In more than one industry, we have been told that usage often goes down in the month before a customer leaves. Upon closer examination, this turns out to be an example of learning something that is not true. For 7 months, the subscriber used about 100 minutes per month. Then, in the eighth month, usage went down to about half that. In the ninth month, there was no usage at all. This subscriber appears to fit the pattern in which a month with decreased usage precedes abandonment of the service. But appearances are deceiving. Looking at minutes of use by day instead of by month would show that the customer continued to use the service at a constant rate until the middle of the month and then stopped completely, presumably because on that day, he or she began using a competing service.
Because of the way that weekends and holidays fell in 2003, October had fewer trading days than August and September. That fact alone accounts for the entire drop-off in sales. In the previous examples, aggregation led to confusion. Failure to aggregate to the appropriate level can also lead to confusion. In one case, data provided by a charitable organization showed an inverse correlation between donors’ likelihood to respond to solicitations and the size of their donations. Those more likely to respond sent smaller checks. This counterintuitive finding is a result of the large number of solicitations the charity sent out to its supporters each year. Imagine two donors, each of whom plans to give $500 to the charity. One responds to an offer in January by sending in the full $500 contribution and tosses the rest of the solicitation letters in the trash. The other sends a $100 check in response to each of five solicitations. On their annual income tax returns, both donors report having given $500, but when seen at the individ ual campaign level, the second donor seems much more responsive. When aggregated to the yearly level, the effect disappears.