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Figure 9.5 Showing marketing interventions and product sales on the same chart makes
it possible to see effects of marketing efforts.


Such analysis does not require looking at individual market baskets”daily
or weekly summaries of product sales are sufficient. However, it does require
knowing when marketing interventions take place”and sometimes getting
such a calendar is the biggest challenge. One of the questions that such a chart
can answer is the effect of the intervention. A challenge in answering this ques­
tion is determining whether the additional sales are incremental or are made
by customers who would purchase the product anyway at some later time.
Market basket data can start to answer this question. In addition to looking
at the volume of sales after an intervention, we can also look at the number of
baskets containing the item. If the number of customers is not increasing, there
is evidence that existing customers are simply stocking up on the item at a
lower cost.
A related question is whether discounting results in additional sales of other
products. Association rules can help answer this question by finding combina­
tions of products that include those being promoted during the period of the
promotion. Similarly, we might want to know if the average size of orders
increases or decreases after an intervention. These are examples of questions
where more detailed transaction level data is important.


Clustering Products by Usage
Perhaps one of the most interesting questions is what groups of products often
appear together. Such groups of products are very useful for making recom­
mendations to customers”customers who have purchased some of the prod­
ucts may be interested in the rest of them (Chapter 8 talks about product
Market Basket Analysis and Association Rules 295


recommendations in more detail). At the individual product level, association
rules provide some answers in this area. In particular, this data mining tech­
nique determines which product or products in a purchase suggest the pur­
chase of other particular products at the same time.
Sometimes it is desirable to find larger clusters than those provided by asso­
ciation rules, which include just a handful of items in any rule. Standard cluster­
ing techniques, which are described in Chapter 11, can also be used on market
basket data. In this case, the data needs to be pivoted, as shown in Figure 9.6, so
that each row represents one order or customer, and there is a flag or a counter
for each product purchased. Unfortunately, there are often thousands of differ­
ent products. To reduce the number of columns, such a transformation can take
place at the category level, rather than at the individual product level.
There is typically a lot of information available about products. In addition
to the product hierarchy, such information includes the color of clothes,
whether food is low calorie, whether a poster includes a frame, and so on.
Such descriptions provide a wealth of information, and can lead to useful ad
hoc questions:
Do diet products tend to sell together?
––

Are customers purchasing similar colors of clothing at the same time?
––

Do customers who purchase framed posters also buy other products?
––


Being able to answer such questions is often more useful than trying to clus­
ter products, since such directed questions often lead directly to marketing
actions.


LINE ITEM

LINE ITEM ID ITEM
LINE
ORDER ID
PRODUCT IID LINE ITEM ORDER PIVOT
LINE TEM ID
QUANTITY ID
ORDER
UNIT PRODUCT ITEM ID
PRICELINE ID ORDER ID
UNIT QUANTITY ID
COST ORDER HAS PRODUCT A
GIFT WRAP FLAG
UNIT PRODUCT ID
PRICE HAS PRODUCT B
TAXABLE FLAG
UNIT QUANTITY
COST HAS PRODUCT C
etc. GIFT WRAP FLAG
UNIT PRICE HAS PRODUCT D
TAXABLE FLAG
UNIT COST etc.
etc. GIFT WRAP FLAG
TAXABLE FLAG
etc.
C




D
A




B
T



CT




T




T
UC




UC




UC
U
OD




OD




OD




OD
PR




PR




PR




PR




ORDER ID LINE ITEM ID B
..
ORDER ID 0 1 1 0
ORDER ID LINE ITEM ID C

Figure 9.6 Pivoting market basket data makes it possible to run clustering algorithms to
find interesting groups of products.
296 Chapter 9


Association Rules

One appeal of association rules is the clarity and utility of the results, which
are in the form of rules about groups of products. There is an intuitive appeal
to an association rule because it expresses how tangible products and services
group together. A rule like, “if a customer purchases three-way calling, then that
customer will also purchase call waiting,” is clear. Even better, it might suggest a
specific course of action, such as bundling three-way calling with call waiting
into a single service package.
While association rules are easy to understand, they are not always useful.
The following three rules are examples of real rules generated from real data:
Wal-Mart customers who purchase Barbie dolls have a 60 percent likeli­
––

hood of also purchasing one of three types of candy bars.
Customers who purchase maintenance agreements are very likely to
––

purchase large appliances.
When a new hardware store opens, one of the most commonly sold
––

items is toilet bowl cleaners.
The last two examples are examples that we have actually seen in data. The
first is an example quoted in Forbes on September 8, 1997. These three exam­
ples illustrate the three common types of rules produced by association rules:
the actionable, the trivial, and the inexplicable. In addition to these types of rules,
the sidebar “Famous Rules” talks about one other category.


Actionable Rules
The useful rule contains high-quality, actionable information. Once the pattern is
found, it is often not hard to justify, and telling a story can lead to insights and
action. Barbie dolls preferring chocolate bars to other forms of food is not a likely

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