Archive for the ‘Predictive Analytics’ Category

Did data analytics miss the forest for the trees?

April 25, 2017

Team Clinton’s GOTV effort got out a lot of votes … for Trump

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Let’s dust-off another post related to the recently released book Shattered: Inside Clinton’s Doomed Campaign.

huffpost-big-data-clinton

 

According to the Huffington Post:

As the post-election day hangover wears off, an examination of the mechanics behind the Clinton’s get out the vote efforts ― reaching out to Clinton voters in key states at the door, on the phone or by text messages ― reveals evidence of what appears to be a pretty shocking truth.

Clinton volunteers were inadvertently turning out Trump voters.

Possibly in significant numbers.

What went wrong? (more…)

Did data analytics miss the forest for the trees?

November 17, 2016

Team Clinton’s GOTV effort got out a lot of votes … for Trump

======

huffpost-big-data-clinton

According to the Huffington Post:

As the post-election day hangover wears off, an examination of the mechanics behind the Clinton’s get out the vote efforts ― reaching out to Clinton voters in key states at the door, on the phone or by text messages ― reveals evidence of what appears to be a pretty shocking truth.

Clinton volunteers were inadvertently turning out Trump voters.

Possibly in significant numbers.

What went wrong? (more…)

Trump : “Data analytics is overrated” … could he be right?

August 2, 2016

Last week, the WSJ ran an opinion piece: Trump’s Big Data Gamble.

The punch line: “While Donald tweets to the masses, Hillary will be precisely targeting persuadable voters.”

Advantage Hillary, right?

Maybe.  Maybe not.

In an AP interview, Trump said that he “always thought that it (meaning data analytics) was overrated” and, accordingly, he’ll spend limited money on data operations to identify and track potential voters and to model various turnout scenarios that could give him the 270 Electoral College votes needed to win the presidency.

He’s moving away from the model Obama used successfully in his 2008 and 2012 wins, and the one that likely Democratic nominee Hillary Clinton is trying to replicate, including hiring many of the staff that worked for Obama in his “Victory Lab”.

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A data-light strategy may sound very old-school in the era of big data … especially coming from Trump …. but it reminded me of an opinion piece that Peggy Noonan wrote in the WSJ soon after Obama’s 2012 election win.

Noonan had a riff about predictive analytics that caught my eye.

It pointed out one of the downsides of predictive analytics … the craft of crunching big data bases to ID people, their behaviors and their hot buttons.

Here’s what Noonan had to say …

(more…)

Flashback: Remember when Target caused a stir by ID’ing moms-to-be?

June 15, 2016

Now, researchers are trolling your web searches to auto-detect diseases.

The Washington Post recently channeled a study done by Microsoft — published in the Journal of Oncology Practice …

The essence: Microsoft’s big data analysts ID’ed folks who were querying questions like “how to treat pancreatic cancer” — hypothesizing that they might have been diagnosed with the disease.

Then, the researchers backtracked thru the prior searches done by those folks and detected a pattern of precedent queries that revolved around symptoms, e.g. abdominal swelling.

Bottom line:  the researchers were able to use the inferred pattern of symptoms to early-predict a disease diagnosis for a statistically significant number people who queried symptoms.

That’s potentially big news in disease diagnosis, though doctors caution that for many diseases, the onset of patient-queried symptoms may be too late-stage for effective treatment.

 

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The Microsoft query- disease analysis reminded me of how Target created some Big Data buzz for analyzing purchase patterns to ID moms-to-be. 

(more…)

Trump : “Data analytics is overrated” … could he be right?

May 19, 2016

In an AP interview, Trump said that he “always thought that it (meaning data analytics) was overrated” and, accordingly, he’ll spend limited money on data operations to identify and track potential voters and to model various turnout scenarios that could give him the 270 Electoral College votes needed to win the presidency.

He’s moving away from the model Obama used successfully in his 2008 and 2012 wins, and the one that likely Democratic nominee Hillary Clinton is trying to replicate, including hiring many of the staff that worked for Obama in his “Victory Lab”.

clip_image002
=====

A data-light strategy may sound very old-school in the era of big data … especially coming from Trump …. but it reminded me of an opinion piece that Peggy Noonan wrote in the WSJ soon after Obama’s 2012 election win.

Noonan had a riff about predictive analytics that caught my eye.

It pointed out one of the downsides of predictive analytics … the craft of crunching big data bases to ID people, their behaviors and their hot buttons.

Here’s what Noonan had to say …

(more…)

Gotcha: This is an unrecognized computer …

November 9, 2015

If you do any banking online, you’ve probably gotten that message at one time or another.

Maybe it was when you got a new computer … or, when you used a friend’s computer to pay a bill.

You probably didn’t think much of it.

You just answered the security questions and paid your bill.

Bet you didn’t stop to wonder: How did Bank of Boise know that this wasn’t my usual computer?

Better yet, ask: How does the bank know when I am on my regular computer?

Well, now that I’ve aroused you curiosity, the answer is ….

Your computer has its own distinctive “device fingerprints” that make it identifiable on the Net as your computer.

image

I worry about stuff like this.  So, I’d thought about this one.

And, my thinking was wrong.

Here’s what’s going on …

(more…)

Gotcha: This is an unrecognized computer …

March 26, 2015

If you do any banking online, you’ve probably gotten that message at one time or another.

Maybe it was when you got a new computer … or, when you used a friend’s computer to pay a bill.

You probably didn’t think much of it.

You just answered the security questions and paid your bill.

Bet you didn’t stop to wonder: How did Bank of Boise know that this wasn’t my usual computer?

Better yet, ask: How does the bank know when I am on my regular computer?

Well, now that I’ve aroused you curiosity, the answer is ….

Your computer has its own distinctive “device fingerprints” that make it identifiable on the Net as your computer.

image

I worry about stuff like this.  So, I’d thought about this one.

And, my thinking was wrong.

Here’s what’s going on …

(more…)

Pssst: What’s your zip code?

February 24, 2015

Ever wonder why the gun-chewing cashier asks you for your zip code?

I naively assumed the store was just doing some kind of geo-survey … trying to figure out where their customers were coming from … how far they were driving to shop their store.

Silly boy.

image

CNN reports that ”Every time you mindlessly give a sales clerk your zip code at checkout, you’re giving data companies and retailers the ability to track everything from your body type to your bad habits.”

Whoa, Nellie.

Here’s what’s happening   …

(more…)

Nums: Why are economists so bad at forecasting?

February 10, 2015

Wash Post had an interesting analysis titled “This graph shows how bad the Fed is at predicting the future

The crux of their argument: the Fed has a clear recent tendency to mis-forecast economic growth … not by a little, by a lot …  forecasting almost twice as rapid growth as is ultimately realized.

For example,  in 2009 the Fed was predicting 4.2 percent growth in 2011.  But then in 2010 it revised that down to 3.85 percent growth. And in 2011 they revised it further to 2.8 percent growth. And when all was said and done, the economy only grew about 2.4 percent that year. The Fed projected growth almost twice as fast as what actually happened.

 

image

What’s going on?

 

(more…)

Disruption: Automating knowledge work …

December 19, 2014

In the old days, folks fretted (or dreamed) about the effect of computerized automation in factories and ATMs replacing bank tellers.

According to a recent McKinsey report:

Physical labor and transactional tasks have been widely automated …

image

Now, advances in data analytics, low-cost computer power, machine learning, and interfaces that “understand” humans are moving the automation frontier rapidly towards “knowledge work”..

Developments in how machines process language and understand context are allowing computers to search for information and find patterns of meaning at superhuman speed.

Here are a couple of examples …

(more…)

Sportswriter say: Advanced analytics can save the Redskins … oh, really

December 3, 2014

We’re working through predictive analytics in class these days.

So, my eyes are open for articles on the subject.

Predictive analytics.

You know, the stuff that Moneyball got rolling in baseball … and Target popularized by identifying pregnant women before the women knew they were expecting.

Let’s set the stage.

The Washington Redskins have been having (another) rough season.

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Veteran sportswriter Tony Kornheiser says advanced analytics could save the Redskins…

(more…)

Gotcha: This is an unrecognized computer …

November 25, 2014

If you do any banking online, you’ve probably gotten that message at one time or another.

Maybe it was when you got a new computer … or, when you used a friend’s computer to pay a bill.

You probably didn’t think much of it.

You just answered the security questions and paid your bill.

Bet you didn’t stop to wonder: How did Bank of Boise know that this wasn’t my usual computer?

Better yet, ask: How does the bank know when I am on my regular computer?

Well, now that I’ve aroused you curiosity, the answer is ….

Your computer has its own distinctive “device fingerprints” that make it identifiable on the Net as your computer.

image

I worry about stuff like this.  So, I’d thought about this one.

And, my thinking was wrong.

Here’s what’s going on …

(more…)

Uh-oh: Most published research findings are false…

November 21, 2014

I didn’t say it, the New Yorker magazine did, setting off a buzz in the halls of academia.

The theme of the New Yorker article –- titled “Truth Wears Off” –was that most (academic) research was flawed and not able to be replicated.  This is, the results were at best true under some special circumstances at a specific point in time, but can’t be replicated. At worst, they’re just plain bull.

Hmmm.

image

 

Challenging the integrity of publication-driven academics?

Turns out that the New Yorker wasn’t the first mag on the beat.

(more…)

Uh-oh: Most published research findings are false…

October 29, 2014

I didn’t say it, the New Yorker magazine did, setting off a buzz in the halls of academia.

The theme of the New Yorker article –- titled “Truth Wears Off” –was that most (academic) research was flawed and not able to be replicated.  This is, the results were at best true under some special circumstances at a specific point in time, but can’t be replicated. At worst, they’re just plain bull.

Hmmm.

image

 

Challenging the integrity of publication-driven academics?

Turns out that the New Yorker wasn’t the first mag on the beat.

(more…)

Nums: Why are economists so bad at forecasting?

October 7, 2014

Wash Post had an interesting analysis titled “This graph shows how bad the Fed is at predicting the future

The crux of their argument: the Fed has a clear recent tendency to mis-forecast economic growth … not by a little, by a lot …  forecasting almost twice as rapid growth as is ultimately realized.

For example,  in 2009 the Fed was predicting 4.2 percent growth in 2011.  But then in 2010 it revised that down to 3.85 percent growth. And in 2011 they revised it further to 2.8 percent growth. And when all was said and done, the economy only grew about 2.4 percent that year. The Fed projected growth almost twice as fast as what actually happened.

 

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What’s going on?

 

(more…)

Nums: Why’s the Fed so bad at forecasting?

April 18, 2014

Wash Post had an interesting analysis titled “This graph shows how bad the Fed is at predicting the future

The crux of their argument: the Fed has a clear recent tendency to mis-forecast economic growth … not by a little, by a lot …  forecasting almost twice as rapid growth as is ultimately realized.

For example,  in 2009 the Fed was predicting 4.2 percent growth in 2011.  But then in 2010 it revised that down to 3.85 percent growth. And in 2011 they revised it further to 2.8 percent growth. And when all was said and done, the economy only grew about 2.4 percent that year. The Fed projected growth almost twice as fast as what actually happened.

 

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What’s going on?

 

(more…)

Gotcha: Soon, speed cams will be so yesterday …

February 4, 2014

Speed cams are bad … AAA has done audits revealing that 1 in 10 tickets issued by them are in error … with drivers having little recourse since only  the cameras are are presumed innocent until proven guilty.

Yep, they’re bad, but …

Imagine all speed limits being tightly enforced … 24 X 7.

Scary thought, right?

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Here’s what will replace the speed cam … and disrupt our lives.

(more…)

Disruption: Automating knowledge work …

December 17, 2013

In the old days, folks fretted (or dreamed) about the effect of computerized automation in factories and ATMs replacing bank tellers.

According to a recent McKinsey report:

Physical labor and transactional tasks have been widely automated …

image

Now, advances in data analytics, low-cost computer power, machine learning, and interfaces that “understand” humans are moving the automation frontier rapidly towards “knowledge work”..

Developments in how machines process language and understand context are allowing computers to search for information and find patterns of meaning at superhuman speed.

Here are a couple of examples …

(more…)

Trax: The Experian connection …

November 5, 2013

Something caught my eye, buried deep in the weeds of the chatter re: the ObamaCare web site fiasco.

Forbes had an early-on article theorizing that a major cause of the web site problems was the Feds insistance that folks shouldn’t see potentially shocking list prices, but rather should input a lot of their private data so that they can be flashed a net price – after government subsidies.

That’s old news … and, you can believe it or not.

Here’s the passage that got me thinking:

The core problem stems from “the slate of registration systems [that] intersect with Oracle Identity Manager, a software component embedded in a government identity-checking system.”

The main Healthcare.gov web page collects information using CGI Group technology.

Then that data is transferred to a system built by Quailty Software Services.

QSS then sends data to Experian, the credit-history firm.

Hmmm

Experian – one the 3 major credit bureaus.

Why get a private sector credit bureau involved?

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At first, I thought the Feds might have stumbled on a borderline brilliant idea …

(more…)

Trax: Netflix tightens up its algorithms …

September 4, 2013

I bought a new “smart” TV … and it came with a 6-month free subscription to Netflix steaming service.

Sweet.

In concept, the streaming concept is a cool idea, except for:

  1. The “loading” message brings a movie to a halt when the “cache” gets full, needs to be emptied, and more content has to stream in.
  2. The limited streaming library … I thought I’d get access to practically every movie ever made … not so, by a long shot.
  3. The goofy recommendations for what I’d like to view next.

#3 surprised me since Netflix have invested heavily in systems to figure out what we want to to see.

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Here’s what’s going on and what Netflix is doing about it …

(more…)

Trax: Using casinos’ loyalty systems to ID gambling addicts …

August 20, 2013

Excerpted from the WSJ …

Casinos have developed detailed behavioral profiles of many of their customers, based in part on information gathered though loyalty-card programs that can track slot-machine play and other-gambling activity.

The casinos use this information to tailor marketing offerings, particularly to the small minority who make up the bulk of their revenue base.

 

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It’s called predictive analytics, and casinos have been on the leading edge.

Here’s the rub …

(more…)

Trax: How Netflix knows what to recommend …

August 15, 2013

Consider this …

In March, Netflix shipped its 4 billionth DVD.

In the first quarter of 2013 alone, it streamed more than 4 billion hours.

The company estimates that 75 percent of viewer activity is driven by the company’s algorithmic recommendation.

 

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Here’s some skinny on how Netflix works the numbers  …

(more…)

Uh-oh: Most published research findings are false…

June 30, 2013

I didn’t say it, the New Yorker magazine did, setting off a buzz in the halls of academia.

The theme of the New Yorker article –- titled “Truth Wears Off” –was that most (academic) research was flawed and not able to be replicated.  This is, the results were at best true under some special circumstances at a specific point in time, but can’t be replicated. At worst, they’re just plain bull.

Hmmm.

image

 

Challenging the integrity of publication-driven academics?

Turns out that the New Yorker wasn’t the first mag on the beat.

(more…)

Uh-oh: Flawed research … “retraction notices” surge

June 30, 2013
Punch line: An increasing number of published research studies – scientific & academic – are being “retracted” because the outcomes being reported can’t be replicated or are just plain fraudulent.

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Here are the details ..

(more…)

More: Why Fed economic forecasts are bad ….

June 28, 2013

Earlier this week we posted Nums: Why’s the Fed so bad at forecasting?

We cited Nate Silver’s thesis that economists’ forecasts are generally poor for 4 main reasons:

  1. Complexity makes it hard to to pin down cause & effect.
  2. The economy is dynamic, especially subject to policy jolts
  3. Economic data is imprecise and subject to large revisions
  4. Forecasts often reflect political bias … pro and con.

On cue, the Feds released released their revision to Q1 GDP …

Based on revised data, the economy grew at a 1.8% annual rate in the first quarter,  well below previous estimate of 2.4% growth.

The biggest change was a cut in the government’s estimate of consumer spending which is more than 70% of the economy.

Consumer spending growth dropped to 2.6% from 3.4% growth.

 

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Source: USA Today

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The revision — .6% – may initially sound like loose change, but it’s a 25% miss.

So, economic models that operating on the original (higher) estimate have a starting point that is off by 25%.

The error compounds over time.

It’s a version of what theorists call chaos theory … how a seemingly small variation at a starting point can compound into a major effect over time.

= = = =

Side note: And, in the “new normal” economy, the downward revision was good for the stock market since it puts pressure on the Fed to continue pumping money into the economy … the bulk of which is flowing straight to the stock market.

Go figure.

* * * * *
Follow on Twitter @KenHoma                   >> Latest Posts

Nums: Why’s the Fed so bad at forecasting?

June 24, 2013

Wash Post had an interesting analysis this week titled “This graph shows how bad the Fed is at predicting the future

The crux of their argument: the Fed has a clear recent tendency to mis-forecast economic growth … not by a little, by a lot …  forecasting almost twice as rapid growth as is ultimately realized.

For example,  in 2009 the Fed was predicting 4.2 percent growth in 2011.  But then in 2010 it revised that down to 3.85 percent growth. And in 2011 they revised it further to 2.8 percent growth. And when all was said and done, the economy only grew about 2.4 percent that year. The Fed projected growth almost twice as fast as what actually happened.

 

image

What’s going on?

 

(more…)

Nums: How to win at Monopoly …

June 22, 2013

Everybody knows that Blackjack is a game of probabilities and that card-counting can get you kicked out of casinos – because it helps slightly with the odds.

Did you know that math and statistics can also improve your odds in Monopoly?

Business Insider posted a fun (and thorough) pitch re: how to win in Monopoly … a great practical (?) application of math and statistics.

 

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Here are a couple of takeaways and a link to the entire pitch … worth browsing, even if you’re not a Monopoly aficionado.

(more…)

Un-Gotcha: 10 ways to protect your online privacy …

June 21, 2013

Useful compilation from Forbes … some no-brainers, some new (to me).

Ranges from clearing browser cookies & history frequently to masking IP addresses.

Worth browsing.

click to view

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Follow on Twitter @KenHoma              >> Latest Posts

Gotcha: This is an unrecognized computer.

June 19, 2013

If you do any banking online, you’ve probably gotten that message at one time or another.

Maybe it was when you got a new computer … or, when you used a friend’s computer to pay a bill.

You probably didn’t think much of it.

You just answered the security questions and paid your bill.

Bet you didn’t stop to wonder: How did Bank of Boise know that this wasn’t my usual computer.

Well, now that I’ve aroused you curiosity, the answer is ….

You’re computer has its own distinctive “device fingerprints” that make it identifiable on the Net as your computer.

image

I worry about stuff like this.  So, I’d thought about this one.

And, my thinking was wrong.

Here’s what’s going on …

(more…)

Nums: How likely is it that a criminal will do it again?

June 18, 2013

In a prior post Feds: “Hire ex-cons … we do” … say, what? … we reported that Feds are hiring ex-cons into the State Department … and pressuring private companies to hire them, too.

Sounds risky to me, but what if there was some objective way to cut the risk … to determine the likelihood that an ex-con would (or would not) go straight.

 

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There is a way.

Some courts and parole departments are using predictive analytics to help decide who belongs in prison.

Here’s the scoop …

(more…)

NetTrax: You can run, but you can’t hide …. MORE

June 14, 2013

Yesterday we posted about a company developing algorithms that comb through key factors including content of posts, and location, among others

…..  to provide a very to develop a identify and “unify social profiles” for users who may be using different names or handles on each of their social networks.

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The post elicited strong interest and 2 replies that I want to highlight.

The first is from Niv Singer, Chief Technology Officer at Tracx … the man, and the company referenced.

(more…)

Flashback: Remember when Target caused a stir by aiming at moms-to-be?

June 11, 2013

The Feds’ phone & internet surveillance programs that were revealed last week have raised the public’s consciousness re: Big Data.

Remember when Target created some Big Data buzz for analyzing purchase patterns to ID moms-to-be?

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In a previous post, we excepted from a NY Times article How Companies Learn Your Secrets that

  1. Much of what people do is based on habits, not conscious reasoning.
  2. Consumers’ shopping habits and brand loyalties are often more habitual than thoughtful.
  3. But, there are certain “events” — e.g. new baby, new home, recent divorce — that seem to make consumers more open to switching stores and brands.
  4. Savvy marketers are learning to identify these critical events — before they happen — and try to get consumers to switch  their behavior.

Target is one of the retailers identifying customers who are “vulnerable to intervention by marketers” … and pouncing on them.

Who?  Moms-to-be.

How are they doing it?

(more…)

Gotcha: Soon, speed cams will be so yesterday …

June 4, 2013

Speed cams are bad but …

Imagine all speed limits being tightly enforced … 24 X 7.

Scary thought, right?

image

Here’s what will replace the speed cam … and disrupt our lives.

(more…)

Disruption: Automating knowledge work …

May 24, 2013

In the old days, folks fretted (or dreamed) about the effect of computerized automation in factories and ATMs replacing bank tellers.

According to a recent McKinsey report:

Physical labor and transactional tasks have been widely automated …

image

Now, advances in data analytics, low-cost computer power, machine learning, and interfaces that “understand” humans are moving the automation frontier rapidly towards “knowledge work”..

Developments in how machines process language and understand context are allowing computers to search for information and find patterns of meaning at superhuman speed.

Here are a couple of examples …

(more…)

NetTrax: You’re leaving cookie crumbs on the net … lots of them

May 8, 2013

What happens when you click to a web site?

Short answer: you have new cookies installed on your computer or have old cookies modified  … whether you know it or not … and  you then spew crumbs all over the Internet … letting companies track you, profile you, and hard sell you stuff.

Here’s a visual of what a couple of clicks can do … each dot represents a  site or company that can grab your information … just because you innocently clicked.

Later we’ll explain the graphic and what’s going on.

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First some background on web tracking …

(more…)

Pssst: What’s your zip code?

May 6, 2013

Ever wonder why the gun-chewing cashier asks you for your zip code?

I naively assumed the store was just doing some kind of geo-survey … trying to figure out where their customers were coming from … how far they were driving to shop their store.

Silly boy.

image

CNN reports that ”Every time you mindlessly give a sales clerk your zip code at checkout, you’re giving data companies and retailers the ability to track everything from your body type to your bad habits.”

Whoa, Nellie.

Here’s what’s happening   …

(more…)

Nums: Is predictive analytics winning battles, not wars?

May 3, 2013

Peggy Noonan has a piece in the WSJ today that I almost skipped.

You know, another  “Is Obama a Lame Duck?” piece.

Buried in the column was a riff about predictive analytics that caught my eye.

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It pointed out one of the downsides of  predictive analytics … the craft  of crunching big data bases to ID people, their behaviors and their hot buttons.

Here’s what Noonan had to say …

(more…)

Gotcha: You probably paid too much … especially if you’re bad at math.

April 18, 2013

Awhile ago, we reported a study that consumers almost invariably pick 33% more stuff than a 33% price discount.

Ouch.

Consumers are notoriously bad at spotting real values. Why?

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According to the Atlantic ….

  • First: Consumers don’t know what the heck anything should cost, so we rely on parts of our brains that aren’t strictly quantitative.
  • Second: Although humans spend in numbered dollars, we make decisions based on clues and half-thinking that amount to innumeracy.

More specifically, here are some more ways consumers end up paying too much …

(more…)

Pssst: Facebook is stalking you in stores ….

April 16, 2013

Why?

Ostensibly to see if its sponsors’ ads are working.

But, some skeptics (e.g. me) think that there may be other motives, too.

Here’s the scoop.

Last year, Facebook entered into a partnership with a company called Datalogix.

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Everybody knows what Facebook does.

Datalogix, not so much.

Datalogix is a firm that records the purchasing patterns of more than 100 million American households.

When you stop by the supermarket … you probably hand the cashier a loyalty card to get a discount on your items.

That card ties your identity to your purchases.

Your sales data is sent over to a server maintained by Datalogix, which has agreements with hundreds of major retailers to procure such data.

Source: Slate

Hmmm.

Facebook and Datalogix … why the hook-up?

(more…)

Nums: What percentage of Facebook users click on the ads?

April 15, 2013

According to an AP-CNBC poll

User clicks are a critical part of an advertiser’s calculus when gauging the effectiveness of those ads and how much they’re willing to pay for them.

So, how does Facebook do?

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Here are some survey results …

(more…)

Nums: Ask why … not just how many.

April 10, 2013

Some highlights from an HBR article:  The Hidden Biases in Big Data 

These days the business and management science worlds are focused on how large datasets can decode consumers’ behavior patterns … enabling marketers to laser-target high potential prospects with finely-honed messages, offers, and “attention”.

“Big data” … becomes problematic when it adheres to “data fundamentalism” … the notion that correlation always indicates causation, and that massive data sets and predictive analytics always reflect objective truth … that  “with enough data, the numbers speak for themselves.”

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Big data has hidden biases in both collection methods and analysis …

(more…)

Nums: A world of battling algorithms

February 26, 2013

I’ve been getting back into behavioral economics and predictive analytics.

Led me back to a cool 15 minute TED Talk.

Tech entrepreneur Kevin Slavin tells how algorithms have reached across industries and into every day life.

A couple of lines caught my attention:

  • There are more than 2,000 physicists working on Wall Street developing operational algorithms
  • Massive scale speed trading is dependent on millisecond read & respond rates …
  • So, firms are physically literally locating right next to internet routing hubs to cut transmission times
  • And, of course, there isn’t time for human intervention and control
  • “We may be building whole worlds we don’t really understand, and can’t control.”

Worth listening to this pitch … a very engaging geek who may be onto something big.

* * * * *
Follow on Twitter @KenHoma               >> Latest Posts

Are Mac users easy pickings?

July 19, 2012

Punch line: Online retailers are using sophisticated analytics and web tracking methods to tailor their offerings… and to get folks to pay higher prices.

To get  the lowest prices: (1) Use a PC (not  Mac or iPad), (20 don’t sign on from a ritzy location, (3) pass thru a price-shopping site on your way to the destination site, (4) ask to see items sorted by price — from low to high, (5) check out at least one cheap item — maybe even put in your cart — then delete it later

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Excerpted from WSJ

Retailers are becoming bigger users of so-called predictive analytics, crunching reams of data to guess the future shopping habits of customers.

The goal is to tailor offerings to people believed to have the highest “lifetime value” to the retailer.

Online, seemingly innocuous information is available to predict shoppers’ tastes and spending habits.

For example, The average household income for adult owners of Mac computers is $98,560, compared with $74,452 for a PC owner.

Drilling down, Orbitz  has found that people who use Apple spend as much as 30% more a night on hotels, so the online travel agency is starting to show them different, and sometimes costlier, travel options than Windows visitors see.

More specifically …

  • Mac users on average spend $20 to $30 more a night on hotels than their PC counterparts, a significant margin given the site’s average nightly hotel booking is around $100
  • Mac users are 40% more likely to book a four- or five-star hotel than PC users,
  • When Mac and PC users book the same hotel, Mac users tend to stay in more expensive rooms.

Other factors that have influence over results include

  • A user’s location (e.g. geo-targeting high wealth zip codes)
  • A shoppers history on the site (e.g. purchases at list price or at discounts).
  • The referring site (e.g. Kayak delivers price-sensitive shoppers to travel sites)takes those properties into account.

Caveat emptor !

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