Data Mining Techniques for Segmentation - Data Mining Techniques in CRM: Inside Customer Segmentation - page 68

Table 3.1 Behavioral fields used in the PCA example.

Field name Description

VOICE_OUT_CALLS Monthly average of outgoing voice calls

VOICE_OUT_MINS Monthly average number of minutes of outgoing

voice calls

SMS_OUT_CALLS Monthly average of outgoing SMS calls

MMS_OUT_CALLS Monthly average of outgoing MMS calls

OUT_CALLS_ROAMING Monthly average of outgoing roaming calls (calls

made in a foreign country)

GPRS_TRAFFIC Monthly average GPRS traffic

PRC_VOICE_OUT_CALLS Percentage of outgoing voice calls: outgoing voice

calls as a percentage of total outgoing calls

PRC_SMS_OUT_CALLS Percentage of SMS calls

PRC_MMS_OUT_CALLS Percentage of MMS calls

PRC_INTERNET_CALLS Percentage of Internet calls

PRC_OUT_CALLS_ROAMING Percentage of outgoing roaming calls: roaming

calls as a percentage of total outgoing calls

Statistical Hypothesis Testing and Significance

Statistical hypothesis testing is applied when we want to make inferences
about the whole population by using sample results. It involves the for-
mulation of a null hypothesis that is tested against an opposite, alternative
hypothesis. The null hypothesis states that an observed effect is simply due
to chance or random variation of the particular dataset examined.
As an example of statistical testing, let us consider the case of the
correlations between the phone usage fields presented in Table 3.2 and
examine whether there is indeed a linear association between the number
and the minutes of voice calls. The null hypothesis to be tested states that
these two fields are not (linearly) associated in the population. This hypothesis
is to be tested against an alternative hypothesis which states that these two
fields are correlated in the population. Thus the statistical test examines the
following statements:

H 0 : the linear correlation in the population is 0 (no linear association); versus
H a : the linear correlation in the population differs from 0.

The sample estimate of the population correlation coefficient is quite
large (0.84) but this may be due to the particular data analyzed (one