Промышленный лизинг Промышленный лизинг  Методички 

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Trivial Rules

Trivial results are already known by anyone at all familiar with the business. The second example ( Customers who purchase maintenance agreements are very likely to purchase large appliances ) is an example of a trivial rule. In fact, customers typically purchase maintenance agreements and large appliances at the same time. Why else would they purchase maintenance agreements? The two are advertised together, and rarely sold separately (although when sold separately, it is the large appliance that is sold without the agreement rather than the agreement sold without the appliance). This rule, though, was found after analyzing hundreds of thousands of point-of-sale transactions from Sears. Although it is valid and well supported in the data, it is still useless. Similar results abound: People who buy 2-by-4s also purchase nails; customers who purchase paint buy paint brushes; oil and oil filters are purchased together, as are hamburgers and hamburger buns, and charcoal and lighter fluid.

A subtler problem falls into the same category. A seemingly interesting result-such as the fact that people who buy the three-way calling option on their local telephone service almost always buy call waiting-may be the result of past marketing programs and product bundles. In the case of telephone service options, three-way calling is typically bundled with call waiting, so it is difficult to order it separately. In this case, the analysis does not produce actionable results; it is producing already acted-upon results. Although it is a danger for any data mining technique, market basket analysis is particularly susceptible to reproducing the success of previous marketing campaigns because of its dependence on unsummarized point-of-sale data-exactly the same data that defines the success of the campaign. Results from market basket analysis may simply be measuring the success of previous marketing campaigns.

Trivial rules do have one use, although it is not directly a data mining use. When a rule should appear 100 percent of the time, the few cases where it does not hold provide a lot of information about data quality. That is, the exceptions to trivial rules point to areas where business operations, data collection, and processing may need to be further refined.

Inexplicable Rules

Inexplicable results seem to have no explanation and do not suggest a course of action. The third pattern ( When a new hardware store opens, one of the most commonly sold items is toilet bowl cleaner ) is intriguing, tempting us with a new fact but providing information that does not give insight into consumer behavior or the merchandise or suggest further actions. In this case, a large hardware company discovered the pattern for new store openings, but could not figure out how to profit from it. Many items are on sale during the store openings, but the toilet bowl cleaners stood out. More investigation might give some



explanation: Is the discount on toilet bowl cleaners much larger than for other products? Are they consistently placed in a high-traffic area for store openings but hidden at other times? Is the result an anomaly from a handful of stores? Are they difficult to find at other times? Whatever the cause, it is doubtful that further analysis of just the market basket data can give a credible explanation.

WARNINGwhen applying market basket analysis, many of the results are often either trivial or inexplicable. Trivial rules reproduce common knowledge about the business, wasting the effort used to apply sophisticated analysis techniques. Inexplicable rules are flukes in the data and are not actionable.

FAMOUS RULES: BEER AND DIAPERS

Perhaps the most talked about association rule ever found is the association between beer and diapers. This is a famous story from the late 1980s or early 1990s, when computers were just getting powerful enough to analyze large volumes of data. The setting is somewhere in the midwest, where a retailer is analyzing point of sale data to find interesting patterns.

Lo and behold, lurking in all the transaction data, is the fact that beer and diapers are selling together. This immediately sets marketing minds in motion to figure out what is happening. A flash of insight provides the explanation: beer drinkers do not want to interrupt their enjoyment of televised sports, so they buy diapers to reduce trips to the bathroom. No, thats not it. The more likely story is that families with young children are preparing for the weekend, diapers for the kids and beer for Dad. Dad probably knows that after he has a couple of beers, Mom will change the diapers.

This is a powerful story. Setting aside the analytics, what can a retailer do with this information? There are two competing views. One says to put the beer and diapers close together, so when one is purchased, customers remember to buy the other one. The other says to put them as far apart as possible, so the customer must walk by as many stocked shelves as possible, having the opportunity to buy yet more items. The store could also put higher-margin diapers a bit closer to the beer, although mixing baby products and alcohol would probably be unseemly.

The story is so powerful that the authors noticed at least four companies using the story-IBM, Tandem (now part of HP), Oracle, and NCR Teradata. The actual story was debunked on April 6, 1998 in an article in Forbes magazine called Beer-Diaper Syndrome.

The debunked story still has a lesson. Apparently, the sales of beer and diapers were known to be correlated (at least in some stores) based on inventory. while doing a demonstration project, a sales manager suggested that the demo show something interesting, like beer and diapers being sold together. with this small hint, analysts were able to find evidence in the data. Actually, the moral of the story is not about the power of association rules. It is that hypothesis testing can be very persuasive and actionable.



How Good Is an Association Rule?

Association rules start with transactions containing one or more products or service offerings and some rudimentary information about the transaction. For the purpose of analysis, the products and service offerings are called items. Table 9.1 illustrates five transactions in a grocery store that carries five products.

These transactions have been simplified to include only the items purchased. How to use information like the date and time and whether the customer paid with cash or a credit card is discussed later in this chapter.

Each of these transactions gives us information about which products are purchased with which other products. This is shown in a co-occurrence table that tells the number of times that any pair of products was purchased together (see Table 9.2). For instance, the box where the Soda row intersects the OJ column has a value of 2, meaning that two transactions contain both soda and orange juice. This is easily verified against the original transaction data, where customers 1 and 4 purchased both these items. The values along the diagonal (for instance, the value in the OJ column and the OJ row) represent the number of transactions containing that item.

Table 9.1 Grocery Point-of-Sale Transactions

CUSTOMER

ITEMS

Orange juice, soda

Milk, orange juice, window cleaner

Orange juice, detergent

Orange juice, detergent, soda

Window cleaner, soda

Table 9.2 Co-Occurrence of Products

WINDOW CLEANER

MILK SODA DETERGENT

1 1 2

Window Cleaner

1 1 0

Milk

1 0 0

Soda

0 3 3

Detergent

0 1 2



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