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SELECT * FROM DatabaseServer1.AdventureWorksDW.dbo.DimProductCategory WHERE ProductCategoryKey NOT IN (SELECT ProductCategoryKey FROM DatabaseServer2.AdventureWorksDW.dbo.DimCategoryForecast)

The median customer lifetime provides information about exactly one customer, the one in the middle Averages are more useful, because they can be included in financial calculations So if a customer generates $200 in revenue per year, and the average customer stays for two years, then the average customer generates $400 in revenue The average truncated tenure is the average tenure for a given period of time after customers start, answering a question such as: What is the average number of days that customers are expected to survive in the first year after they start Limiting the span to one year is helpful for both business reasons and technical reasons On the business side, it means that the results can be validated after one year On the technical side, average truncated tenures are easier to calculate, because they are for a finite time period.

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Calculating the average truncated tenure from the survival curve turns out to be quite easy To illustrate the process, start with the simplest case, the average one-day tenure That is, what is the average tenure of customers in the one day after they start The number of customers who survived to day one is the number who started times day-one survival The average divides by the number who started, so the average is just the survival on day one If 99% of customers survive for one day, then the average customer survives for 099 days in the first day after they start What is the average two-day tenure This is the average number of days that customers are active in the two days after they start The total number of days that customers survive is the sum of those who were around on days one and two.

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So, the total number of days is day-one survival times the number of customers who started plus day-two survival times the number of customers who started The average divides out the number of customers The average two-day tenure is survival on day one plus survival on day two This generalizes to any tenure The average tenure for any given time after a customer starts is the sum of the survival values up to that tenure Another way of looking at the calculation leads to the observation that the area under the survival curve is the average truncated tenure Figure 6-20 shows how to calculate the area, by placing rectangles around each survival value The area of each rectangle is the base times the height The base is one time unit The height is the survival value Voila.

Earlier chapters contain examples of models, without describing them as such The RFM methodology introduced in 8 assigns an RFM bin to each customer; the estimated response rate of the RFM bin is a model score that estimates response The expected remaining lifetime from a survival model is a model score Even the expected value from the chi-square test is an example of a model score, produced by a basic statistics formula What these have in common is that they all find patterns in data that can be applied back to the original data or to new data, producing a meaningful result The first type of model in this chapter is the look-alike model, which takes an example typically of something particularly good or bad and finds other rows that are similar to the example Look-alike models use a definition of similarity.

The area under the curve is the sum of the survival values, which as we just saw, is the average truncated tenure..

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