I took exception last week to an artcile published on Bloomberg offering another zombie rushing into the "Retail Apocalypse" with this lead:
"At a time when the economy is growing, unemployment is low, wages are rebounding and consumers are eager to buy, Americans are spending less and less on clothing."
Only, it's not true.
I'm not calling Bloomberg "fake news." I'm calling out "biased use of statistics."
The article builds its case on this stat: "In 1977, clothing accounted for 6.2% of household spending. Four decades later, it’s plummeted to half that." However, there are other stats, from the same source, the US Bureau of Labor Statistics, over the same term.
- Total amount spent on clothing INCREASED from $100 billion to $230 billion.
- Total number of households has increased.
- Total wages and household income have increased.
- The price paid per unit of clothing has bifurcated high & low.
- The PERCENTAGE spent on clothes is lower, the DOLLARS spent on clothes are greater.
I'm not an economist, I welcome more expert opinions. I'm not accounting for inflation, income inequality or other factors. Where and how people shop for clothes has changed. What they buy has changed. However, people are not walking around naked, yet.
Lies, Damn Lies, and Statistics:
Gas is cheap.
The job market is strong.
Consumer debt is down.
The nation’s weather is cold.
Retail sales are expected to top $23 trillion this year, including $1.3 trillion in e-commerce led by China and the U.S.” - eMarketer
Retail Apocaplypse to Close American Department Stores - Reuters
“Total retail sales tumbled 11 percent to $50.9 billion during the extended Black Friday weekend. “ - National Retail Federation (NRF).
Online sales on Thanksgiving were up 14.3 percent, while Black Friday online sales were up 9.5 percent.” - IBM
“Despite growth, Black Friday has lost its punch.” - Retention Science
- The NRF data above is based upon a consumer survey, not purchasing data.
- The IBM data is from eCommerce “shopping cart” transactions.
- Cold Weather is good for in-store sales - It brings consumers into stores to buy outerwear and accessories,
- Cold weather is bad for in-store retail sales - It keeps consumers at home, where they shop online and have fewer “impulse” purchases.
To paraphrase Andrew Lang:
"Retail analysts use statistics in the same way that a drunk uses lamp-posts—for support rather than illumination.”
Statistical Disconnects: Super Cats & Deadly Ice Cream
While media, marketers, retailers, wholesalers, politicians, economists and analysts may misinterpret statistics, there is a more significant problem when the data is itself inaccurate.
Many statistical studies are poorly designed.
The collection, or analysis of data may not match the purpose of the study.
IQ testing is one classic example of mismatched analysis. The test does not measure intelligence; its stated purpose. IQ tests do not measure problem solving, creativity or emotional intelligence. And, IQ tests are culturally and racially biased; they use “context” related questions such as, “Where does milk come from?” Is the correct answer “a cow,” “a supermarket,” or "Amazon.com"? IQ tests may be effective at intelligence bench-marking and predictive academic achievement.
“The Pepsi Challenge” data illustrated that customers preferred the taste of Pepsi over that of coke. There were two “fallacies” in the study. First, the study used “sips” from small paper cups; sweeter taste is preferred in small sips, but not in larger consumption. Second, a possibly more “valuable” data point was not shared; consumers who “discovered” through the survey that they preferred the taste of Pepsi, still preferred to buy Coke: “We’re a Coke household.”
“Cherry-Picking” of Data
Statisticians are skilled at choosing data to include in their study, and that which they disregard.
Poor data sampling.
Fact: One summer, 132 cats were brought to Manhattan Animal Medical Center, they had fallen out of open windows. Of the 132 cats, 128 survived, some from 32 story falls.
The New York Times wrote of the miraculous survival instincts of cats; the ability to “adjust rotation orientation” during a fall, “flying-squirrel” aerodynamics, joints and muscles acting as “shock absorbers.” The media discussion lasted for months. ("On Landing Like a Cat - It's a Fact" - New York Times, August 22, 1989)
The data point that ended the Super Cat conversation involved data sampling. One woman interviewed said, “my poor cat must have been the exception. She fell out our 9th story window and died. Of course, I did not bring her body to the hospital, nor did I report it…” The original survey had only included cats that had survived their falls. When cats that died on impact were included the study it was no longer newsworthy.
Presentation of Data may Bias Interpretation
“Milk with 3.5% Fat” - wow, that sounds like a lot vs. “96.5% Fat Free Milk” - better? This is the fat percentage for whole milk. Low fat milk is usually presented as “Reduced Fat, 2% Milk.”
Poor understanding of “probability.”
According to research on "millennials" (collected from a Wall Street Journal reporter’s in-box):
- Millennials will soon be the majority of the U.S. workforce–a huge generation!
- Slightly more than one-third of them are skilled at 3D printing and 22% of them are skilled at driverless cars–new technology!
- 79% of them have bachelor’s degrees–so educated!
- 17% of millennials would “rather become a YouTube star than fall in love”–different priorities!
- And 55% more millennials are signing up for 401(k) plans–good savers!
If any of that information sounds dubious, that’s because it is, and a look at these different claims will show some of the recurring flaws in research and statistical analysis. Read the full WSJ Blog article, "How to Tell If a 'Fact' About Millennials Isn't Actually a Fact."
Another great discussion on "probability" is from Stephen J. Gould, "The Median Isn't the Message." Gould had been diagnosed with cancer (abdominal mesothelioma), and had been informed that he had "a median mortality of eight months." (Note: Gould lived another, prolific, 20 years.)
Note: There is a probability "brain teaser" at the bottom of this post. If you wish to sleep tonight, don't try to solve it.
"Correlation Does Not Imply Causation"
Fact: Children with larger feet score better on spelling tests. Interesting.
Many observers have attempt to find “causality” in this data. Why do children with larger feet spell better?
Did A cause B? Larger feet cause spelling skills to improve. Is there a nutrition cause? Something about shoes? Foot size is likely genetic, is there a genetic component? Nature over nurture?
Did B cause A? Perhaps skill at spelling causes hormonal changes? Could time spent on academic achievement increase hunger, food consumption and therefor foot size?
Another view at the data illustrates that children’s feet grow larger as they grow older. Overall, 8-year-olds have larger feet than 5-year-olds, and 15-year-olds have larger feet than 8-year olds. Older children tend to spell better than than younger counterparts. Foot size and spelling are correlated - but one does not cause the other.
Fact: As ice-cream consumption increases, so does the local incidence of drowning.
Ice-cream does not cause drowning. Nor do drowning incidents cause local residents to eat more ice-cream. As summer temperatures rise people buy more ice-cream; they also spend more time in the pools and the ocean.
Is More Better?
We have more data and statistics available than ever before, and it’s moving beyond our comprehension. Big data, by definition, is so complex that traditional data processing can not capture, filter, or process it.
The real question: What to do with all this data?
We can, effectively argue that warm jackets and mittens sell better in cold weather. These statistics are not only correlated, we can likely show causality. This season’s cold weather yielded high sales levels for warm apparel and accessories. The weather, possibly, brought more customers into stores to purchase these items (weather that is too cold may keep customers home, or induce them to shop online.) While in stores, these customers may have purchased items they would not have purchased otherwise.
As we cannot, yet, control the weather and the manufacturing and purchasing of cold weather apparel is made 9 months ahead of the winter season… how are we to use this year’s weather data to make better purchasing decisions next year?
Regardless of the weather next year, some cold weather apparel will be sold. Study the choices consumers made this year, the competitive environment, visual and technical trends, and related statistics. Build great products and present them in a compelling fashion. Sometimes, you just have to outperform the other guy.
Statistics: to use them is perilous - to not use them is ignorant.
Marketing Strategy for 2018, let's resolve to... ask smarter questions, design better studies, challenge data accuracy, study probability, understand correlation & causation, and weigh the risks and rewards of acting on statistics and analysis.
Lies, damn lies, and statistics - (a quoation attributed to Mark Twain). As talented marketers, In 2015 it will be our job to understand, tame, and effectively use increasingly complex data. Let's think carefully and knowlegeably before we act.
Share your favorite tales and lessons regarding data and statistics. We all learn from your comments.
(c) 2018, David J. Katz - New York City
Post Script & Brain Teaser
The Monty Hall Problem:
Suppose you're on a game show, modeled after "Let's Make a Deal", and you're given the choice of three doors: Behind one door is a car; behind the others, goats.
You pick a door, say No. 1, and Monty Hall, the host, who knows what's behind all the doors, assists you by opening another door, say No. 2, which has a goat.
He then says to you, "You have a choice to stick with door No. 1, or you may now change your pick to door No. 3, it's up to you."
Is it to your advantage to switch your choice?"
Most people, including Ph.D.s, respond that there is no advantage to switching door choice. They say that the original probability was 1 out of 3 chances of choosing the car; and, after one door choice was subsequently eliminated, the chances became 1 out of 2, or 50/50.
Most people... are wrong. Based on probability you should switch doors.
The Monty Hall Problem was published in Parade Magazine in 1990 and rapidly became a heated debate, a serious argument among readers, geeks and mathematicians.
Warning: Pondering this problem can cause grief, loss of sleep, and headaches. If you would like to know why you should switch to door No. 3, watch this...
David J. Katz is chief marketing officer at Randa Accessories, an industry-leading multinational consumer products company, and the world's largest men's accessories business.
His specialty is collaborating with retailers, brands and suppliers to innovate successful outcomes in evolving markets.
David was selected by LinkedIn as a "Top Voice in 2017." He has been named a leading fashion industry "Change Agent" by Women's Wear Daily and a "Menswear Mover" by MR Magazine.
His words and deeds have been featured in The New York Times, The Wall Street Journal, New York Magazine, The Huffington Post, MR Magazine, and WWD. He is a public speaker, and co-author of the best-selling book "Design for Response: Creative Direct Marketing That Works" [Rockport Publishers].
David is a graduate of Tufts University and the Harvard Business School. He is a student of neuroscience, consumer behavior and "stimulus and response." The name Pavlov rings a bell.
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