As data mining and scraping consumer information grows imageby leaps and bounds so does the intelligence on what they determine you are going to be shown as a price for a product.  The store as an example here for most of this article is Staples.  So much for everyone being offered the same price for the same product anymore:)  We all hear that the financial world uses mathematics that are way too complicated, said by the quants themselves.  Well here we go with trying to buy a simple stapler according to the Wall Street Journal and their recordings of the event. 

Some sites as noted here even showed different people different credit card offerings based on their credit score.  Here we go again with using analytics based on probably both linear and non linear data to get down to the exact penny as to what kind of profit can be derived from that stapler bought from each zip code in the US:)  Amazon tried it for a while and then abandoned it.  While you are sitting there on the website the scrapers and miners are going to work on predicting and evaluating your information. 

Actually a while back I read an article that kind of made sense on some of this real time adjusting pricing in the fact that if it picks up tremendously, that sites can in fact experience the same thing as we see in markets today with flash crashes as now you have a ton more algorithms at work to keep that data changing all the time, it is what it is so retailers look like they are taking some lessons from the financial markets, and is all of this data activity really necessary to promote more profitability and how much is it really generating is the question.  The more code you run and change things, the greater your chances are for some rogue algorithms to kick in and potentially mess everything up.  When I say rogue let’s say you were given the lower price but at the time of check out did something go wrong and charge you the higher price?  Expect things like this to occur as it happens all the time with shifting and changing data at rapid speeds and rates.  A few errors like the potential example above might be enough to cut out some complex algorithms that really do not need to be there. 

This is also part of the reason that we don’t have enough manufacturing going on in the US as companies buy and sell data and make billions of dollars in profits doing it.  Why would a company build a new factor and hire employees when they can get into the data game like this? They simply open a factory overseas and hire a few geeks to handle their “low risk” data mining game.  Now when disasters occur everyone wants to scoop up those who are in need and thus I asked this if the data mining programmers went to work to find the audience that was somewhat captive?  Sure it happens but to what extent?  You can almost be sure those who were victims of Sandy and lived on Staten Island have had their data “mined to death” as all know they are in need and want to be in their face as soon as they can.  A little bit of this goes a long ways when you end up with 10-12 companies that do the same thing bugging you at the same time for business when you have just had your entire life disrupted. 

Excise Taxing the Data Sellers–Nobody’s Supporting the US IT Infrastructure, Especially In Times of Disaster-Companies Have Probably Made Money Off Selling Scraped Data of Those Hurt in the Wake of “Sandy”

How much did Staples pay to have the complex algorithms written for all of their online services and is it “really’ paying off?   The one example shown here also stated that in the zip codes of New York, lower prices were given in Brooklyn and Queens and the others paid higher prices. Now for those checking your credit online, do they use a guy like this that makes millions in profits selling credit information about you that you can’t even see or have access to what they have on you?  This guy operates outside federal laws due to the way he classifies his business too, not fair. 

E-Scoring Credit Algorithms Invisible To Consumers Used to Market and Evaluate, Does Not Fall Under Federal Law And Such Are Used by Insurance Companies - How Will This Work With Exchanges –Attack of the Killer Algorithms Chapter 42

This is pretty much what we have ended up with and why consumer trust is just not there anymore…you get duped right and left way too often.  Here’s a series of videos I call Algo Duping 101 and all are worth watching to get educated and up to speed as to what’s happening with mathematics and computer code today as it’s not just occurring in the markets, as you can read from this article everyone wants that last little tiny edge. 

Big Data, Flawed Data, Business Intelligence, Where’s The Future and What Has Been Our Past…A World With ”Algo Duping” of Society and Consumers

Time to excise tax the data sellers who get their data for nothing (off the taxpayer’s backs) and their profits for free. It’s time they pay their share so we have funds for disasters, for the NIH and the FDA.  Shoot even Macy’s is in a deal with a Chinese online retailer with dumping $15 million in to get a share of the online trickle down algorithm business.  BD 

The Journal identified several companies, including Staples, Discover Financial Services, Rosetta Stone Inc. and Home Depot Inc., that were consistently adjusting prices and displaying different product offers based on a range of characteristics that could be discovered about the user. Office Depot, for example, told the Journal that it uses "customers' browsing history and geolocation" to vary the offers and products it displays to a visitor to its site.

In 2010, the Journal reported that Capital One Financial Corp. was using personalization technology to decide which credit cards to show first-time visitors to its website. Recent Journal follow-up testing indicated that Capital One was showing different users different cards first—either those for "excellent credit" or "average credit."

It is difficult for online shoppers to know why, or even if, they are being offered different deals from other people. Many sites switch prices at lightning speed in response to competitors' offerings and other factors, a practice known as "dynamic pricing." Other sites test different prices but do so without regard to the buyer's characteristics.

Often, sites tailored results by geography. In the tests, Discover, for instance, showed a prominent offer for the company's new "it" card to computers connecting from cities including Denver, Kansas City, Mo., and Dallas, Texas. Computers connecting from Scranton, Penn., Kingsport, Tenn., and Los Angeles didn't see the same offer.

New York City, too, appeared to be a special case. Tests of using ZIP Codes in the boroughs of the Bronx, Manhattan and Staten Island consistently saw higher prices, while Brooklyn and Queens saw almost only the discounted prices.

This despite the fact that all parts of New York City look to be within 20 miles of a Staples competitor, according to the websites.

As a final test, the Journal ordered two separate Swingline staplers from, from two nearby ZIP Codes—one costing $14.29 and the other one $15.79. The staplers arrived the same day. They appear to be indistinguishable from one another and do an equally thorough job of stapling. showed higher prices most often—86% of the time—when the ZIP Code actually had a brick-and-mortar Staples store in it, but was also far from a competitor's store. In calculating these percentages, the Journal excluded New York City and used the more than 29,000 "standard" ZIP Codes in the 50 states and District of Columbia. This meant things like ZIP Codes with only post-office boxes weren't counted.


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