When beginning a new predictive analytics project, the client often mentions the importance of a “quick win”. It makes sense to think about delivering fast results, in a limited area, that excites important stakeholders and gains support and funding for more predictive projects. A great goal.
It’s the implementation of the quick win in a predictive project that can be difficult. There are at least 2 challenges with using a traditional quick win approach to predictive analytics projects.
Challenge #1: Predicting Something That Doesn’t Get Stakeholders Excited
Almost daily I hear of another predictive project that was limited in scope and allowed people to dip their toe in the predictive water and get a “quick win”. The problem was the results of the project predicted something stakeholders didn’t care about or couldn’t take action on.
The problem with this quick win is that results of this prediction can lead to questions around – are they also the most expensive schools? Does only a certain economic class of person attend these schools.
Using these predictions opens up discussions of economic discrimination, making HR and executives nervous. They often decide to ignore their newfound ability to predict performance, they don’t implement the prediction and the project doesn’t advance the case for more predictive projects.
The problem with this quick win? While HR thought the project was a winner, project results got no excitement from business stakeholders and didn’t advance the goal of gaining additional support and resources for more predictive projects.
Executives have seen little or no correlation between engagement and actual business results at their own firm. Imagine trying to sell the VP of Sales on predicting engagement of their sales reps? At the end of the day their employees aren’t hired to be engaged, they are hired to do their job and sell.
In non-analytics projects you’re able to do a pilot with a small amount of people and data. You can focus on a small piece, a sample, something light, less expensive, less risky and less time consuming before you fully commit.
An example would be piloting a piece of software. You could install it for a small number of people and gain their feedback before making a broader commitment.
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