Archive for the ‘Behavioral analytics’ Category

“Authoritarians” for Trump … pssst, that may be you!

January 20, 2016

Interesting piece in Politico

Let’s start with a quick test.

Imagine yourself as a parent.



Would you prefer that your children be:

  1. respectful or independent
  2. obedient or self-reliant
  3. well-behaved or considerate
  4. well-mannered or curious

Pick one from each of the above pairs.

Let’s see how ‘authoritarian’ you are …


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.


Consumers are notoriously bad at spotting real values. Why?


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 …


NetTrax: If you think that you’re being followed around on the net … you’re right.

October 2, 2012

And the company doing it is probably Acxiom … recently profiled in the NY Times.

I had some weird happenings recently.

A friend who works internet marketing for a “plus sized” women’s clothes company used my computer to show me her site’s cool redesign.

For the next couple of weeks I was getting pop-up ads for big women’s clothes … even when I was on common sites like ESPN or WSJ.


Another time, I checked the spelling of a Spanish word via Google.

Next couple of times that I went to You Tube, the lead in ads were in Spanish.

Double huh?

I was wondering how the web “knew” … now I know, courtesy of the NY Times.

Here are some highlights …


IT knows who you are. It knows where you live. It knows what you do.

It peers deeper into American life than the F.B.I. or the I.R.S., or those prying digital eyes at Facebook and Google.

If you are an American adult, the odds are that it knows things like your age, race, sex, weight, height, marital status, education level, politics, buying habits, household health worries, vacation dreams — and on and on.

Right now, more than 23,000 computer servers are collecting, collating and analyzing consumer data for a company …  called the Acxiom Corporation, the quiet giant of a multibillion-dollar industry known as database marketing.

Acxiom has amassed the world’s largest commercial database on consumers —  Its servers process more than 50 trillion data “transactions” a year.

Acxiom maintains its own database on about 190 million individuals and 126 million households in the United States

Its database contains information about 500 million active consumers worldwide, with about 1,500 data points per person.

Acxiom’s Consumer Data Products Catalog offers hundreds of details — called “elements” — that corporate clients can buy about individuals or households, to augment their own marketing databases. Companies can buy data to pinpoint households that are concerned, say, about allergies, diabetes or “senior needs.”

In a fast-changing digital economy, Acxiom is developing the most advanced techniques to mine and refine data.

Digital marketers already customize pitches to users, based on their past activities … think “cookies”.

Acxiom  is pursuing far more comprehensive techniques in an effort to influence consumer decisions.

It is integrating what it knows about our offline, online and even mobile selves, creating in-depth behavior portraits in pixilated detail …  Its  a “360-degree view” on consumers.


How it works

Scott Hughes, an up-and-coming small-business owner and Facebook denizen, is Acxiom’s ideal consumer.

In fact,  Acxiom created him.  Mr. Hughes is a fictional character who appeared in an Acxiom investor presentation in 2010.

A frequent shopper, he was designed to show the power of Acxiom’s multichannel approach.

In the presentation, he logs on to Facebook and sees that his friend Ella has just become a fan of Bryce Computers, an imaginary electronics retailer and Acxiom client.

Ella’s update prompts Mr. Hughes to check out Bryce’s fan page and do some digital window-shopping for a fast inkjet printer.

Such browsing seems innocuous — hardly data mining. But it cues an Acxiom system designed to recognize consumers, remember their actions, classify their behaviors and influence them with tailored marketing.

When Mr. Hughes follows a link to Bryce’s retail site, for example, the system recognizes him from his Facebook activity and shows him a printer to match his interest.

He registers on the site, but doesn’t buy the printer right away, so the system tracks him online.

Lo and behold, the next morning, while he scans baseball news on, an ad for the printer pops up again.

That evening, he returns to the Bryce site where, the presentation says, “he is instantly recognized” as having registered.

It then offers a sweeter deal: a $10 rebate and free shipping.

It’s not a random offer.

Acxiom has its own classification system, PersonicX, which assigns consumers to one of 70 detailed socioeconomic clusters and markets to them accordingly.

In this situation, it pegs Mr. Hughes as a “savvy single” — meaning he’s in a cluster of mobile, upper-middle-class people who do their banking online, attend pro sports events, are sensitive to prices — and respond to free-shipping offers.

Correctly typecast, Mr. Hughes buys the printer.

But the multichannel system of Acxiom and its online partners is just revving up.

Later, it sends him coupons for ink and paper, to be redeemed via his cellphone, and a personalized snail-mail postcard suggesting that he donate his old printer to a nearby school.



There is a fine line between customization and stalking.

While many people welcome the convenience of personalized offers, others may see the surveillance engines behind them as intrusive or even manipulative.

Privacy advocates say they are more troubled by data brokers’ ranking systems, which classify some people as high-value prospects, to be offered marketing deals and discounts regularly, while dismissing others as low-value — known in industry slang as “waste.”

Exclusion from a vacation offer may not matter much …  but if marketing algorithms judge certain people as not worthy of receiving promotions for higher education or health services, they could have a serious impact.

“Over time, that can really turn into a mountain of pathways not offered, not seen and not known about.”

A bit creepy, right?

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Behavioral analytics … bad when Target does it … OK for political campaigns?

September 19, 2012

A couple of months ago Target got some bad press when it was revealed that the company was mining customers’ purchase histories to slot them into behavioral groups susceptible to tailored promotional pitches.

For example, Target identified purchases that mothers-to-be made early in their pregnancies – sometimes before they even knew they were pregnant.  Think bigger jeans, skin care lotions.

Many folks railed that it was an example of big brother invasion of privacy.

Well, guess what?

Political campaigns are using the same methods that Target was using

The modern science of politics is increasingly based on principles from behavioral psychology and data analytics.


Campaigns today mine large data bases with mathematical algorithms that slot folks into categories and provide the basis for how people should be approached (or ignored).

According to the WSJ:

Perhaps the most valuable data in modern campaigns comes from statistical “microtargeting” models—the political world’s version of credit scores.

Campaigns gather thousands of data points on voters, culled from what they put on their registration forms, what they have told canvassers who have visited their homes in the past, and information on their buying and lifestyle habits collected by commercial data warehouses.

The campaigns then run algorithms trawling for patterns linking those demographic characteristics to the political attitudes measured in their polling.

Financial institutions run such statistical models to generate predictions about whether a given individual will demonstrate a certain behavior, like paying a bill on time or defaulting on a loan.

Campaigns do the same, only they are interested in predicting political behavior.

So it’s typical now to generate individual scores, presented as a percentage likelihood, that a voter will cast a ballot, support one party or the other, be pro-choice or antiabortion, or respond to a request to volunteer.

These scores now stick to voters as indelibly as credit scores.

And just as a bank officer won’t sign off on a loan without requesting one, a field director for a campaign won’t send a volunteer to a voter’s door without knowing the relevant number.

BTW: It’s Team Obama that’s doing most of this stuff.

Bad for Target … but OK for Obama.


* * * * *

WSJ source: “The Victory Lab: The Secret Science of Winning Campaigns” by Sasha Issenberg

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