DSS News
                     D. J. Power, Editor
             November 10, 2002 -- Vol. 3, No. 23
         A Bi-Weekly Publication of DSSResources.COM

    Check the article by F. Kelly "Implementing an EIS"


 * DSS Wisdom
 * Ask Dan! - What is the "true story" about data mining, beer 
    and diapers?
 * What's New at DSSResources.COM
 * DSS News Releases


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DSS Wisdom

Bonczek, Holsapple, and Whinston (1981) concluded "With the 
continued and rapid decline in computing costs, there is the potential 
of using computers to enhance the decision-making capabilities of 
individuals. A theory of the entire process of decision making should be 
the basis for introducing computer technology into decision processes in 
order to enhance decision-making capabilities. It is from such a theory 
of decision making that we can build generalized decision support 
systems (p. 380)."

from Bonczek, R. H., C. W. Holsapple, and A. B. Whinston, 
Foundations of Decision Support Systems, New York, NY: Academic Press, 


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Ask Dan!
by Daniel J. Power

What is the "true story" about using data mining to identify a relation 
between sales of beer and diapers?

This is one of those recurring questions related to a famous 
decision support example. The story of using data mining to find a 
relation between "beer and diapers" is told, retold and added to 
like any other legend or "tall tale". I can't recall exactly when 
I first heard a version of the tale, but I have used the story and added 
to it myself on occasion. The following are some versions of the tale 

An article in The Financial Times of London (Feb. 7, 1996) stated, "The 
oft-quoted example of what data mining can achieve is the 
case of a large US supermarket chain which discovered a strong 
association for many customers between a brand of babies nappies 
(diapers) and a brand of beer. Most customers who bought the nappies 
also bought the beer. The best hypothesisers in the world would 
find it difficult to propose this combination but data mining 
showed it existed, and the retail outlet was able to exploit it by 
moving the products closer together on the shelves." 

Bill Palace at UCLA (Spring 1996) in his web lecture notes writes "For 
example, one Midwest grocery chain used the data mining 
capacity of Oracle software to analyze local buying patterns. They 
discovered that when men bought diapers on Thursdays and Saturdays, they 
also tended to buy beer. Further analysis showed that these shoppers 
typically did their weekly grocery shopping on Saturdays. 
On Thursdays, however, they only bought a few items. The retailer 
concluded that they purchased the beer to have it available for the 
upcoming weekend. The grocery chain could use this newly discovered 
information in various ways to increase revenue. For example, they could 
move the beer display closer to the diaper display. And, they could make 
sure beer and diapers were sold at full price on Thursdays."  

Hermiz and Manganaris (1999) stated "One of the most repeated 
(though likely fabricated) data mining stories is the discovery 
that beer and diapers frequently appear together in a shopping basket. 
The explanation goes that when fathers are sent out on an errand to buy 
diapers, they often purchase a six-pack of their favorite beer as a 

Also, the 8th Annual Virginia High School Programming Contest (2001) had 
a problem titled Beer and Diapers. The problem statement begins "Store 
owners have long noticed that inspecting customer transactions can 
increase their profit. For example, placing the items frequently 
purchased together next to each other can stimulate purchasing of these 
items. Obviously, milk and cereal are frequently purchased together. 
However, some patterns are less obvious. For example, it was found that 
people who buy diapers also buy beer. Given a number of transactions, 
your job is to find a pair of items that frequently occur together."

You'll find other versions on the web and in data mining books. As 
a result of student questions and my own curiosity, I decided to try to 
find out the "truth" about this story. In July 2002, I received 
a media advisory about a live webcast on the past, present and future of 
data mining sponsored by Teradata, a division of NCR. The webcast was 
celebrating the 10th anniversary of a beer and diapers study and the 
data mining legend it started. I couldn't participate in the "live 
event" on July 31, 2002, but I did watch the archived webcast and the 
moderator, Holly Michael of Teradata, emailed me a transcript in 
September 2002.

Thomas Blischok, CEO of MindMeld, Inc., was one of the four panelists. 
Blischok managed the original study that started the beer and diapers 
legend. Holly Michael began the webcast by summarizing the legend.  In 
her version "A number cruncher was examining retail check-out data. He 
discovered a strange correlation, a higher than expected pairing of beer 
and diapers in afternoon transactions, and presumably the data indicated 
that young fathers were likely to pick up something for themselves as 
they picked up baby supplies on their way home from work. The story goes 
on to say that the retailer then rearranged the displays to boost sales 
of both products."

Holly then turned the webcast over to Thom Blischok who explained his 
early 1990s data mining project for Osco Drug. Thom noted that Osco Drug 
is one of the pioneering companies in data mining. He said "as we worked 
with the senior management team of their organization, we helped them 
create a totally new merchandising strategy. A merchandizing strategy 
which was focused on buying what was sold in the stores versus the 
traditional methodology at that time of selling what was bought by the 

According to Blischok, "Their senior management team had a vision, and 
their vision was centered around a strategy to reinvent the store 
centered on consumer demand. This is where the legend began. We took 
over 1.2 million market baskets. A market basket is the stuff you put in 
the physical cart and check out at the register. And these represented 
transactions from about 25 stores. Our strategy on the NCR side was to 
discover what people bought in a given shopping experience."

And what about the legend? Blischok said "Yes, if we go back to the 
legend, we did discover that between 5:00 and 7:00 p.m. that consumers 
bought beer and diapers. This was an insight that the retailer had never 
seen before, and the fact that we discovered this affinity was not the 
real transformational event that occurred. What this showed Osco in this 
early pioneering effort was that it was possible to redesign the store 
based on consumer preferences at the center of all decisions. Their 
management team got it. They simply understood that they had the 
opportunity to change. Well, in reality they never did anything with 
beer and diapers relationships. But what they did do was to 
conservatively begin the reinvention of their merchandising processes."

Mike Grote, Director of the Teradata Data Mining lab in San Diego, 
followed up on Blischok's presentation with an update on data mining in 
2002. Mike noted "So if we think back about that beer and diapers story 
that we are leveraging here today for purposes of the press conference, 
there are certainly some limitations associated with that as a data 
mining example, especially when contrasted with where the state of the 
art of data mining is today. I think in the context of that example, the 
tools that were state of the art, query generation tools, allowed Thom 
and his team to examine very, very large numbers of transactions and see 
where some particular purchases occurred together. So what does that 
show, and how would we contrast that with how we might approach the 
problem today? Well, probably what we would do with the problem today is 
we would use some additional tools that would not only enable us to 
identify where events were happening together, but they would in fact 
allow us to make determinations whether that one event led to a 
significantly increased likelihood that another event is going to occur, 
or whether one purchase significantly increases the chance that another 
purchase is going to happen." 

Does everyone agree with the above account? YES and NO! John Earle in a 
note at posted 12/21/1998 wrote "I worked for Teradata and 
the man attributed with starting the myth. We had done a data discovery 
for Osco Drugs...looking for affinities between what items were 
purchased on a single ticket. Then we suggested tests for moving 
merchandise in the store to see how it affected affinities. ...Our 
'fearless'leader, Thom Blischok, when talking with prospects and the 
press, didn't distinguish between the actual affinities tested and our 
hypotheses. Our job was to sell the value of systems. Sometimes in 
selling, fact blurred with folklore."

Tom Fawcett of HP Labs posted a note on the origin of the "diapers and 
beer" example at on Wednesday, June 14, 2000. Fawcett 
provides a third hand explanation of the origin of this example from 
Lounette Dyer via Ronny Kohavi. His posting claims Thom Blischok 
"dreamed up the 'diapers and beer' example. To the best of my knowledge 
it was never supported in any data that they analyzed." 

Ronny Kohavi in an email at dated July 6, 2000 wrote 
"For my invited talk at ICML in 1998, I tracked the beer and diapers 
example further. Check out slide 21 in Basically, I found the 
person in Blischok's group who ran the queries. K. Heath ran self joins 
in SQL (1990), trying to find two itemsets that have baby items, which 
are particularly profitable. She found this beer and diapers pattern in 
their data of 50 stores over a day period. When I talked to her, she 
mentioned that she didn't think the pattern was significant, but it was 

So what are the facts? In 1992, Thomas Blischok, manager of a retail 
consulting group at Teradata, and his staff prepared an analysis of 1.2 
million market baskets from about 25 Osco Drug stores. Database queries 
were developed to identify affinities. The analysis "did discover that 
between 5:00 and 7:00 p.m. that consumers bought beer and diapers". Osco 
managers did NOT exploit the beer and diapers relationship by moving the 
products closer together on the shelves. This decision support study was 
conducted using query tools to find an association. The true story is 
very bland compared to the legend.

So if someone asks you about the story of "data mining, beer and 
diapers" you now know the facts. The story most people tell is fiction 
and legend. You can continue telling the story, but remember no matter 
how you tell it, the story of "data mining, beer and diapers" is NOT a 
good example of the possiblities for decision support with current data 
mining technologies.


Brand, E. and R. Gerritsen, Association and Sequencing, February 1998, 

Cohen, N., Data Mining: Nagging that it really adds up, 2000, URL

Fawcett, Tom, Origin of "diapers and beer", posted at, 
Wednesday, June 14, 2000, URL

Fu, X., J. Budzik, K. J. Hammond, Mining Navigation History for 
Recommendation, Infolab, Northwestern University, in Proceedings
of Intelligent User Interfaces 2000, ACM Press, 2000, URL

Hermiz, K. and S. Manganaris, Beyond Beer and Diapers, DB2 Magazine, 
Winter 1999, URL

Kohavi, R., Origin of "diapers and beer", email dated July 6, 2000,

Michael, H., Transcript of the Beer and Diapers webcast, email, 
September 3, 2002.

Palace, Bill, Data Mining, a technology note prepared for Management 
274A, Anderson Graduate School of Management at UCLA, Spring 1996, URL

Riggs Eckelberry's OF INTEREST, More On Diapers and Beer, Monday, 
December 21, 1998, URL

Teradata Webcast, Beyond Beer and Diapers - The Origins and Future of 
Data Mining, archived 7/31/2002 at

   Check the new DSS book edited by M. Mora, G. Forgionne 
   and J. Gupta at

What's New at DSSResources.COM

11/07/2002 Posted article by Kelly, F., "Implementing an Executive 
Information System (EIS)".


  Get information about Dan Power's book, Decision Support 
  Systems: Concepts and Resources for Managers, at .


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