This post is part of a series on how the 9 Laws of Data Mining from Tom Khabaza can be applied to analytics. You can find previous posts here. Law…
Mega-retailer Target recently announced that it would be closing the doors of eleven locations by February 1, 2015. The company’s press release stated, “The decision to close a Target store is only made after careful consideration of the long-term financial performance of a particular location.”[i] Target’s team of site selection experts have certainly been doing a great job considering their $72.6 BILLION in revenue for 2014. [ii] That being said, what were the root causes behind the closing of these particular stores?
This post contributed by 4CGeoWorks Managing Consultant of Global Services, George Anderson
Many retailers today are faced with the daunting task of international store expansion, but for many looking to grow, the notion of expansion outside of the U.S. is a complex challenge. How are they to know, with confidence, which country they should expand into next?
To appropriately develop and expand across a global platform of new/existing stores and for the retailer to be successful in the international theatre, one key factor is to mitigate risk. To mitigate risk the retailer must have a clear market understanding. They must understand not only who the customer is today, but how many customers exist in the marketplace, their share of wallet (how much disposable income they have to spend on non-essentials) and how many potential locations can realistically be opened.
Law #5: “Watkins’ Law” – There are always patterns [i]
First, let’s talk about “The Good.” David Byrne, writing in the introduction to Gareth Cook’s book, The Best American Infographics, 2013, describes the power of the infographic as:
“…an inbuilt ability to manipulate visual metaphors in ways we cannot do with the things and concepts they stand for — to use them as malleable, conceptual Tetris blocks or modeling clay that we can more easily squeeze, stack, and reorder. And then — whammo! — a pattern emerges, and we’ve arrived someplace we would never have gotten by any other means.”
He could just as easily have been talking about the data mining and analytics process, except that the process is much slower and more methodical than the expression “whammo” suggests.[ii]
This post is part of a series on how the 9 Laws of Data Mining from Tom Khabaza can be applied to analytics. You can find previous posts here. The statement that “a…