Archive for the ‘Mktg – Research’ Category

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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From the pollsters’ lips to the teleprompter’s ears (and candidate’s lips) … but, what about the forest?

August 8, 2012

My students know that, at heart,  I’m a quant guy and encourage market research over gut feel.


So, I should salivate over the Obama campaigns reliance on market research, data mining, and precision messaging.

Excerpted from WSJ

The Obama campaign has elevated poll-testing and focus-grouping to near-clinical heights.

The results from his vaunted focus groups  drive the president’s every action: his policies, his campaign venues, his targeted demographics, his messaging.

More specifically, spotted an interesting analysis in The Hill:

Recent campaign spending records of the Obama campaign, disclosed that they’ve spent $15 million on polling since the first of the year.

Based on typical polling rate card, $15 million for polls translate to about 6 million minutes of polling time.

Assuming interview lengths of 10 minutes, that’s like 600,000 interviews.

Of course, “polling” doesn’t necessarily mean one-on-one interviewing.

Perhaps as much as a third of the $15 million may have been spent on focus groups and ad testing with dials.

Again, using normal rate cards,  upwards of 4,000 Americans may have been asked to participate in these test sessions.

Yep, from the pollsters lips to the teleprompter’s ears … to the candidate’s lips … to the voters ears.

So, what’s my beef?

First, lack of “authenticity” … a willingness to say anything to anybody if it polls well … even if it’s not true (e.g. the multiple Pinocchios that the Wash Post gave to the Bain outsourcing riff and the incredible “I spend less than any President since Harry Truman)

Second, a willingness to “tailor” the message to different groups or individuals … i.e. to pander shamelessly.

And, the larger point: losing the forest in the trees …  whipsawing based on minutiae and missing the big picture,

After all, it’s the economy, stupid.

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Halloween: Battle of the bite-sized candies …

October 25, 2011

Punch line: Despite a tough economic situation, Americans are likely to spend more this Halloween season.  And, what candy should you buy for the trick-o-treaters? Well, this year consider bite-size M&Ms and Skittles – they scored the highest across all key metrics according to Insight Workbench … 

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Excerpted from, “M&Ms and Skittles Best Bite-Size Halloween Brands

According to the National Retail Federation’s 2011 Halloween Consumer Intentions and Actions Survey, Americans will spend $72.31 on costumes, candy, and decorations, up from last year’s $66.28 and 2009’s $56.31 …

Overall, this Halloween is all about bite-size …

According to Insight Workbench, Candy Corn, the most iconic Halloween candy had the weakest metrics across all categories: lowest share of buzz, a Net Sentiment score of 52 and a Passion Intensity score of 48. Most people eat it solely at Halloween for tradition’s sake …

According to the NPD Group, about 5% of all candy consumed annually is eaten between Halloween and the week after with the most popular choices being chocolate, chewy candies and hard candy.

“It really came down to a battle of the bite-sized candy bits: the good ole reliable, melts-in-your-mouth-not-in-your-hand chocolaty M&Ms vs. the chewy, fruit-impersonating Skittles that let you “taste the rainbow.”

“Halloween was once an inexpensive holiday. Families made treats like candy apples, constructed costumes out of old bed sheets, and made their own spooky decorations. As stores stockpile all of the typical Halloween fare … plan a budget for this trick or treat season,” says Howard Dvorkin, CPA and founder of Consolidated Credit Counseling Services, Inc. …

Edit by KJM.

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Submarine warfare: Quiznos tosses focus groups for “speed dining”

June 21, 2010

Punch Line: To speed time-to-market, Quiznos employs rapid-fire taste tests that help it give customers what they want, sooner.

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Excerpted from Bloomberg Business Week: Damn the Torpedoes! Getting Quiznos, January 14, 2010

Quiznos has always positioned itself as a cut above Subway in the fast-food market — and priced its sandwiches accordingly.

While restaurant operators regularly enlist consumers for feedback, many have turned away from traditional focus groups … to avoid the peril of group think from a methodology that some experts say is “a bit dated”.

Quiznos  swear by a method called “speed-dining”.  The company  empanels as many as 25 groups in back-to-back, 90-minute tastings.

By reworking recipes based on snap reviews, Quiznos can get products from test kitchen to the market in six months … twice as fast as competitors.

Now Quiznos is gunning for upmarket consumers with two new subs priced at up to $7.49.

That may seem foolhardy, with unemployment at 10%. But Quiznos is confident.

After all, speed diners ate them up in October.

Full article:

Distinguishing between customers’ nice-to-haves and gotta-haves …

November 19, 2009

Excerpted from: HBR, What Do Customers Really Want?, by Almquist & Lee, April 2009

Most customer-preference rating tools used in product development today are blunt instruments, primarily because consumers have a hard time articulating their real desires.

Asked to rate a long list of product attributes on a scale of 1 (“completely unimportant”) to 10 (“extremely important”), customers are apt to say they want many or even most of them.

To crack that problem, companies need a way to help customers sharpen the distinction between “nice to have” and “gotta have.”

Some companies are beginning to pierce the fog using a research technique called “Maximum Difference Scaling.” which requires customers to make a sequence of explicit trade-offs.

  • Researchers begin by amassing a list of product or brand attributes—typically from 10 to 40— that represent potential benefits.
  • Then they present respondents with sets of four or so attributes at a time, asking them to select which attribute of each set they prefer most and
  • Subsequent rounds of mixed groupings enable the researchers to identify the standing of each attribute relative to all the others by the number of times customers select it as their most or least important consideration.

A popular restaurant chain recently used MaxDiff to understand why its expansion efforts were misfiring. In a series of focus groups and preference surveys, consumers agreed about what they wanted: more healthful meal options and updated decor.

But, using MaxDiff showed that prompt service of hot meals and a convenient location were far more important to customers than healthful items and modern furnishings, which ended up well down on the list.

The best path forward was to improve kitchen service and select restaurant sites based on where customers worked.

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