiat.” In this scenario, a key stakeholder asserts a target for success that has no realistic basis. As an example, consider a financial services company does some high-level analysis to measure both the total and average numbers of products or services that each customer has. The result is that the average number of products per customer is 3.5; as a result, a senior manager insists that this average must be increased by 10% so that the new target is to increase the average number of products/services per customer to 3.85.
Here we can start asking a bunch of questions:
Technology OverkillOur second scenario is expecting technology to tell you something that you could have reasoned by common sense, without expecting technology to provide the answer. I recall a presentation I heard a few years ago discussing the use of predictive analytics for sales productivity at an amusement park. Apparently the analysis demonstrated that there was a correlation between inclement weather (specifically, rain) and decreased point of sale productivity. A review of the business situation showed that when it rained, people moved out of the rain into the closest areas of shelter, which were mostly stores. Some were induced to buy more products, mostly as a result of being in these stores. Additional purchases meant that more people were standing in line to buy stuff, and that meant that the usual number of open registers was not sufficient to handle the extra load. Voila – decreased point of sale productivity.
But do you really need an analytics environment to tell you this? On the contrary, one might say that this is just common sense, as are many other conclusions “discovered” by analytics systems. And if we are looking at small and midsize businesses, the owners or executive managers are likely to have their fingers on the pulse of the organization to know the most significant operational opportunities.
Define Justifiable TargetsSo to best understand what is meant by the third phase of our process preparation “define justifiable targets for success” (as discussed in the initial article on straightforward analytics), specifically for the use of analytics, let’s break it down into more discrete objectives:
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