Data FTW?
I just finished reading Competing on Analytics. It is the most painful literary experience I’ve had to date. The co-authors of this book have no business writing…anything…ever. Fire the editor too. However, amidst the shitty organization and delivery in this book, there are some interesting things to think about.
First, a quick overview so we’re all on the same page.
OVERVIEW
Competing on Analytics brings to light the differences between companies that use analytical data as supporting information to make business decisions and companies that allow analytical data to drive all of their operations. An analytically competitive company would do some of the following: use an algorithm that sifts through job applications to identify ideal matches for open positions, use algorithms to identify customer profiles that are most profitable, forecast market trends to define product demands and so on.
I’m going to split the rest up between business and consumer interests.
BUSINESS PERSPECTIVE
Everyone can probably think of at least one company that does something like this and most of them are the newer tech savvy companies like Google or Amazon. So why only the younger companies? Well that should be obvious, old people are scared of computers. Seriously, that’s what it comes down to. The old guard wants to trust their gut and the new kids know to the trust the numbers.
And of course, the new guys who rely on analytics to drive their business are the ones dominating their market segments. The book is riddled with examples of how various companies (like Netflix, Capital One, Google, etc) either converted or initially set out to use analytics to streamline their internal operations or target their marketing at more profitable customers.
We, the technologists, say “duh” to the idea of using analyzed data to make decisions, but apparently there are plenty of unbelievers out there. Those are the people I think this book is speaking to. They point out that executive sponsorship is required for a company to become an analytical competitor, so give this book to your boss (or your bosses boss, or his bosses boss boss boss) 
The resounding message, supported by the author’s research, is that analytics can make a gigantic improvement in revenue, operational expenditures and really any area you apply analytics.
CONSUMER PERSPECTIVE
What are these companies doing with this new found efficiency and information that benefits their customers? Evil of course. Ok, not all of them. I’ll talk about the good guys and then rip on the evil fucks. And by good guys I mean companies using analytics to the benefit of their customers. They may be plenty evil in general (especially the first example), but this article is about analytics.
The good guys:
Wal-Mart is a master of product logistics. They can move product from the manufacture to the warehouse and on to the shelf better than anyone else. This is all accomplished with supply chain analytics and it drives their margin down, which is partially passed on to the consumer.
A person’s FICO score can screw them in the credit racket. Most companies take it for granted; if you have a low credit score, you are a high risk at defaulting on loans so you generally can’t get them. Capital One decided to create their own scoring system based on their own analyzed data. Now, consumers identified as financially responsible have a better chance at getting a credit card through them even if their FICO score is low. This also caused Capital One to experience explosive growth because they were able to identify a massive market of potential customers that no one else was tapping in to.
Now for the analytically evil:
Who loves Netflix?! Wooo! Ya, they’re evil. If you are a high volume customer (thus reducing their profit margin on your account) they put your orders in the slow lane for shipping. Their customer profile analytics are very nice, but that is a component that taints their whole system.
Amazon uses a similar customer profile system that determines what you might want to buy based on your purchase history and other fancy features. They don’t do this anymore, but their system had the same high volume customer penalty. If you purchased a lot of merchandise through them, you were actually seeing higher prices than people with a low volume history. That’s fucked up.
IN CONCLUSION
With command of vast data stores comes knowledge and with that knowledge comes power. And what does that lead to boys and girls? That’s right, the Dark Side. While intensive analytics can be awesome for businesses, it has a long ways to go until it ultimately starts benefiting the consumer. And yes, in the end, that’s all who matters
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I’m having a hard time understanding why you’d read this book in the first place if it was so painful…
I guess my algorithm is lacking the proper data to figure that out…
Toz asked me to read it for work. My theory is that this book should be passed up the food chain, not down.
You’re passing it the wrong way, Toz! We already agree this is a good idea, we don’t need the book! lol
Well, depending on what you consider evil or not, Google is no fan of privacy, and they analyzed that a law would change, so before the law has even been written, let alone voted on, they decide to store private data for a lot longer than they’d need to if they cared out you.
I was in line for the bank the other day, the “customer” line has been renamed the “consumer” line, that’s sickening.
Ohh, here’s a book I think both you and Toz should read. Why - by Charles Tilly. Coupled with what you guys know from other areas of management training at UAT, it can provide a whole new angle to a lot of issues.